System

Abstract

An object of a system according to an embodiment is to predict the occurrence of trouble and optimally deploy security resources.SOLUTION: A system includes a collection unit, an analysis unit, an arrangement unit, and an adjustment unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and predicts the occurrence of a trouble. The arrangement unit arranges the security resource according to the trouble occurrence place and time zone predicted by the analysis unit. The adjustment unit monitors the deployment status of the security resources deployed by the deployment unit in real time, and adjusts the security level as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately predict the occurrence of trouble and optimally allocate security resources, so there is room for improvement.

[0005] The system according to the embodiment aims to predict the occurrence of trouble and optimally allocate security resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a deployment unit, and an adjustment unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and predicts the occurrence of trouble. The deployment unit deploys security resources according to the location and time period of the trouble predicted by the analysis unit. The adjustment unit monitors in real time the deployment status of the security resources deployed by the deployment unit and adjusts security force as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can predict the occurrence of trouble and optimally allocate security resources. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A security optimization system according to an embodiment of the present invention uses a generation AI to predict the occurrence of troubles caused by a decline in public safety or the unique atmosphere of an event, and optimizes security efforts. In the security optimization system, the generation AI analyzes past security data and event data to predict the occurrence of troubles. Then, based on the prediction results, the system optimizes security efforts and deploys necessary security resources. This prevents troubles and enables rapid response. For example, the security optimization system collects data such as past crime occurrences, the number of event participants, the type of event, and weather data, and the generation AI analyzes the data using machine learning. For example, by analyzing the occurrence of troubles and crime rates at past events, it is possible to predict the occurrence of troubles. Next, the security optimization system optimally deploys security resources based on the prediction results. For example, troubles can be prevented by deploying security guards or installing surveillance cameras in predicted trouble locations. Furthermore, the security optimization system monitors the deployment status of security resources in real time and adjusts security efforts as necessary. For example, if an abnormality is detected in a predicted trouble location, it can respond by deploying additional security guards. This enables rapid response and prevents troubles. As a result, the security optimization system will be able to create a safe social environment and ensure the continuity of events.As a result, the security optimization system will be able to use generative AI to predict the occurrence of trouble and optimize security forces, promoting quick responses and preventing trouble before it occurs, thereby creating a safe social environment and ensuring the continuity of events.

[0029] A security optimization system according to an embodiment includes a collection unit, an analysis unit, a deployment unit, and a coordination unit. The collection unit collects data. The data includes, for example, past crime occurrences, the number of event participants, the type of event, and weather data, but is not limited to these examples. For example, the collection unit acquires past crime occurrences from a database. The collection unit can also acquire the number of event participants from an event organizer. The collection unit can also acquire weather data from a weather database. For example, the collection unit acquires past crime occurrences from a database, the number of event participants from the event organizer, and weather data from the weather database. The analysis unit uses a generative AI to analyze the data collected by the collection unit and predict the occurrence of trouble. The analysis is performed, for example, using a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the data using deep learning to predict the occurrence of trouble. The analysis unit can also analyze the data using a support vector machine. The analysis unit can also analyze the data using a random forest. For example, the analysis unit analyzes the data using deep learning to predict the occurrence of trouble. The deployment unit deploys security resources according to the location and time period of the occurrence of the trouble predicted by the analysis unit. Examples of security resources include, but are not limited to, security guards, surveillance cameras, and drones. For example, the deployment unit deploys security guards at the location where the trouble is predicted to occur. The deployment unit can also install surveillance cameras at the location where the trouble is predicted to occur. The deployment unit can also deploy drones at the location where the trouble is predicted to occur. For example, the deployment unit deploys security guards, installs surveillance cameras, and deploys drones at the location where the trouble is predicted to occur. The adjustment unit monitors the deployment status of the security resources deployed by the deployment unit in real time and adjusts security efforts as necessary. Adjustments are made, for example, when an abnormality is detected, but are not limited to, examples. For example, the adjustment unit deploys additional security guards when an abnormality is detected. The adjustment unit can also adjust the field of view of a surveillance camera when an abnormality is detected.The adjustment unit can also change the drone's flight route if an abnormality is detected. For example, if an abnormality is detected, the adjustment unit may deploy additional security guards, adjust the field of view of the surveillance cameras, and change the drone's flight route. This enables the security optimization system according to the embodiment to prevent problems and respond quickly through data collection, analysis, and deployment and adjustment of security resources.

[0030] The collection unit can collect past crime occurrences, the number of event participants, the type of event, weather data, and other related data. For example, the collection unit acquires past crime occurrences from a database. The collection unit can also acquire the number of event participants from the event organizer. The collection unit can also acquire weather data from a weather database. For example, the collection unit acquires past crime occurrences from a database, acquires the number of event participants from the event organizer, and acquires weather data from the weather database. This collection of diverse data improves the accuracy of trouble predictions. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can acquire past crime occurrences from a database and input them into the generation AI, which then analyzes and collects the data.

[0031] The analysis unit can analyze the collected data using machine learning and predict the occurrence of trouble. The analysis unit can analyze the data using, for example, deep learning and predict the occurrence of trouble. The analysis unit can also analyze the data using a support vector machine. The analysis unit can also analyze the data using a random forest. For example, the analysis unit can analyze the data using deep learning and predict the occurrence of trouble. In this way, the use of machine learning improves the accuracy of the prediction of the occurrence of trouble. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can analyze the data and predict the occurrence of trouble.

