system

Abstract

The system according to this embodiment aims to integrate information from various data sources and predict crime patterns and dangerous areas with high accuracy. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a management unit. The collection unit collects data from security cameras, social media, police reports, etc. The analysis unit analyzes the data collected by the collection unit and predicts crime patterns and dangerous areas. The provision unit provides the analysis results obtained by the analysis unit through a smartphone application for citizens. The management unit provides the analysis results through a dashboard for city administrators.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information from various data sources has not been sufficiently integrated to predict crime patterns and dangerous areas, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate information from various data sources and accurately predict crime patterns and dangerous areas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a management unit. The data collection unit collects data from sources such as security cameras, social media, and police reports. The analysis unit analyzes the data collected by the data collection unit to predict crime patterns and dangerous areas. The data provision unit provides the analysis results obtained by the analysis unit through a smartphone application for citizens. The management unit provides the analysis results through a dashboard for city administrators. [Effects of the Invention]

[0007] The system according to this embodiment can integrate information from various data sources and predict crime patterns and dangerous areas with high accuracy. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The crime prevention platform according to an embodiment of the present invention is a system that effectively prevents and suppresses crime in urban areas by utilizing multimodal generating AI. This system collects information from diverse data sources such as security cameras, social media, and police reports. The multimodal generating AI analyzes the collected data to predict crime patterns and dangerous areas. The analysis results are provided in real time through a smartphone app for citizens to support individual safety actions. Simultaneously, the analysis results are provided through a dashboard for urban administrators, enabling efficient resource allocation and the development of strategic crime prevention measures. This effectively prevents and suppresses crime in urban areas, improving citizen safety. Furthermore, urban administrators can implement efficient resource allocation and strategic crime prevention measures. Examples of data include security camera footage, social media posts, and police reports. This information is input into the multimodal generating AI. Next, the multimodal generating AI analyzes the collected data to predict crime patterns and dangerous areas. For example, it can analyze the frequency, time of day, and type of crime in specific areas. This allows for the identification of areas and times where crime is more likely to occur. The analysis results are provided in real time through a smartphone app for citizens. For example, when a user enters their current location, nearby high-risk areas and crime occurrence information are displayed. This allows users to obtain information that supports their own safety actions. At the same time, the analysis results are provided through a dashboard for city administrators. Through the dashboard, city administrators can check crime patterns and information on high-risk areas, and plan efficient resource allocation and strategic crime prevention measures. This includes, for example, optimizing police patrol routes and reviewing the placement of security cameras. This platform enables effective crime prevention and suppression in urban areas, improving the safety of citizens. Furthermore, city administrators can implement efficient resource allocation and strategic crime prevention measures. For example, by focusing police presence on areas with high crime rates, the crime rate can be reduced. In this way, the crime prevention platform can effectively prevent and suppress crime in urban areas and improve the safety of citizens.

[0029] The crime prevention platform according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a management unit. The collection unit collects data such as security camera footage, social media posts, and police reports. For example, the collection unit collects video data from security cameras. The collection unit can also collect social media posts. The collection unit can also collect police reports. For example, the collection unit collects high-resolution video data from security cameras and uses it for analysis. The collection unit collects social media posts in real time to detect signs of crime. The collection unit periodically collects police reports and analyzes crime trends. The analysis unit analyzes the data collected by the collection unit and predicts crime patterns and high-risk areas. For example, the analysis unit analyzes the frequency of crimes in a specific area. The analysis unit can also analyze the time of day when crimes occur. The analysis unit can also analyze the type of crime. For example, the analysis unit analyzes the frequency of crimes in a specific area over time to understand crime trends. The analysis unit analyzes the time of day when crimes occur to identify times when crimes are more likely to occur. The analysis unit analyzes crime types and understands crime characteristics in each region. The provision unit provides the analysis results obtained by the analysis unit through a smartphone app for citizens. For example, when a user enters their current location, the provision unit displays information on nearby dangerous areas and crime occurrences. The provision unit can also provide users with real-time crime information. The provision unit can also provide users with information to support safe behavior. For example, when a user enters their current location, the provision unit displays information on nearby dangerous areas and crime occurrences on a map. The provision unit notifies users of crime information in real time. The provision unit provides users with advice to support safe behavior. The management unit provides analysis results through a dashboard for city administrators. For example, the management unit reviews information on crime patterns and dangerous areas and plans efficient resource allocation and strategic crime prevention measures. The management unit can also optimize police patrol routes. The management unit can also review the placement of security cameras. For example, the management unit optimizes police patrol routes based on information on crime patterns and dangerous areas. The management department will review the placement of security cameras and establish an effective surveillance system. The management department will evaluate the effectiveness of crime prevention measures and revise them as necessary.As a result, the crime prevention platform according to this embodiment can effectively prevent and suppress crime in urban areas and improve the safety of citizens.

[0030] The data collection department collects data from sources such as security cameras, social media, and police reports. Specifically, it collects high-resolution video data from security cameras and uses it for analysis. Security cameras are installed in urban areas, public facilities, and commercial facilities, recording footage 24 hours a day. This video data is transmitted in real time to a central database and immediately used for analysis. Social media posts are also collected, and signs of crime are detected by monitoring specific keywords and hashtags. For example, posts containing keywords such as "suspicious person" or "theft" are automatically collected and sent to the analysis department. Police reports are also collected regularly and are important data for analyzing crime trends. Reports contain details of incidents, including location, time of occurrence, and extent of damage. By analyzing this information, crime patterns and high-risk areas can be identified. The data collection department centrally manages information from these diverse data sources and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to predict crime patterns and high-risk areas. Specifically, it analyzes the frequency of crimes in specific areas over time to understand crime trends. For example, based on data from the past few years, it analyzes the frequency of crimes in specific areas on a monthly and weekly basis to identify increases and decreases in crime depending on the season and day of the week. Furthermore, it analyzes the time of day when crimes occur to identify times when crimes are more likely to occur. For example, it identifies areas where crimes are frequent at night or in the early morning and conducts intensive patrols during those times. It also analyzes the types of crimes to understand the characteristics of crime in each area. For example, it identifies characteristics such as a high incidence of car break-ins in one area and a high incidence of theft in another. The analysis unit uses AI to analyze this data and simulate multiple scenarios to identify the most likely risks. The AI ​​uses image recognition technology to analyze security camera footage and detect suspicious movements and individuals. It also analyzes social media posts using natural language processing technology to detect early signs of crime. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk in specific areas and time periods based on past crime data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The service provider will deliver the analysis results obtained by the analysis provider through a smartphone app for citizens. Specifically, when a user enters their current location, the app will display nearby dangerous areas and crime occurrence information on a map. For example, if a user enters their home or work address, the app will display the types of crimes that have recently occurred in the vicinity, the time of occurrence, and the level of danger, color-coded. The app also has a function to notify users of crime information in real time; for example, if a suspicious person is sighted in a particular area or if an emergency occurs, the user will be notified via push notification or voice alert. Furthermore, the service provider will provide users with advice to support safe behavior. For example, it will provide advice on precautions when going out at night, suggestions for safe routes, and emergency contact information. In this way, the service provider can quickly and accurately provide users with information to ensure their own safety and minimize the risk of crime. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can update information on dangerous areas or revise the advice based on user feedback. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, minimizing the risk of crime.

[0033] The management department provides analysis results through a dashboard for city administrators. Specifically, it reviews crime patterns and information on high-risk areas to plan efficient resource allocation and strategic crime prevention measures. For example, if a particular area experiences a high crime rate, police patrols will be concentrated in that area. Patrol times and routes can also be optimized according to the time of day and type of crime. Furthermore, the management department reviews the placement of security cameras to build an effective surveillance system. For example, the placement and orientation of security cameras can be adjusted to match areas and times of high crime rates. The management department also evaluates the effectiveness of crime prevention measures and reviews them as needed. For example, it regularly evaluates whether specific measures are effective and considers new measures if they are ineffective. Based on this information, the management department can comprehensively manage crime prevention measures across the entire city and implement efficient and effective measures. In addition, the management department can share information and collaborate with other cities and regions to promote wide-area crime prevention measures. For example, it can collaborate with neighboring cities and regions to conduct wide-area patrols and share information to suppress crime. This will allow the management department to improve the overall safety of the city and provide an environment where citizens can live with peace of mind.

[0034] The data collection unit collects data such as security camera video data, social media posts, and police reports. For example, the data collection unit can collect security camera video data. The data collection unit can also collect social media posts. The data collection unit can also collect police reports. For example, the data collection unit collects security camera video data in high resolution and uses it for analysis. The data collection unit collects social media posts in real time to detect signs of crime. The data collection unit periodically collects police reports to analyze crime trends. This improves the accuracy of the analysis by collecting information from diverse data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input security camera video data into AI and have the AI ​​perform the analysis of the video data.

[0035] The analysis unit analyzes the frequency, timing, and type of crimes occurring in a specific area. For example, the analysis unit can analyze the frequency of crimes in a specific area. The analysis unit can also analyze the timing of crimes. The analysis unit can also analyze the types of crimes. For example, the analysis unit can analyze the frequency of crimes in a specific area over time to understand crime trends. The analysis unit can analyze the timing of crimes to identify times when crimes are more likely to occur. The analysis unit can analyze the types of crimes to understand the characteristics of crimes in each area. This makes it possible to identify areas and times when crimes are more likely to occur. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI predict crime patterns and dangerous areas.

[0036] The information provider displays information about dangerous areas and crime occurrences in the vicinity when the user enters their current location. For example, when the user enters their current location, the information provider displays information about dangerous areas and crime occurrences in the vicinity on a map. The information provider can also notify the user of crime information in real time. The information provider can also provide the user with advice to support safe behavior. For example, when the user enters their current location, the information provider displays information about dangerous areas and crime occurrences in the vicinity on a map. The information provider notifies the user of crime information in real time. The information provider provides the user with advice to support safe behavior. This allows the user to obtain information that supports their own safe behavior. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the analysis results into AI and have the AI ​​provide the user with the most appropriate information.

[0037] The management department reviews information on crime patterns and high-risk areas, and plans efficient resource allocation and strategic crime prevention measures. For example, the management department optimizes police patrol routes based on information on crime patterns and high-risk areas. The management department reviews the placement of security cameras and establishes an effective surveillance system. The management department evaluates the effectiveness of crime prevention measures and revises them as needed. This allows urban administrators to implement efficient resource allocation and strategic crime prevention measures. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input analysis results into AI and have the AI ​​plan optimal resource allocation and crime prevention measures.

[0038] The management department optimizes police patrol routes and reviews the placement of security cameras. For example, the management department optimizes police patrol routes. The management department can also review the placement of security cameras. The management department can evaluate the effectiveness of crime prevention measures and revise them as needed. For example, the management department optimizes police patrol routes and focuses patrols on areas with high crime rates. The management department reviews the placement of security cameras and establishes an effective surveillance system. The management department evaluates the effectiveness of crime prevention measures and revises them as needed. This makes it possible to optimize police patrol routes and review the placement of security cameras. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input analysis results into AI and have the AI ​​execute the optimal patrol routes and security camera placements.

[0039] The data collection unit analyzes past crime data and selects the optimal data collection method. For example, the data collection unit may concentrate data collection during specific time periods based on past crime data. The data collection unit can also analyze past crime data and enhance data collection in specific areas. The data collection unit can also refer to past crime data and select a data collection method corresponding to specific crime patterns. For example, the data collection unit may concentrate data collection during specific time periods based on past crime data. The data collection unit may analyze past crime data and enhance data collection in specific areas. The data collection unit may refer to past crime data and select a data collection method corresponding to specific crime patterns. This improves the efficiency of data collection by selecting the optimal data collection method based on past crime data. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past crime data into AI and have the AI ​​execute the optimal data collection method.

[0040] The data collection unit adjusts the collection scope when collecting data, taking into account social factors such as specific events or festivals. For example, the data collection unit strengthens data collection in the surrounding areas when a large-scale event is held. The data collection unit can also concentrate data collection in specific areas during festivals and celebrations. The data collection unit can also increase the frequency of data collection during times when social gatherings are high. For example, the data collection unit strengthens data collection in the surrounding areas when a large-scale event is held. The data collection unit concentrates data collection in specific areas during festivals and celebrations. The data collection unit increases the frequency of data collection during times when social gatherings are high. By adjusting the data collection scope to take social factors into account, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on social factors into AI and have the AI ​​perform the adjustment of the collection scope.

[0041] The data collection unit prioritizes collecting highly relevant data, taking geographical location information into consideration. For example, the data collection unit prioritizes collecting crime data around the user's current location. The data collection unit can also pre-collect data for areas the user plans to travel to. The data collection unit can also prioritize collecting data for places the user frequently visits. For example, the data collection unit prioritizes collecting crime data around the user's current location. The data collection unit pre-collects data for areas the user plans to travel to. The data collection unit prioritizes collecting data for places the user frequently visits. This improves the accuracy of data collection by prioritizing the collection of highly relevant data, taking geographical location information into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI and have the AI ​​collect highly relevant data.

[0042] The data collection unit analyzes social media activity and collects relevant data during data collection. For example, the data collection unit collects crime-related posts on social media in real time. The data collection unit can also monitor specific hashtags on social media and collect relevant data. The data collection unit can also analyze users' social media activity and collect relevant regional data. For example, the data collection unit collects crime-related posts on social media in real time. The data collection unit monitors specific hashtags on social media and collects relevant data. The data collection unit analyzes users' social media activity and collects relevant regional data. This enables more effective data collection by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into AI and have the AI ​​perform the collection of relevant data.