[0032] The placement unit can place security guards at locations where predicted trouble will occur. For example, the placement unit places security guards at locations where predicted trouble will occur. The placement unit can also install surveillance cameras at locations where predicted trouble will occur. The placement unit can also place drones at locations where predicted trouble will occur. For example, the placement unit places security guards at locations where predicted trouble will occur, installs surveillance cameras, and places drones. By placing security guards at locations where predicted trouble will occur, trouble can be prevented before it occurs. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the placement unit can input predicted locations where trouble will occur into a generation AI, which can then optimize the placement of security guards.

[0033] The placement unit can install a surveillance camera at a location where a predicted trouble will occur. For example, the placement unit installs a surveillance camera at a location where a predicted trouble will occur. The placement unit can also deploy a security guard at a location where a predicted trouble will occur. The placement unit can also deploy a drone at a location where a predicted trouble will occur. For example, the placement unit installs a surveillance camera at a location where a predicted trouble will occur, deploys a security guard, and deploys a drone. By installing the surveillance camera, it becomes easier to monitor the occurrence of a trouble. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the placement unit can input a predicted location where a trouble will occur into the generation AI, and the generation AI can optimize the installation of the surveillance camera.

[0034] The adjustment unit monitors the deployment status of security resources in real time and can deploy additional security guards when an abnormality is detected. For example, the adjustment unit deploys additional security guards when an abnormality is detected. The adjustment unit can also adjust the field of view of a surveillance camera when an abnormality is detected. The adjustment unit can also change the flight route of a drone when an abnormality is detected. For example, the adjustment unit deploys additional security guards, adjusts the field of view of a surveillance camera, and changes the flight route of a drone when an abnormality is detected. This enables real-time monitoring and a quick response when an abnormality is detected. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input response procedures when an abnormality is detected into the generation AI, and the generation AI can optimize the adjustment of security resources.

[0035] The collection unit can detect signs of trouble by collecting social media posts in addition to past crime occurrence status and event data. For example, the collection unit can analyze social media posts to detect suspicious activity in a specific area. The collection unit can also collect social media posts from event participants to detect signs of trouble early. The collection unit can also analyze social media hashtags to detect signs of trouble related to a specific event. For example, the collection unit can analyze social media posts to detect suspicious activity in a specific area. In this way, by collecting social media posts, signs of trouble can be detected early. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input social media posts into a generation AI, which can detect signs of trouble.

[0036] When collecting data, the collection unit can collect detailed data by focusing on a specific area or time period. For example, the collection unit collects detailed information on crime occurrences in a specific area at night. The collection unit can also collect data during a specific time period when a large-scale event is being held. The collection unit can also collect data on weekends in a specific area to predict the occurrence of trouble. For example, the collection unit collects detailed information on crime occurrences in a specific area at night. This enables more detailed data collection by focusing on a specific area or time period. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input data on a specific area or time period into the generation AI, which then collects detailed data.

[0037] The collection unit can analyze audio and video data during data collection to detect abnormal behavior and audio. The collection unit can, for example, analyze video data from a surveillance camera to detect abnormal behavior. The collection unit can also analyze audio data to detect abnormal audio and noise. The collection unit can also combine and analyze video data and audio data to detect signs of trouble. For example, the collection unit analyzes video data from a surveillance camera to detect abnormal behavior. In this way, abnormal behavior and audio can be detected by analyzing the audio and video data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input audio and video data to a generation AI, which can detect abnormal behavior and audio.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit can prioritize collecting crime occurrence status in a specific area. The collection unit can also prioritize collecting data around an event venue. The collection unit can also prioritize collecting social media posts in a specific area. For example, the collection unit prioritizes collecting crime occurrence status in a specific area. In this way, highly relevant data can be collected preferentially by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input geographical location information into the generation AI, which can then prioritize collecting highly relevant data.

[0039] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the content of the user's social media posts and collect related data. The collection unit can also analyze the activities of the user's social media friends and collect related data. The collection unit can also analyze the user's social media hashtags and collect related data. For example, the collection unit can analyze the content of the user's social media posts and collect related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's social media activities into the generation AI, which can collect related data.

[0040] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit, for example, improves the collection method based on past data collection results. The collection unit can also customize the collection method by reflecting user feedback. The collection unit can also optimize the collection method by referring to past trouble occurrence situations. For example, the collection unit improves the collection method based on past data collection results. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input past feedback into the generation AI, which can customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also allocate analysis resources according to the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to crime occurrence data. The analysis unit can also apply a different analysis algorithm to event data. The analysis unit can also apply a dedicated analysis algorithm to social media posts. For example, the analysis unit applies a specific analysis algorithm to crime occurrence data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data category into the generation AI, which then applies an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing past analysis results as feedback. For example, the analysis unit improves the analysis algorithm based on past analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also allocate analysis resources based on the time of submission. For example, the analysis unit prioritizes analysis of the most recent data. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also allocate analysis resources based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI, which can then adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple terms to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with extensive expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple terms to a user with little expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0047] During deployment, the deployment unit can create a detailed deployment plan for security resources according to the location and time period of the predicted trouble occurrence. The deployment unit, for example, deploys security guards to the location where the predicted trouble will occur. The deployment unit can also concentrate security resources in the time period when the predicted trouble will occur. The deployment unit can also create a deployment plan for security resources according to the location and time period of the predicted trouble occurrence. For example, the deployment unit deploys security guards to the location where the predicted trouble will occur. This enables efficient security by creating a detailed deployment plan for security resources according to the location and time period of the predicted trouble occurrence. Some or all of the above-mentioned processing in the deployment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input data on the location and time period of the predicted trouble occurrence into the generation AI, which can then create a detailed deployment plan.