[0043] The analysis unit adjusts the level of detail of the analysis based on the severity of the crime during the analysis. For example, the analysis unit performs a detailed analysis for serious crimes. The analysis unit can also perform a simplified analysis for minor crimes. The analysis unit can also perform an analysis with an appropriate level of detail depending on the type of crime. For example, the analysis unit performs a detailed analysis for serious crimes. The analysis unit performs a simplified analysis for minor crimes. The analysis unit performs an appropriate level of detail depending on the type of crime. This allows for more effective analysis by adjusting the level of detail of the analysis based on the severity of the crime. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input crime data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit applies different analysis algorithms depending on the crime category during analysis. For example, the analysis unit applies a specific algorithm to violent crimes. The analysis unit may also apply a different algorithm to property crimes. The analysis unit may also apply a dedicated algorithm to cybercrimes. For example, the analysis unit applies a specific algorithm to violent crimes. The analysis unit applies a different algorithm to property crimes. The analysis unit applies a dedicated algorithm to cybercrimes. By applying different analysis algorithms depending on the crime category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input crime category data into a generative AI and have the generative AI apply an appropriate analysis algorithm.

[0045] The analysis unit determines the priority of the analysis based on the timing of the crimes. For example, the analysis unit prioritizes the analysis of recently occurring crimes. The analysis unit can also analyze crimes that are likely to occur at specific times based on past crime data. The analysis unit can also prioritize the analysis of crimes related to seasons or events. For example, the analysis unit prioritizes the analysis of recently occurring crimes. The analysis unit analyzes crimes that are likely to occur at specific times based on past crime data. The analysis unit prioritizes the analysis of crimes related to seasons or events. This allows for more effective analysis by determining the priority of the analysis based on the timing of the crimes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input crime timing data into a generative AI and have the generative AI determine the priority of the analysis.

[0046] The analysis unit adjusts the order of analysis based on the relevance of the crimes during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant crimes. The analysis unit can also postpone the analysis of less relevant crimes. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the crimes. For example, the analysis unit prioritizes the analysis of highly relevant crimes. The analysis unit postpones the analysis of less relevant crimes. The analysis unit dynamically adjusts the order of analysis according to the relevance of the crimes. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the crimes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input crime relevance data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0047] The information delivery unit selects the optimal delivery method by referring to the user's past behavior history when providing information. For example, the information delivery unit may prioritize information delivery methods that the user has used in the past. The information delivery unit can also propose the optimal information delivery method based on the user's past behavior history. The information delivery unit can also provide customized information delivery based on the user's behavior patterns. For example, the information delivery unit may prioritize information delivery methods that the user has used in the past. The information delivery unit may propose the optimal information delivery method based on the user's past behavior history. The information delivery unit provides customized information delivery based on the user's behavior patterns. This makes it possible to provide more effective information delivery by selecting the optimal delivery method by referring to the user's past behavior history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit may input user behavior history data into AI and have the AI ​​select the optimal information delivery method.

[0048] The information provider customizes the information provided based on the user's current location. For example, the provider prioritizes providing crime information around the user's current location. The provider can also provide information in advance about areas the user plans to visit. The provider can also suggest optimal safety actions based on the user's current location. For example, the provider prioritizes providing crime information around the user's current location. The provider provides information in advance about areas the user plans to visit. The provider suggests optimal safety actions based on the user's current location. This allows for more appropriate information to be provided by customizing the information provided based on the user's current location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's location data into AI and have the AI ​​customize the information provided.

[0049] The information delivery unit selects the optimal delivery method by considering the user's device information when providing information. For example, if the user is using a smartphone, the information delivery unit will provide information tailored to the screen size. If the user is using a tablet, the information delivery unit can also provide information optimized for a larger screen. If the user is using a smartwatch, the information delivery unit can also provide concise and highly visible information. For example, if the user is using a smartphone, the information delivery unit will provide information tailored to the screen size. If the user is using a tablet, the information delivery unit will provide information optimized for a larger screen. If the user is using a smartwatch, the information delivery unit will provide concise and highly visible information. By selecting the optimal delivery method by considering the user's device information, more effective information delivery becomes possible. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0050] The information provider analyzes the user's social media activity and provides relevant information when providing information. For example, the provider analyzes the user's social media posts and provides relevant crime information. The provider can also provide relevant information based on the activity of accounts the user follows. The provider can also refer to the user's social media activity and provide the most appropriate information. For example, the provider analyzes the user's social media posts and provides relevant crime information. The provider provides relevant information based on the activity of accounts the user follows. The provider refers to the user's social media activity and provides the most appropriate information. By analyzing the user's social media activity and providing relevant information, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media data into AI and have AI perform the provision of relevant information.

[0051] The management department selects the optimal management method by referring to past management data during management. For example, the management department may concentrate management on specific time periods based on past management data. The management department can also analyze past management data and strengthen management in specific areas. The management department can also refer to past management data and select a management method that corresponds to a specific crime pattern. For example, the management department may concentrate management on specific time periods based on past management data. The management department may analyze past management data and strengthen management in specific areas. The management department may refer to past management data and select a management method that corresponds to a specific crime pattern. This improves the accuracy of management by selecting the optimal management method by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department may input past management data into AI and have the AI ​​select the optimal management method.

[0052] The management department customizes management methods based on specific regions and time periods during management. For example, if the crime rate is high in a particular region, the management department will focus its management efforts on that region. If crimes are frequent during a particular time period, the management department can also concentrate its management efforts during that time period. The management department can also adopt different management methods depending on the region and time period. For example, if the crime rate is high in a particular region, the management department will focus its management efforts on that region. If crimes are frequent during a particular time period, the management department will concentrate its management efforts during that time period. The management department adopts different management methods depending on the region and time period. This allows for more effective management by customizing management methods based on specific regions and time periods. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input regional and time period data into AI and have the AI ​​perform the customization of management methods.

[0053] The management department selects the optimal management method when managing a user, taking geographical location information into consideration. For example, the management department selects the optimal management method based on crime information around the user's current location. The management department can also adjust the management method based on information about areas the user plans to travel to. The management department can also select the management method based on information about places the user frequently visits. For example, the management department selects the optimal management method based on crime information around the user's current location. The management department adjusts the management method based on information about areas the user plans to travel to. The management department selects the management method based on information about places the user frequently visits. By selecting the optimal management method while considering geographical location information, the accuracy of management is improved. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input geographical location information into AI and have the AI ​​select the optimal management method.