[0048] The deployment unit can optimally deploy the security resources according to the type of security resource at the time of deployment. For example, the deployment unit deploys security guards at a location where a predicted trouble will occur. The deployment unit can also install surveillance cameras at a location where a predicted trouble will occur. The deployment unit can also deploy drones at a location where a predicted trouble will occur. For example, the deployment unit deploys security guards at a location where a predicted trouble will occur. This enables efficient security by optimally deploying the security resources according to the type of security resource. Some or all of the above-described processing in the deployment unit may be performed using, or without using, a generation AI. For example, the deployment unit can input the type of security resource into the generation AI, which can then perform the optimal deployment.

[0049] The deployment unit can improve the accuracy of deployment by referring to past deployment results when deploying. The deployment unit, for example, improves the deployment method of security resources based on past deployment results. The deployment unit can also improve the deployment accuracy of security resources by referring to past deployment results. The deployment unit can also use past deployment results as feedback to improve the deployment accuracy of security resources. For example, the deployment unit improves the deployment method of security resources based on past deployment results. In this way, by referring to past deployment results, the accuracy of deployment is improved. Some or all of the above-mentioned processing in the deployment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input past deployment results into the generation AI, which can improve the deployment accuracy.

[0050] During deployment, the deployment unit can prioritize deploying security resources to highly relevant locations by taking geographical location information into consideration. For example, the deployment unit deploys security resources by taking into consideration the crime occurrence situation in a specific area. The deployment unit can also prioritize deploying security resources around an event venue. The deployment unit can also deploy security resources by taking into consideration the content of social media posts in a specific area. For example, the deployment unit deploys security resources by taking into consideration the crime occurrence situation in a specific area. In this way, by taking geographical location information into consideration, security resources can be prioritized to be deployed to highly relevant locations. Some or all of the above-described processing in the deployment unit may be performed using, or without, a generation AI. For example, the deployment unit can input geographical location information to the generation AI, and the generation AI can prioritize deploying security resources to highly relevant locations.

[0051] During deployment, the deployment unit can analyze the content of social media posts and deploy security resources to related locations. For example, the deployment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and deploy security resources. The deployment unit can also collect social media posts from event participants and deploy security resources to locations where there are signs of trouble. The deployment unit can also analyze social media hashtags and deploy security resources to locations where there are signs of trouble related to a specific event. For example, the deployment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and deploy security resources. In this way, security resources can be deployed to related locations by analyzing the content of social media posts. Some or all of the above-mentioned processing in the deployment unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input the content of social media posts into a generation AI, and the generation AI can deploy security resources to related locations.

[0052] The deployment unit can customize the deployment method by reflecting past feedback at the time of deployment. The deployment unit can improve the deployment method of security resources based on, for example, past deployment results. The deployment unit can also customize the deployment method of security resources by reflecting user feedback. The deployment unit can also optimize the deployment method of security resources by referring to past trouble occurrence situations. For example, the deployment unit improves the deployment method of security resources based on past deployment results. In this way, the deployment method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the deployment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input past feedback into the generation AI, which can customize the deployment method.

[0053] During adjustment, the adjustment unit can analyze data collected in real time and optimize the allocation of security resources. The adjustment unit, for example, optimizes the allocation of security resources based on data collected in real time. The adjustment unit can also analyze data collected in real time and create a security resource allocation plan. The adjustment unit can also improve the security resource allocation method based on data collected in real time. For example, the adjustment unit optimizes the allocation of security resources based on data collected in real time. In this way, the allocation of security resources can be optimized by analyzing the data collected in real time. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input data collected in real time to a generation AI, which can optimize the allocation of security resources.

[0054] During adjustment, the adjustment unit can set detailed response procedures to be taken when an abnormality is detected. For example, the adjustment unit sets detailed response procedures to be taken when an abnormality is detected. The adjustment unit can also adjust security resources based on the response procedures to be taken when an abnormality is detected. The adjustment unit can also use the response procedures to be taken when an abnormality is detected as feedback to improve the method of adjusting security resources. For example, the adjustment unit sets detailed response procedures to be taken when an abnormality is detected. In this way, setting detailed response procedures to be taken when an abnormality is detected enables a rapid response. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input response procedures to be taken when an abnormality is detected into the generation AI, and the generation AI can set the response procedures in detail.

[0055] When making adjustments, the adjustment unit can improve the accuracy of the adjustment by referring to past adjustment results. The adjustment unit, for example, improves the method for adjusting security resources based on past adjustment results. The adjustment unit can also improve the accuracy of adjusting security resources by referring to past adjustment results. The adjustment unit can also improve the accuracy of adjusting security resources by utilizing past adjustment results as feedback. For example, the adjustment unit improves the method for adjusting security resources based on past adjustment results. In this way, by referring to past adjustment results, the accuracy of adjustment is improved. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input past adjustment results into the generation AI, which can improve the accuracy of the adjustment.

[0056] During adjustment, the adjustment unit can prioritize security resources to highly relevant locations by taking geographical location information into consideration. The adjustment unit can adjust security resources by taking into consideration, for example, the crime occurrence situation in a specific area. The adjustment unit can also prioritize security resources around an event venue. The adjustment unit can also adjust security resources by taking into consideration the content of social media posts in a specific area. For example, the adjustment unit adjusts security resources by taking into consideration the crime occurrence situation in a specific area. In this way, by taking geographical location information into consideration, security resources can be prioritized to highly relevant locations. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input geographical location information to the generation AI, which can then prioritize security resources to highly relevant locations.