[0054] The management department improves the accuracy of its management by referring to relevant literature and data during management. For example, the management department improves management methods by referring to the latest literature on crime prevention. The management department can also improve the accuracy of management by analyzing past crime data. The management department can also introduce optimal management methods by referring to management data from other cities. For example, the management department improves management methods by referring to the latest literature on crime prevention. The management department improves the accuracy of management by analyzing past crime data. The management department introduces optimal management methods by referring to management data from other cities. This makes more effective management possible by improving the accuracy of management by referring to relevant literature and data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant literature and data into AI and have the AI ​​perform the improvement of management accuracy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can also analyze past crime data and select the optimal data collection method. For example, it can concentrate data collection during specific time periods based on past crime data. It can also analyze past crime data and strengthen data collection in specific areas. It can refer to past crime data and select a data collection method that corresponds to a specific crime pattern. This improves the efficiency of data collection by selecting the optimal data collection method based on past crime data.

[0057] The information delivery department can also select the most suitable delivery method by referring to the user's past behavior history when providing information. For example, it can prioritize selecting information delivery methods that the user has used in the past. It can also suggest the most suitable information delivery method based on the user's past behavior history. It can also provide customized information based on the user's behavior patterns. By selecting the most suitable delivery method by referring to the user's past behavior history, more effective information delivery becomes possible.

[0058] The data collection unit can also adjust the collection scope to take into account social factors such as specific events or festivals. For example, data collection can be intensified in the surrounding areas when a large-scale event is held. Data collection can also be concentrated in specific areas during festivals or celebrations. The frequency of data collection can also be increased during times when social gatherings are high. By adjusting the data collection scope to take social factors into account, more effective data collection becomes possible.

[0059] The analysis unit can also adjust the level of detail of the analysis based on the severity of the crime. For example, a detailed analysis can be performed for serious crimes, while a simplified analysis can be performed for minor crimes. The analysis can also be performed at an appropriate level of detail depending on the type of crime. By adjusting the level of detail of the analysis based on the severity of the crime, a more effective analysis becomes possible.

[0060] The management department can also select the optimal management method by considering geographical location information during management. For example, they can select the optimal management method based on crime information around the user's current location. They can also adjust the management method based on information about areas the user plans to visit. They can also select the management method based on information about places the user frequently visits. By selecting the optimal management method while considering geographical location information, the accuracy of management is improved.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data from sources such as security cameras, social media, and police reports. For example, it collects high-resolution video data from security cameras and uses it for analysis. It collects social media posts in real time to detect signs of crime. It regularly collects police reports to analyze crime trends. Step 2: The analysis unit analyzes the data collected by the collection unit to predict crime patterns and high-risk areas. For example, it analyzes the frequency of crimes in a specific area over time to understand crime trends. It analyzes the time of day when crimes occur to identify times when crimes are more likely to occur. It analyzes the types of crimes to understand the characteristics of crime in each area. Step 3: The service provider delivers the analysis results obtained by the analysis unit through a smartphone app for citizens. For example, when a user enters their current location, the app displays nearby dangerous areas and crime occurrence information on a map. It also notifies users of crime information in real time and provides advice to support safe behavior. Step 4: The management department provides the analysis results through a dashboard for city administrators. For example, they optimize police patrol routes based on crime patterns and information on high-risk areas. They review the placement of security cameras and build an effective surveillance system. They evaluate the effectiveness of crime prevention measures and revise them as needed.

[0063] (Example of form 2) The crime prevention platform according to an embodiment of the present invention is a system that effectively prevents and suppresses crime in urban areas by utilizing multimodal generating AI. This system collects information from diverse data sources such as security cameras, social media, and police reports. The multimodal generating AI analyzes the collected data to predict crime patterns and dangerous areas. The analysis results are provided in real time through a smartphone app for citizens to support individual safety actions. Simultaneously, the analysis results are provided through a dashboard for urban administrators, enabling efficient resource allocation and the development of strategic crime prevention measures. This effectively prevents and suppresses crime in urban areas, improving citizen safety. Furthermore, urban administrators can implement efficient resource allocation and strategic crime prevention measures. Examples of data include security camera footage, social media posts, and police reports. This information is input into the multimodal generating AI. Next, the multimodal generating AI analyzes the collected data to predict crime patterns and dangerous areas. For example, it can analyze the frequency, time of day, and type of crime in specific areas. This allows for the identification of areas and times where crime is more likely to occur. The analysis results are provided in real time through a smartphone app for citizens. For example, when a user enters their current location, nearby high-risk areas and crime occurrence information are displayed. This allows users to obtain information that supports their own safety actions. At the same time, the analysis results are provided through a dashboard for city administrators. Through the dashboard, city administrators can check crime patterns and information on high-risk areas, and plan efficient resource allocation and strategic crime prevention measures. This includes, for example, optimizing police patrol routes and reviewing the placement of security cameras. This platform enables effective crime prevention and suppression in urban areas, improving the safety of citizens. Furthermore, city administrators can implement efficient resource allocation and strategic crime prevention measures. For example, by focusing police presence on areas with high crime rates, the crime rate can be reduced. In this way, the crime prevention platform can effectively prevent and suppress crime in urban areas and improve the safety of citizens.

[0064] The crime prevention platform according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a management unit. The collection unit collects data such as security camera footage, social media posts, and police reports. For example, the collection unit collects video data from security cameras. The collection unit can also collect social media posts. The collection unit can also collect police reports. For example, the collection unit collects high-resolution video data from security cameras and uses it for analysis. The collection unit collects social media posts in real time to detect signs of crime. The collection unit periodically collects police reports and analyzes crime trends. The analysis unit analyzes the data collected by the collection unit and predicts crime patterns and high-risk areas. For example, the analysis unit analyzes the frequency of crimes in a specific area. The analysis unit can also analyze the time of day when crimes occur. The analysis unit can also analyze the type of crime. For example, the analysis unit analyzes the frequency of crimes in a specific area over time to understand crime trends. The analysis unit analyzes the time of day when crimes occur to identify times when crimes are more likely to occur. The analysis unit analyzes crime types and understands crime characteristics in each region. The provision unit provides the analysis results obtained by the analysis unit through a smartphone app for citizens. For example, when a user enters their current location, the provision unit displays information on nearby dangerous areas and crime occurrences. The provision unit can also provide users with real-time crime information. The provision unit can also provide users with information to support safe behavior. For example, when a user enters their current location, the provision unit displays information on nearby dangerous areas and crime occurrences on a map. The provision unit notifies users of crime information in real time. The provision unit provides users with advice to support safe behavior. The management unit provides analysis results through a dashboard for city administrators. For example, the management unit reviews information on crime patterns and dangerous areas and plans efficient resource allocation and strategic crime prevention measures. The management unit can also optimize police patrol routes. The management unit can also review the placement of security cameras. For example, the management unit optimizes police patrol routes based on information on crime patterns and dangerous areas. The management department will review the placement of security cameras and establish an effective surveillance system. The management department will evaluate the effectiveness of crime prevention measures and revise them as necessary.As a result, the crime prevention platform according to this embodiment can effectively prevent and suppress crime in urban areas and improve the safety of citizens.