[0057] During adjustment, the adjustment unit can analyze the content of social media posts and adjust security resources to relevant locations. For example, the adjustment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and adjust security resources accordingly. The adjustment unit can also collect social media posts from event participants and adjust security resources to locations where there are signs of trouble. The adjustment unit can also analyze social media hashtags and adjust security resources to locations where there are signs of trouble related to a specific event. For example, the adjustment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and adjust security resources accordingly. In this way, security resources can be adjusted to relevant locations by analyzing the content of social media posts. Some or all of the above-described processing in the adjustment unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input the content of social media posts into a generation AI, which can then adjust security resources to relevant locations.

[0058] During adjustment, the adjustment unit can customize the adjustment method by reflecting past feedback. The adjustment unit, for example, improves the security resource adjustment method based on past adjustment results. The adjustment unit can also customize the security resource adjustment method by reflecting user feedback. The adjustment unit can also optimize the security resource adjustment method by referring to past trouble occurrence situations. For example, the adjustment unit improves the security resource adjustment method based on past adjustment results. In this way, the adjustment method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input past feedback into the generation AI, which can customize the adjustment method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting data, the collection unit can analyze the user's behavioral patterns and determine the optimal timing for data collection. For example, data collection can be concentrated on times when the user is frequently moving. It can also collect detailed data during times when the user is stationary. If a user stays in a specific location for a long time, it can prioritize the collection of data related to that location. This enables more effective data collection by optimizing the timing of data collection based on the user's behavioral patterns. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's behavioral data into a generation AI, which can then determine the optimal timing for data collection.

[0061] When collecting data, the collection unit can collect detailed data by focusing on specific areas or time periods. For example, the collection unit can collect detailed information on crime occurrences in a specific area at night. The collection unit can also collect data during specific time periods when a large-scale event is being held. The collection unit can also collect data on weekends in a specific area to predict the occurrence of trouble. This enables more detailed data collection by focusing on specific areas or time periods. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input data on a specific area or time period into the generation AI, which can then collect detailed data.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A simplified analysis can also be performed on data with low importance. The analysis unit can also allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0063] During deployment, the deployment unit can prioritize deploying security resources to highly relevant locations by taking geographical location information into consideration. For example, the deployment unit can deploy security resources by taking into consideration the crime occurrence situation in a specific area. The deployment unit can also prioritize deploying security resources around an event venue. The deployment unit can also deploy security resources by taking into consideration the content of social media posts in a specific area. In this way, by taking geographical location information into consideration, security resources can be prioritized to highly relevant locations. Some or all of the above-mentioned processing in the deployment unit may be performed using, or without, a generation AI. For example, the deployment unit can input geographical location information into the generation AI, and the generation AI can prioritize deploying security resources to highly relevant locations.

[0064] During adjustment, the adjustment unit can analyze data collected in real time and optimize the allocation of security resources. For example, the adjustment unit optimizes the allocation of security resources based on data collected in real time. The adjustment unit can also analyze data collected in real time and create a security resource allocation plan. The adjustment unit can also improve the security resource allocation method based on data collected in real time. In this way, the allocation of security resources can be optimized by analyzing data collected in real time. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI, for example. For example, the adjustment unit can input data collected in real time into a generation AI, which can then optimize the allocation of security resources.

[0065] During adjustment, the adjustment unit can set detailed response procedures to be performed when an abnormality is detected. For example, the adjustment unit sets detailed response procedures to be performed when an abnormality is detected. The adjustment unit can also adjust security resources based on the response procedures to be performed when an abnormality is detected. The adjustment unit can also use the response procedures to be performed when an abnormality is detected as feedback to improve the method of adjusting security resources. In this way, setting detailed response procedures to be performed when an abnormality is detected enables a rapid response. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input response procedures to be performed when an abnormality is detected into the generation AI, and the generation AI can set the response procedures in detail.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data. The data includes past crime occurrences, the number of event participants, the type of event, weather data, etc. For example, the collection unit obtains past crime occurrences from a database, obtains the number of event participants from the event organizer, and obtains weather data from a weather database. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the occurrence of problems. The analysis is performed using generative AI and machine learning algorithms (e.g., deep learning, support vector machines, random forests). Step 3: The deployment unit deploys security resources according to the location and time of the trouble predicted by the analysis unit. Security resources include security guards, surveillance cameras, drones, etc. For example, security guards are deployed, surveillance cameras are installed, and drones are deployed in the location where the trouble is predicted to occur. Step 4: The coordination unit monitors the deployment status of the security resources deployed by the deployment unit in real time and adjusts the security force as necessary. Adjustments are made when an abnormality is detected, and include, for example, deploying additional security guards, adjusting the field of view of surveillance cameras, and changing the flight route of drones.

[0068] (Example 2) A security optimization system according to an embodiment of the present invention uses a generation AI to predict the occurrence of troubles caused by a decline in public safety or the unique atmosphere of an event, and optimizes security efforts. In the security optimization system, the generation AI analyzes past security data and event data to predict the occurrence of troubles. Then, based on the prediction results, the system optimizes security efforts and deploys necessary security resources. This prevents troubles and enables rapid response. For example, the security optimization system collects data such as past crime occurrences, the number of event participants, the type of event, and weather data, and the generation AI analyzes the data using machine learning. For example, by analyzing the occurrence of troubles and crime rates at past events, it is possible to predict the occurrence of troubles. Next, the security optimization system optimally deploys security resources based on the prediction results. For example, troubles can be prevented by deploying security guards or installing surveillance cameras in predicted trouble locations. Furthermore, the security optimization system monitors the deployment status of security resources in real time and adjusts security efforts as necessary. For example, if an abnormality is detected in a predicted trouble location, it can respond by deploying additional security guards. This enables rapid response and prevents troubles. As a result, the security optimization system will be able to create a safe social environment and ensure the continuity of events.As a result, the security optimization system will be able to use generative AI to predict the occurrence of trouble and optimize security forces, promoting quick responses and preventing trouble before it occurs, thereby creating a safe social environment and ensuring the continuity of events.