[0065] The data collection department collects data from sources such as security cameras, social media, and police reports. Specifically, it collects high-resolution video data from security cameras and uses it for analysis. Security cameras are installed in urban areas, public facilities, and commercial facilities, recording footage 24 hours a day. This video data is transmitted in real time to a central database and immediately used for analysis. Social media posts are also collected, and signs of crime are detected by monitoring specific keywords and hashtags. For example, posts containing keywords such as "suspicious person" or "theft" are automatically collected and sent to the analysis department. Police reports are also collected regularly and are important data for analyzing crime trends. Reports contain details of incidents, including location, time of occurrence, and extent of damage. By analyzing this information, crime patterns and high-risk areas can be identified. The data collection department centrally manages information from these diverse data sources and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes data collected by the data collection unit to predict crime patterns and high-risk areas. Specifically, it analyzes the frequency of crimes in specific areas over time to understand crime trends. For example, based on data from the past few years, it analyzes the frequency of crimes in specific areas on a monthly and weekly basis to identify increases and decreases in crime depending on the season and day of the week. Furthermore, it analyzes the time of day when crimes occur to identify times when crimes are more likely to occur. For example, it identifies areas where crimes are frequent at night or in the early morning and conducts intensive patrols during those times. It also analyzes the types of crimes to understand the characteristics of crime in each area. For example, it identifies characteristics such as a high incidence of car break-ins in one area and a high incidence of theft in another. The analysis unit uses AI to analyze this data and simulate multiple scenarios to identify the most likely risks. The AI ​​uses image recognition technology to analyze security camera footage and detect suspicious movements and individuals. It also analyzes social media posts using natural language processing technology to detect early signs of crime. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk in specific areas and time periods based on past crime data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The service provider will deliver the analysis results obtained by the analysis provider through a smartphone app for citizens. Specifically, when a user enters their current location, the app will display nearby dangerous areas and crime occurrence information on a map. For example, if a user enters their home or work address, the app will display the types of crimes that have recently occurred in the vicinity, the time of occurrence, and the level of danger, color-coded. The app also has a function to notify users of crime information in real time; for example, if a suspicious person is sighted in a particular area or if an emergency occurs, the user will be notified via push notification or voice alert. Furthermore, the service provider will provide users with advice to support safe behavior. For example, it will provide advice on precautions when going out at night, suggestions for safe routes, and emergency contact information. In this way, the service provider can quickly and accurately provide users with information to ensure their own safety and minimize the risk of crime. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can update information on dangerous areas or revise the advice based on user feedback. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, minimizing the risk of crime.

[0068] The management department provides analysis results through a dashboard for city administrators. Specifically, it reviews crime patterns and information on high-risk areas to plan efficient resource allocation and strategic crime prevention measures. For example, if a particular area experiences a high crime rate, police patrols will be concentrated in that area. Patrol times and routes can also be optimized according to the time of day and type of crime. Furthermore, the management department reviews the placement of security cameras to build an effective surveillance system. For example, the placement and orientation of security cameras can be adjusted to match areas and times of high crime rates. The management department also evaluates the effectiveness of crime prevention measures and reviews them as needed. For example, it regularly evaluates whether specific measures are effective and considers new measures if they are ineffective. Based on this information, the management department can comprehensively manage crime prevention measures across the entire city and implement efficient and effective measures. In addition, the management department can share information and collaborate with other cities and regions to promote wide-area crime prevention measures. For example, it can collaborate with neighboring cities and regions to conduct wide-area patrols and share information to suppress crime. This will allow the management department to improve the overall safety of the city and provide an environment where citizens can live with peace of mind.

[0069] The data collection unit collects data such as security camera video data, social media posts, and police reports. For example, the data collection unit can collect security camera video data. The data collection unit can also collect social media posts. The data collection unit can also collect police reports. For example, the data collection unit collects security camera video data in high resolution and uses it for analysis. The data collection unit collects social media posts in real time to detect signs of crime. The data collection unit periodically collects police reports to analyze crime trends. This improves the accuracy of the analysis by collecting information from diverse data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input security camera video data into AI and have the AI ​​perform the analysis of the video data.

[0070] The analysis unit analyzes the frequency, timing, and type of crimes occurring in a specific area. For example, the analysis unit can analyze the frequency of crimes in a specific area. The analysis unit can also analyze the timing of crimes. The analysis unit can also analyze the types of crimes. For example, the analysis unit can analyze the frequency of crimes in a specific area over time to understand crime trends. The analysis unit can analyze the timing of crimes to identify times when crimes are more likely to occur. The analysis unit can analyze the types of crimes to understand the characteristics of crimes in each area. This makes it possible to identify areas and times when crimes are more likely to occur. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI predict crime patterns and dangerous areas.

[0071] The information provider displays information about dangerous areas and crime occurrences in the vicinity when the user enters their current location. For example, when the user enters their current location, the information provider displays information about dangerous areas and crime occurrences in the vicinity on a map. The information provider can also notify the user of crime information in real time. The information provider can also provide the user with advice to support safe behavior. For example, when the user enters their current location, the information provider displays information about dangerous areas and crime occurrences in the vicinity on a map. The information provider notifies the user of crime information in real time. The information provider provides the user with advice to support safe behavior. This allows the user to obtain information that supports their own safe behavior. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the analysis results into AI and have the AI ​​provide the user with the most appropriate information.

[0072] The management department reviews information on crime patterns and high-risk areas, and plans efficient resource allocation and strategic crime prevention measures. For example, the management department optimizes police patrol routes based on information on crime patterns and high-risk areas. The management department reviews the placement of security cameras and establishes an effective surveillance system. The management department evaluates the effectiveness of crime prevention measures and revises them as needed. This allows urban administrators to implement efficient resource allocation and strategic crime prevention measures. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input analysis results into AI and have the AI ​​plan optimal resource allocation and crime prevention measures.

[0073] The management department optimizes police patrol routes and reviews the placement of security cameras. For example, the management department optimizes police patrol routes. The management department can also review the placement of security cameras. The management department can evaluate the effectiveness of crime prevention measures and revise them as needed. For example, the management department optimizes police patrol routes and focuses patrols on areas with high crime rates. The management department reviews the placement of security cameras and establishes an effective surveillance system. The management department evaluates the effectiveness of crime prevention measures and revises them as needed. This makes it possible to optimize police patrol routes and review the placement of security cameras. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input analysis results into AI and have the AI ​​execute the optimal patrol routes and security camera placements.