[0069] A security optimization system according to an embodiment includes a collection unit, an analysis unit, a deployment unit, and a coordination unit. The collection unit collects data. The data includes, for example, past crime occurrences, the number of event participants, the type of event, and weather data, but is not limited to these examples. For example, the collection unit acquires past crime occurrences from a database. The collection unit can also acquire the number of event participants from an event organizer. The collection unit can also acquire weather data from a weather database. For example, the collection unit acquires past crime occurrences from a database, the number of event participants from the event organizer, and weather data from the weather database. The analysis unit uses a generative AI to analyze the data collected by the collection unit and predict the occurrence of trouble. The analysis is performed, for example, using a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the data using deep learning to predict the occurrence of trouble. The analysis unit can also analyze the data using a support vector machine. The analysis unit can also analyze the data using a random forest. For example, the analysis unit analyzes the data using deep learning to predict the occurrence of trouble. The deployment unit deploys security resources according to the location and time period of the occurrence of the trouble predicted by the analysis unit. Examples of security resources include, but are not limited to, security guards, surveillance cameras, and drones. For example, the deployment unit deploys security guards at the location where the trouble is predicted to occur. The deployment unit can also install surveillance cameras at the location where the trouble is predicted to occur. The deployment unit can also deploy drones at the location where the trouble is predicted to occur. For example, the deployment unit deploys security guards, installs surveillance cameras, and deploys drones at the location where the trouble is predicted to occur. The adjustment unit monitors the deployment status of the security resources deployed by the deployment unit in real time and adjusts security efforts as necessary. Adjustments are made, for example, when an abnormality is detected, but are not limited to, examples. For example, the adjustment unit deploys additional security guards when an abnormality is detected. The adjustment unit can also adjust the field of view of a surveillance camera when an abnormality is detected.The adjustment unit can also change the drone's flight route if an abnormality is detected. For example, if an abnormality is detected, the adjustment unit may deploy additional security guards, adjust the field of view of the surveillance cameras, and change the drone's flight route. This enables the security optimization system according to the embodiment to prevent problems and respond quickly through data collection, analysis, and deployment and adjustment of security resources.

[0070] The collection unit can collect past crime occurrences, the number of event participants, the type of event, weather data, and other related data. For example, the collection unit acquires past crime occurrences from a database. The collection unit can also acquire the number of event participants from the event organizer. The collection unit can also acquire weather data from a weather database. For example, the collection unit acquires past crime occurrences from a database, acquires the number of event participants from the event organizer, and acquires weather data from the weather database. This collection of diverse data improves the accuracy of trouble predictions. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can acquire past crime occurrences from a database and input them into the generation AI, which then analyzes and collects the data.

[0071] The analysis unit can analyze the collected data using machine learning and predict the occurrence of trouble. The analysis unit can analyze the data using, for example, deep learning and predict the occurrence of trouble. The analysis unit can also analyze the data using a support vector machine. The analysis unit can also analyze the data using a random forest. For example, the analysis unit can analyze the data using deep learning and predict the occurrence of trouble. In this way, the use of machine learning improves the accuracy of the prediction of the occurrence of trouble. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can analyze the data and predict the occurrence of trouble.

[0072] The placement unit can place security guards at locations where predicted trouble will occur. For example, the placement unit places security guards at locations where predicted trouble will occur. The placement unit can also install surveillance cameras at locations where predicted trouble will occur. The placement unit can also place drones at locations where predicted trouble will occur. For example, the placement unit places security guards at locations where predicted trouble will occur, installs surveillance cameras, and places drones. By placing security guards at locations where predicted trouble will occur, trouble can be prevented before it occurs. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the placement unit can input predicted locations where trouble will occur into a generation AI, which can then optimize the placement of security guards.

[0073] The placement unit can install a surveillance camera at a location where a predicted trouble will occur. For example, the placement unit installs a surveillance camera at a location where a predicted trouble will occur. The placement unit can also deploy a security guard at a location where a predicted trouble will occur. The placement unit can also deploy a drone at a location where a predicted trouble will occur. For example, the placement unit installs a surveillance camera at a location where a predicted trouble will occur, deploys a security guard, and deploys a drone. By installing the surveillance camera, it becomes easier to monitor the occurrence of a trouble. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the placement unit can input a predicted location where a trouble will occur into the generation AI, and the generation AI can optimize the installation of the surveillance camera.

[0074] The adjustment unit monitors the deployment status of security resources in real time and can deploy additional security guards when an abnormality is detected. For example, the adjustment unit deploys additional security guards when an abnormality is detected. The adjustment unit can also adjust the field of view of a surveillance camera when an abnormality is detected. The adjustment unit can also change the flight route of a drone when an abnormality is detected. For example, the adjustment unit deploys additional security guards, adjusts the field of view of a surveillance camera, and changes the flight route of a drone when an abnormality is detected. This enables real-time monitoring and a quick response when an abnormality is detected. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input response procedures when an abnormality is detected into the generation AI, and the generation AI can optimize the adjustment of security resources.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user feels anxious, the collection unit causes the generation AI to immediately start data collection and quickly perform analysis. Furthermore, if the user feels relaxed, the collection unit can cause the generation AI to periodically collect data and perform analysis as needed. Furthermore, if the user feels excited, the collection unit can cause the generation AI to frequently collect data and perform analysis in real time. For example, if the user feels anxious, the collection unit causes the generation AI to immediately start data collection and quickly perform analysis. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which then adjusts the timing of data collection.