[0074] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit immediately begins data collection and provides information in real time. If the user is relaxed, the data collection unit can also perform periodic data collection and update the information as needed. If the user is excited, the data collection unit can also perform frequent data collection and provide the latest information. For example, if the user is feeling anxious, the data collection unit immediately begins data collection and provides information in real time. If the user is relaxed, the data collection unit performs periodic data collection and updates the information as needed. If the user is excited, the data collection unit performs frequent data collection and provides the latest information. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​execute the timing of data collection.

[0075] The data collection unit analyzes past crime data and selects the optimal data collection method. For example, the data collection unit may concentrate data collection during specific time periods based on past crime data. The data collection unit can also analyze past crime data and enhance data collection in specific areas. The data collection unit can also refer to past crime data and select a data collection method corresponding to specific crime patterns. For example, the data collection unit may concentrate data collection during specific time periods based on past crime data. The data collection unit may analyze past crime data and enhance data collection in specific areas. The data collection unit may refer to past crime data and select a data collection method corresponding to specific crime patterns. This improves the efficiency of data collection by selecting the optimal data collection method based on past crime data. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past crime data into AI and have the AI ​​execute the optimal data collection method.

[0076] The data collection unit adjusts the collection scope when collecting data, taking into account social factors such as specific events or festivals. For example, the data collection unit strengthens data collection in the surrounding areas when a large-scale event is held. The data collection unit can also concentrate data collection in specific areas during festivals and celebrations. The data collection unit can also increase the frequency of data collection during times when social gatherings are high. For example, the data collection unit strengthens data collection in the surrounding areas when a large-scale event is held. The data collection unit concentrates data collection in specific areas during festivals and celebrations. The data collection unit increases the frequency of data collection during times when social gatherings are high. By adjusting the data collection scope to take social factors into account, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on social factors into AI and have the AI ​​perform the adjustment of the collection scope.

[0077] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit prioritizes collecting crime-related data. If the user is relaxed, the data collection unit may also prioritize collecting general safety information. If the user is agitated, the data collection unit may also prioritize collecting real-time incident information. For example, if the user is feeling anxious, the data collection unit prioritizes collecting crime-related data. If the user is relaxed, the data collection unit prioritizes collecting general safety information. If the user is agitated, the data collection unit prioritizes collecting real-time incident information. This allows for the priority collection of more important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​prioritize the data to be collected.

[0078] The data collection unit prioritizes collecting highly relevant data, taking geographical location information into consideration. For example, the data collection unit prioritizes collecting crime data around the user's current location. The data collection unit can also pre-collect data for areas the user plans to travel to. The data collection unit can also prioritize collecting data for places the user frequently visits. For example, the data collection unit prioritizes collecting crime data around the user's current location. The data collection unit pre-collects data for areas the user plans to travel to. The data collection unit prioritizes collecting data for places the user frequently visits. This improves the accuracy of data collection by prioritizing the collection of highly relevant data, taking geographical location information into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI and have the AI ​​collect highly relevant data.

[0079] The data collection unit analyzes social media activity and collects relevant data during data collection. For example, the data collection unit collects crime-related posts on social media in real time. The data collection unit can also monitor specific hashtags on social media and collect relevant data. The data collection unit can also analyze users' social media activity and collect relevant regional data. For example, the data collection unit collects crime-related posts on social media in real time. The data collection unit monitors specific hashtags on social media and collects relevant data. The data collection unit analyzes users' social media activity and collects relevant regional data. This enables more effective data collection by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into AI and have the AI ​​perform the collection of relevant data.

[0080] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is excited, the analysis unit can also provide visually stimulating analysis results. For example, if the user is feeling anxious, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit provides detailed analysis results. If the user is excited, the analysis unit provides visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0081] The analysis unit adjusts the level of detail of the analysis based on the severity of the crime during the analysis. For example, the analysis unit performs a detailed analysis for serious crimes. The analysis unit can also perform a simplified analysis for minor crimes. The analysis unit can also perform an analysis with an appropriate level of detail depending on the type of crime. For example, the analysis unit performs a detailed analysis for serious crimes. The analysis unit performs a simplified analysis for minor crimes. The analysis unit performs an appropriate level of detail depending on the type of crime. This allows for more effective analysis by adjusting the level of detail of the analysis based on the severity of the crime. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input crime data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0082] The analysis unit applies different analysis algorithms depending on the crime category during analysis. For example, the analysis unit applies a specific algorithm to violent crimes. The analysis unit may also apply a different algorithm to property crimes. The analysis unit may also apply a dedicated algorithm to cybercrimes. For example, the analysis unit applies a specific algorithm to violent crimes. The analysis unit applies a different algorithm to property crimes. The analysis unit applies a dedicated algorithm to cybercrimes. By applying different analysis algorithms depending on the crime category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input crime category data into a generative AI and have the generative AI apply an appropriate analysis algorithm.

[0083] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0084] The analysis unit determines the priority of the analysis based on the timing of the crimes. For example, the analysis unit prioritizes the analysis of recently occurring crimes. The analysis unit can also analyze crimes that are likely to occur at specific times based on past crime data. The analysis unit can also prioritize the analysis of crimes related to seasons or events. For example, the analysis unit prioritizes the analysis of recently occurring crimes. The analysis unit analyzes crimes that are likely to occur at specific times based on past crime data. The analysis unit prioritizes the analysis of crimes related to seasons or events. This allows for more effective analysis by determining the priority of the analysis based on the timing of the crimes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input crime timing data into a generative AI and have the generative AI determine the priority of the analysis.

[0085] The analysis unit adjusts the order of analysis based on the relevance of the crimes during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant crimes. The analysis unit can also postpone the analysis of less relevant crimes. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the crimes. For example, the analysis unit prioritizes the analysis of highly relevant crimes. The analysis unit postpones the analysis of less relevant crimes. The analysis unit dynamically adjusts the order of analysis according to the relevance of the crimes. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the crimes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input crime relevance data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0086] The information provider estimates the user's emotions and adjusts the method of information delivery based on the estimated emotions. For example, if the user is feeling anxious, the information provider will provide simple and easily visible information. If the user is relaxed, the information provider may also provide detailed information. If the user is excited, the information provider may also provide visually stimulating information. For example, if the user is feeling anxious, the information provider will provide simple and easily visible information. If the user is relaxed, the information provider will provide detailed information. If the user is excited, the information provider will provide visually stimulating information. By adjusting the method of information delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​adjust the way information is provided.