[0076] The collection unit can detect signs of trouble by collecting social media posts in addition to past crime occurrence status and event data. For example, the collection unit can analyze social media posts to detect suspicious activity in a specific area. The collection unit can also collect social media posts from event participants to detect signs of trouble early. The collection unit can also analyze social media hashtags to detect signs of trouble related to a specific event. For example, the collection unit can analyze social media posts to detect suspicious activity in a specific area. In this way, by collecting social media posts, signs of trouble can be detected early. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input social media posts into a generation AI, which can detect signs of trouble.

[0077] When collecting data, the collection unit can collect detailed data by focusing on a specific area or time period. For example, the collection unit collects detailed information on crime occurrences in a specific area at night. The collection unit can also collect data during a specific time period when a large-scale event is being held. The collection unit can also collect data on weekends in a specific area to predict the occurrence of trouble. For example, the collection unit collects detailed information on crime occurrences in a specific area at night. This enables more detailed data collection by focusing on a specific area or time period. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input data on a specific area or time period into the generation AI, which then collects detailed data.

[0078] The collection unit can analyze audio and video data during data collection to detect abnormal behavior and audio. The collection unit can, for example, analyze video data from a surveillance camera to detect abnormal behavior. The collection unit can also analyze audio data to detect abnormal audio and noise. The collection unit can also combine and analyze video data and audio data to detect signs of trouble. For example, the collection unit analyzes video data from a surveillance camera to detect abnormal behavior. In this way, abnormal behavior and audio can be detected by analyzing the audio and video data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the collection unit can input audio and video data to a generation AI, which can detect abnormal behavior and audio.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit causes the generation AI to prioritize collecting data on crime occurrence situations. Furthermore, if the user is feeling relaxed, the collection unit can also cause the generation AI to prioritize collecting event data. Furthermore, if the user is excited, the collection unit can cause the generation AI to prioritize collecting social media posts. For example, if the user is feeling anxious, the collection unit causes the generation AI to prioritize collecting data on crime occurrence situations. This enables more appropriate data collection by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then prioritize the data.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit can prioritize collecting crime occurrence status in a specific area. The collection unit can also prioritize collecting data around an event venue. The collection unit can also prioritize collecting social media posts in a specific area. For example, the collection unit prioritizes collecting crime occurrence status in a specific area. In this way, highly relevant data can be collected preferentially by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input geographical location information into the generation AI, which can then prioritize collecting highly relevant data.

[0081] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the content of the user's social media posts and collect related data. The collection unit can also analyze the activities of the user's social media friends and collect related data. The collection unit can also analyze the user's social media hashtags and collect related data. For example, the collection unit can analyze the content of the user's social media posts and collect related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's social media activities into the generation AI, which can collect related data.

[0082] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit, for example, improves the collection method based on past data collection results. The collection unit can also customize the collection method by reflecting user feedback. The collection unit can also optimize the collection method by referring to past trouble occurrence situations. For example, the collection unit improves the collection method based on past data collection results. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input past feedback into the generation AI, which can customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible analysis result. This allows for adjusting the presentation method of the analysis according to the user's emotions to provide more appropriate analysis results. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also allocate analysis resources according to the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to crime occurrence data. The analysis unit can also apply a different analysis algorithm to event data. The analysis unit can also apply a dedicated analysis algorithm to social media posts. For example, the analysis unit applies a specific analysis algorithm to crime occurrence data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data category into the generation AI, which then applies an appropriate analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing past analysis results as feedback. For example, the analysis unit improves the analysis algorithm based on past analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is feeling relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling excited, the analysis unit can provide a visually stimulating analysis result. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the length of the analysis.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also allocate analysis resources based on the time of submission. For example, the analysis unit prioritizes analysis of the most recent data. This enables efficient analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI, and the generation AI can determine the priority of analysis.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also allocate analysis resources based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI, which can then adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple terms to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with extensive expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can provide analysis results in simple terms to a user with little expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0091] The allocation unit can estimate the user's emotions and adjust the allocation method of security resources based on the estimated user's emotions. For example, if the user is feeling anxious, the allocation unit can prioritize the allocation of security resources. Furthermore, if the user is relaxed, the allocation unit can also allocate security resources normally. Furthermore, if the user is excited, the allocation unit can allocate security resources over a wider area. For example, if the user is feeling anxious, the allocation unit prioritizes the allocation of security resources. This allows for more appropriate security by adjusting the allocation method of security resources according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 allocation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the allocation unit can input the user's emotion data into the generation AI, which then adjusts the allocation method of security resources.

[0092] During deployment, the deployment unit can create a detailed deployment plan for security resources according to the location and time period of the predicted trouble occurrence. The deployment unit, for example, deploys security guards to the location where the predicted trouble will occur. The deployment unit can also concentrate security resources in the time period when the predicted trouble will occur. The deployment unit can also create a deployment plan for security resources according to the location and time period of the predicted trouble occurrence. For example, the deployment unit deploys security guards to the location where the predicted trouble will occur. This enables efficient security by creating a detailed deployment plan for security resources according to the location and time period of the predicted trouble occurrence. Some or all of the above-mentioned processing in the deployment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input data on the location and time period of the predicted trouble occurrence into the generation AI, which can then create a detailed deployment plan.