[0087] The information delivery unit selects the optimal delivery method by referring to the user's past behavior history when providing information. For example, the information delivery unit may prioritize information delivery methods that the user has used in the past. The information delivery unit can also propose the optimal information delivery method based on the user's past behavior history. The information delivery unit can also provide customized information delivery based on the user's behavior patterns. For example, the information delivery unit may prioritize information delivery methods that the user has used in the past. The information delivery unit may propose the optimal information delivery method based on the user's past behavior history. The information delivery unit provides customized information delivery based on the user's behavior patterns. This makes it possible to provide more effective information delivery by selecting the optimal delivery method by referring to the user's past behavior history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit may input user behavior history data into AI and have the AI ​​select the optimal information delivery method.

[0088] The information provider customizes the information provided based on the user's current location. For example, the provider prioritizes providing crime information around the user's current location. The provider can also provide information in advance about areas the user plans to visit. The provider can also suggest optimal safety actions based on the user's current location. For example, the provider prioritizes providing crime information around the user's current location. The provider provides information in advance about areas the user plans to visit. The provider suggests optimal safety actions based on the user's current location. This allows for more appropriate information to be provided by customizing the information provided based on the user's current location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's location data into AI and have the AI ​​customize the information provided.

[0089] The information provider estimates the user's emotions and determines the priority of information provision based on the estimated emotions. For example, if the user is feeling anxious, the information provider will prioritize providing urgent information. If the user is relaxed, the information provider may also prioritize providing general safety information. If the user is agitated, the information provider may also prioritize providing real-time incident information. For example, if the user is feeling anxious, the information provider will prioritize providing urgent information. If the user is relaxed, the information provider will prioritize providing general safety information. If the user is agitated, the information provider will prioritize providing real-time incident information. This allows for the prioritization of information provision according to the user's emotions, thereby prioritizing the provision of more important information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​determine the priority of information provision.

[0090] The information delivery unit selects the optimal delivery method by considering the user's device information when providing information. For example, if the user is using a smartphone, the information delivery unit will provide information tailored to the screen size. If the user is using a tablet, the information delivery unit can also provide information optimized for a larger screen. If the user is using a smartwatch, the information delivery unit can also provide concise and highly visible information. For example, if the user is using a smartphone, the information delivery unit will provide information tailored to the screen size. If the user is using a tablet, the information delivery unit will provide information optimized for a larger screen. If the user is using a smartwatch, the information delivery unit will provide concise and highly visible information. By selecting the optimal delivery method by considering the user's device information, more effective information delivery becomes possible. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0091] The information provider analyzes the user's social media activity and provides relevant information when providing information. For example, the provider analyzes the user's social media posts and provides relevant crime information. The provider can also provide relevant information based on the activity of accounts the user follows. The provider can also refer to the user's social media activity and provide the most appropriate information. For example, the provider analyzes the user's social media posts and provides relevant crime information. The provider provides relevant information based on the activity of accounts the user follows. The provider refers to the user's social media activity and provides the most appropriate information. By analyzing the user's social media activity and providing relevant information, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media data into AI and have AI perform the provision of relevant information.

[0092] The management department estimates the user's emotions and adjusts its management methods based on the estimated emotions. For example, if the user is feeling anxious, the management department will adopt a management method that provides a rapid response. If the user is relaxed, the management department may also adopt a regular management method. If the user is agitated, the management department may also adopt a more frequent management method. For example, if the user is feeling anxious, the management department will adopt a management method that provides a rapid response. If the user is relaxed, the management department will adopt a regular management method. If the user is agitated, the management department will adopt a more frequent management method. This allows for more appropriate management by adjusting management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into the AI ​​and have the AI ​​adjust management methods accordingly.

[0093] The management department selects the optimal management method by referring to past management data during management. For example, the management department may concentrate management on specific time periods based on past management data. The management department can also analyze past management data and strengthen management in specific areas. The management department can also refer to past management data and select a management method that corresponds to a specific crime pattern. For example, the management department may concentrate management on specific time periods based on past management data. The management department may analyze past management data and strengthen management in specific areas. The management department may refer to past management data and select a management method that corresponds to a specific crime pattern. This improves the accuracy of management by selecting the optimal management method by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department may input past management data into AI and have the AI ​​select the optimal management method.

[0094] The management department customizes management methods based on specific regions and time periods during management. For example, if the crime rate is high in a particular region, the management department will focus its management efforts on that region. If crimes are frequent during a particular time period, the management department can also concentrate its management efforts during that time period. The management department can also adopt different management methods depending on the region and time period. For example, if the crime rate is high in a particular region, the management department will focus its management efforts on that region. If crimes are frequent during a particular time period, the management department will concentrate its management efforts during that time period. The management department adopts different management methods depending on the region and time period. This allows for more effective management by customizing management methods based on specific regions and time periods. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input regional and time period data into AI and have the AI ​​perform the customization of management methods.

[0095] The management department estimates the user's emotions and determines management priorities based on the estimated emotions. For example, if the user is feeling anxious, the management department will prioritize urgent management tasks. If the user is relaxed, the management department may also prioritize routine management tasks. If the user is agitated, the management department may also prioritize frequent management tasks. For example, if the user is feeling anxious, the management department will prioritize urgent management tasks. If the user is relaxed, the management department will prioritize routine management tasks. If the user is agitated, the management department will prioritize frequent management tasks. This allows for prioritizing management tasks according to the user's emotions, thereby prioritizing more important management tasks. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input user sentiment data into an AI and have the AI ​​determine management priorities.

[0096] The management department selects the optimal management method when managing a user, taking geographical location information into consideration. For example, the management department selects the optimal management method based on crime information around the user's current location. The management department can also adjust the management method based on information about areas the user plans to travel to. The management department can also select the management method based on information about places the user frequently visits. For example, the management department selects the optimal management method based on crime information around the user's current location. The management department adjusts the management method based on information about areas the user plans to travel to. The management department selects the management method based on information about places the user frequently visits. By selecting the optimal management method while considering geographical location information, the accuracy of management is improved. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input geographical location information into AI and have the AI ​​select the optimal management method.

[0097] The management department improves the accuracy of its management by referring to relevant literature and data during management. For example, the management department improves management methods by referring to the latest literature on crime prevention. The management department can also improve the accuracy of management by analyzing past crime data. The management department can also introduce optimal management methods by referring to management data from other cities. For example, the management department improves management methods by referring to the latest literature on crime prevention. The management department improves the accuracy of management by analyzing past crime data. The management department introduces optimal management methods by referring to management data from other cities. This makes more effective management possible by improving the accuracy of management by referring to relevant literature and data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant literature and data into AI and have the AI ​​perform the improvement of management accuracy.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can also estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is feeling anxious, data collection can begin immediately, providing information in real time. If the user is relaxed, data collection can be performed periodically, updating the information as needed. If the user is excited, data collection can be performed frequently, providing the latest information. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0100] The data collection unit can also analyze past crime data and select the optimal data collection method. For example, it can concentrate data collection during specific time periods based on past crime data. It can also analyze past crime data and strengthen data collection in specific areas. It can refer to past crime data and select a data collection method that corresponds to a specific crime pattern. This improves the efficiency of data collection by selecting the optimal data collection method based on past crime data.