[0093] The deployment unit can optimally deploy the security resources according to the type of security resource at the time of deployment. For example, the deployment unit deploys security guards at a location where a predicted trouble will occur. The deployment unit can also install surveillance cameras at a location where a predicted trouble will occur. The deployment unit can also deploy drones at a location where a predicted trouble will occur. For example, the deployment unit deploys security guards at a location where a predicted trouble will occur. This enables efficient security by optimally deploying the security resources according to the type of security resource. Some or all of the above-described processing in the deployment unit may be performed using, or without using, a generation AI. For example, the deployment unit can input the type of security resource into the generation AI, which can then perform the optimal deployment.

[0094] The deployment unit can improve the accuracy of deployment by referring to past deployment results when deploying. The deployment unit, for example, improves the deployment method of security resources based on past deployment results. The deployment unit can also improve the deployment accuracy of security resources by referring to past deployment results. The deployment unit can also use past deployment results as feedback to improve the deployment accuracy of security resources. For example, the deployment unit improves the deployment method of security resources based on past deployment results. In this way, by referring to past deployment results, the accuracy of deployment is improved. Some or all of the above-mentioned processing in the deployment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input past deployment results into the generation AI, which can improve the deployment accuracy.

[0095] The allocation unit can estimate the user's emotions and determine the allocation priority of security resources based on the estimated user's emotions. For example, if the user is feeling anxious, the allocation unit can prioritize the allocation of security resources. Furthermore, if the user is relaxed, the allocation unit can also allocate security resources normally. Furthermore, if the user is excited, the allocation unit can allocate security resources over a wider area. For example, if the user is feeling anxious, the allocation unit prioritizes the allocation of security resources. This enables more appropriate security by determining the allocation priority of security resources according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the allocation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the allocation unit can input the user's emotion data into the generation AI, which then determines the allocation priority of security resources.

[0096] During deployment, the deployment unit can prioritize deploying security resources to highly relevant locations by taking geographical location information into consideration. For example, the deployment unit deploys security resources by taking into consideration the crime occurrence situation in a specific area. The deployment unit can also prioritize deploying security resources around an event venue. The deployment unit can also deploy security resources by taking into consideration the content of social media posts in a specific area. For example, the deployment unit deploys security resources by taking into consideration the crime occurrence situation in a specific area. In this way, by taking geographical location information into consideration, security resources can be prioritized to be deployed to highly relevant locations. Some or all of the above-described processing in the deployment unit may be performed using, or without, a generation AI. For example, the deployment unit can input geographical location information to the generation AI, and the generation AI can prioritize deploying security resources to highly relevant locations.

[0097] During deployment, the deployment unit can analyze the content of social media posts and deploy security resources to related locations. For example, the deployment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and deploy security resources. The deployment unit can also collect social media posts from event participants and deploy security resources to locations where there are signs of trouble. The deployment unit can also analyze social media hashtags and deploy security resources to locations where there are signs of trouble related to a specific event. For example, the deployment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and deploy security resources. In this way, security resources can be deployed to related locations by analyzing the content of social media posts. Some or all of the above-mentioned processing in the deployment unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input the content of social media posts into a generation AI, and the generation AI can deploy security resources to related locations.

[0098] The deployment unit can customize the deployment method by reflecting past feedback at the time of deployment. The deployment unit can improve the deployment method of security resources based on, for example, past deployment results. The deployment unit can also customize the deployment method of security resources by reflecting user feedback. The deployment unit can also optimize the deployment method of security resources by referring to past trouble occurrence situations. For example, the deployment unit improves the deployment method of security resources based on past deployment results. In this way, the deployment method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the deployment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the deployment unit can input past feedback into the generation AI, which can customize the deployment method.

[0099] The adjustment unit can estimate the user's emotions and adjust the method of adjusting security resources based on the estimated user's emotions. For example, if the user is feeling anxious, the adjustment unit can focus on adjusting security resources. Furthermore, if the user is relaxed, the adjustment unit can also adjust security resources normally. Furthermore, if the user is excited, the adjustment unit can adjust security resources more broadly. For example, if the user is feeling anxious, the adjustment unit can focus on adjusting security resources. This allows for more appropriate security by adjusting the method of adjusting security resources according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the adjustment unit can input the user's emotion data into the generation AI, which can then adjust the method of adjusting security resources.

[0100] During adjustment, the adjustment unit can analyze data collected in real time and optimize the allocation of security resources. The adjustment unit, for example, optimizes the allocation of security resources based on data collected in real time. The adjustment unit can also analyze data collected in real time and create a security resource allocation plan. The adjustment unit can also improve the security resource allocation method based on data collected in real time. For example, the adjustment unit optimizes the allocation of security resources based on data collected in real time. In this way, the allocation of security resources can be optimized by analyzing the data collected in real time. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input data collected in real time to a generation AI, which can optimize the allocation of security resources.

[0101] During adjustment, the adjustment unit can set detailed response procedures to be taken when an abnormality is detected. For example, the adjustment unit sets detailed response procedures to be taken when an abnormality is detected. The adjustment unit can also adjust security resources based on the response procedures to be taken when an abnormality is detected. The adjustment unit can also use the response procedures to be taken when an abnormality is detected as feedback to improve the method of adjusting security resources. For example, the adjustment unit sets detailed response procedures to be taken when an abnormality is detected. In this way, setting detailed response procedures to be taken when an abnormality is detected enables a rapid response. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input response procedures to be taken when an abnormality is detected into the generation AI, and the generation AI can set the response procedures in detail.