[0101] The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is feeling anxious, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is excited, it can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results.

[0102] The information delivery department can also select the most suitable delivery method by referring to the user's past behavior history when providing information. For example, it can prioritize selecting information delivery methods that the user has used in the past. It can also suggest the most suitable information delivery method based on the user's past behavior history. It can also provide customized information based on the user's behavior patterns. By selecting the most suitable delivery method by referring to the user's past behavior history, more effective information delivery becomes possible.

[0103] The management department can also estimate the user's emotions and adjust management methods based on those estimates. For example, if a user is feeling anxious, they can adopt management methods that require a quick response. If a user is relaxed, they can adopt regular management methods. If a user is agitated, they can adopt frequent management methods. By adjusting management methods according to the user's emotions, more appropriate management becomes possible.

[0104] The data collection unit can also adjust the collection scope to take into account social factors such as specific events or festivals. For example, data collection can be intensified in the surrounding areas when a large-scale event is held. Data collection can also be concentrated in specific areas during festivals or celebrations. The frequency of data collection can also be increased during times when social gatherings are high. By adjusting the data collection scope to take social factors into account, more effective data collection becomes possible.

[0105] The analysis unit can also adjust the level of detail of the analysis based on the severity of the crime. For example, a detailed analysis can be performed for serious crimes, while a simplified analysis can be performed for minor crimes. The analysis can also be performed at an appropriate level of detail depending on the type of crime. By adjusting the level of detail of the analysis based on the severity of the crime, a more effective analysis becomes possible.

[0106] The information delivery system can also estimate the user's emotions and determine the priority of information delivery based on those emotions. For example, if a user is feeling anxious, it can prioritize providing urgent information. If a user is relaxed, it can prioritize providing general safety information. If a user is agitated, it can prioritize providing real-time incident information. By prioritizing information delivery according to the user's emotions, it is possible to deliver more important information first.

[0107] The management department can also select the optimal management method by considering geographical location information during management. For example, they can select the optimal management method based on crime information around the user's current location. They can also adjust the management method based on information about areas the user plans to visit. They can also select the management method based on information about places the user frequently visits. By selecting the optimal management method while considering geographical location information, the accuracy of management is improved.

[0108] The analysis unit can also estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, it can provide a short, concise analysis. If the user is relaxed, it can provide a detailed analysis. If the user is excited, it can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, it is possible to provide more appropriate analysis results.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data from sources such as security cameras, social media, and police reports. For example, it collects high-resolution video data from security cameras and uses it for analysis. It collects social media posts in real time to detect signs of crime. It regularly collects police reports to analyze crime trends. Step 2: The analysis unit analyzes the data collected by the collection unit to predict crime patterns and high-risk areas. For example, it analyzes the frequency of crimes in a specific area over time to understand crime trends. It analyzes the time of day when crimes occur to identify times when crimes are more likely to occur. It analyzes the types of crimes to understand the characteristics of crime in each area. Step 3: The service provider delivers the analysis results obtained by the analysis unit through a smartphone app for citizens. For example, when a user enters their current location, the app displays nearby dangerous areas and crime occurrence information on a map. It also notifies users of crime information in real time and provides advice to support safe behavior. Step 4: The management department provides the analysis results through a dashboard for city administrators. For example, they optimize police patrol routes based on crime patterns and information on high-risk areas. They review the placement of security cameras and build an effective surveillance system. They evaluate the effectiveness of crime prevention measures and revise them as needed.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as a processing unit that collects video data from security cameras. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the collected data and predicts crime patterns and dangerous areas. The provision unit is implemented by the control unit 46A of the smart device 14 as a processing unit that provides the analysis results through a smartphone application for citizens. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that provides the analysis results through a dashboard for city administrators. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as a processing unit that collects video data from security cameras. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the collected data and predicts crime patterns and dangerous areas. The provision unit is implemented by the control unit 46A of the smart glasses 214 as a processing unit that provides the analysis results through a smartphone application for citizens. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that provides the analysis results through a dashboard for city administrators. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as a processing unit that collects video data from security cameras. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the collected data and predicts crime patterns and dangerous areas. The provision unit is implemented by the control unit 46A of the headset terminal 314 as a processing unit that provides the analysis results through a smartphone application for citizens. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that provides the analysis results through a dashboard for city administrators. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as a processing unit that collects video data from security cameras. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that analyzes the collected data and predicts crime patterns and dangerous areas. The provision unit is implemented by the control unit 46A of the robot 414 as a processing unit that provides the analysis results through a smartphone application for citizens. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that provides the analysis results through a dashboard for city administrators. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The data collection department collects data from security cameras, social media, police reports, etc. An analysis unit analyzes the data collected by the aforementioned collection unit to predict crime patterns and dangerous areas, A provisioning unit provides the analysis results obtained by the aforementioned analysis unit through a smartphone application for citizens, The system includes a management unit that provides the aforementioned analysis results through a dashboard for urban administrators. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as security camera footage, social media posts, and police reports. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the frequency, timing, and types of crimes occurring in specific areas. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, When a user enters their current location, the system displays information about nearby dangerous areas and crime occurrences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, We will review crime patterns and information on high-risk areas to plan efficient resource allocation and strategic crime prevention measures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Optimize police patrol routes and review the placement of security cameras. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past crime data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, adjust the collection scope to take into account social factors such as specific events or festivals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the severity of the crime. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the crime. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the crime occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the crimes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, the system selects the optimal method of delivery by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, customize the content based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the optimal method of delivery is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During management, past management data is referenced to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During management, customize management methods based on specific regions and time zones. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During management, the optimal management method is selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During management, we improve the accuracy of management by referring to relevant literature and data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data from security cameras, social media, police reports, etc. An analysis unit analyzes the data collected by the aforementioned collection unit to predict crime patterns and dangerous areas, A provisioning unit provides the analysis results obtained by the aforementioned analysis unit through a smartphone application for citizens, The system includes a management unit that provides the aforementioned analysis results through a dashboard for urban administrators. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as security camera footage, social media posts, and police reports. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the frequency, timing, and types of crimes occurring in specific areas. The system according to feature 1.

4. The aforementioned supply unit is, When a user enters their current location, the system displays information about nearby dangerous areas and crime occurrences. The system according to feature 1.

5. The aforementioned management department, We will review crime patterns and information on high-risk areas to plan efficient resource allocation and strategic crime prevention measures. The system according to feature 1.

6. The aforementioned management department, Optimize police patrol routes and review the placement of security cameras. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past crime data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, adjust the collection scope to take into account social factors such as specific events or festivals. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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