[0102] When making adjustments, the adjustment unit can improve the accuracy of the adjustment by referring to past adjustment results. The adjustment unit, for example, improves the method for adjusting security resources based on past adjustment results. The adjustment unit can also improve the accuracy of adjusting security resources by referring to past adjustment results. The adjustment unit can also improve the accuracy of adjusting security resources by utilizing past adjustment results as feedback. For example, the adjustment unit improves the method for adjusting security resources based on past adjustment results. In this way, by referring to past adjustment results, the accuracy of adjustment is improved. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input past adjustment results into the generation AI, which can improve the accuracy of the adjustment.

[0103] The adjustment unit can estimate the user's emotions and determine the priority of security resource adjustment based on the estimated user's emotions. For example, if the user is feeling anxious, the adjustment unit can prioritize security resources. Furthermore, if the user is relaxed, the adjustment unit can also adjust security resources normally. Furthermore, if the user is excited, the adjustment unit can adjust security resources more broadly. For example, if the user is feeling anxious, the adjustment unit can prioritize security resources. This enables more appropriate security by determining the priority of security resource adjustment according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, the generation AI. For example, the adjustment unit can input the user's emotion data into the generation AI, which can then determine the priority of security resource adjustment.

[0104] During adjustment, the adjustment unit can prioritize security resources to highly relevant locations by taking geographical location information into consideration. The adjustment unit can adjust security resources by taking into consideration, for example, the crime occurrence situation in a specific area. The adjustment unit can also prioritize security resources around an event venue. The adjustment unit can also adjust security resources by taking into consideration the content of social media posts in a specific area. For example, the adjustment unit adjusts security resources by taking into consideration the crime occurrence situation in a specific area. In this way, by taking geographical location information into consideration, security resources can be prioritized to highly relevant locations. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input geographical location information to the generation AI, which can then prioritize security resources to highly relevant locations.

[0105] During adjustment, the adjustment unit can analyze the content of social media posts and adjust security resources to relevant locations. For example, the adjustment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and adjust security resources accordingly. The adjustment unit can also collect social media posts from event participants and adjust security resources to locations where there are signs of trouble. The adjustment unit can also analyze social media hashtags and adjust security resources to locations where there are signs of trouble related to a specific event. For example, the adjustment unit can analyze the content of social media posts, detect suspicious activity in a specific area, and adjust security resources accordingly. In this way, security resources can be adjusted to relevant locations by analyzing the content of social media posts. Some or all of the above-described processing in the adjustment unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input the content of social media posts into a generation AI, which can then adjust security resources to relevant locations.

[0106] During adjustment, the adjustment unit can customize the adjustment method by reflecting past feedback. The adjustment unit, for example, improves the security resource adjustment method based on past adjustment results. The adjustment unit can also customize the security resource adjustment method by reflecting user feedback. The adjustment unit can also optimize the security resource adjustment method by referring to past trouble occurrence situations. For example, the adjustment unit improves the security resource adjustment method based on past adjustment results. In this way, the adjustment method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the adjustment unit can input past feedback into the generation AI, which can customize the adjustment method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, deployment unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 38B of the smart device 14. The collection unit can also acquire past crime occurrences from the database 24 by the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze data using a generation AI by the specific processing unit 290 of the data processing device 12 and predict the occurrence of trouble. For example, the deployment unit can deploy security resources to the location of the predicted trouble occurrence by the control unit 46A of the smart device 14. For example, the adjustment unit can monitor the deployment status of security resources in real time by the control unit 46A of the smart device 14 and adjust security force as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, deployment unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also acquire past crime occurrences from the database 24 via the specific processing unit 290 of the data processing device 12. The analysis unit can, for example, analyze data using a generation AI via the specific processing unit 290 of the data processing device 12 to predict the occurrence of trouble. The deployment unit can, for example, deploy security resources to the location of the predicted trouble occurrence via the control unit 46A of the smart glasses 214. The adjustment unit can, for example, monitor the deployment status of security resources in real time via the control unit 46A of the smart glasses 214 and adjust security forces as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, deployment unit, and adjustment unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also acquire past crime occurrences from the database 24 by the specific processing unit 290 of the data processing device 12. The analysis unit, for example, analyzes data using a generation AI by the specific processing unit 290 of the data processing device 12 and predicts the occurrence of trouble. For example, the deployment unit can deploy security resources to the location of the predicted trouble occurrence by the control unit 46A of the headset-type terminal 314. For example, the adjustment unit can monitor the deployment status of security resources in real time by the control unit 46A of the headset-type terminal 314 and adjust security forces as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, deployment unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data using the camera 42 and microphone 238 of the robot 414. The collection unit can also acquire past crime occurrences from the database 24 by the specific processing unit 290 of the data processing device 12. The analysis unit can, for example, analyze data using a generation AI by the specific processing unit 290 of the data processing device 12 and predict the occurrence of trouble. The deployment unit can, for example, deploy security resources to the location of the predicted trouble occurrence by the control unit 46A of the robot 414. The adjustment unit can, for example, monitor the deployment status of security resources in real time by the control unit 46A of the robot 414 and adjust security forces as necessary.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing data with high urgency. Alternatively, if the user is relaxed, the analysis unit can analyze data according to the normal analysis order. Alternatively, if the user is excited, the analysis unit can prioritize analyzing visually stimulating data. This allows for adjusting the analysis priority according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then determine the analysis priority.

[0109] When collecting data, the collection unit can analyze the user's behavioral patterns and determine the optimal timing for data collection. For example, data collection can be concentrated on times when the user is frequently moving. It can also collect detailed data during times when the user is stationary. If a user stays in a specific location for a long time, it can prioritize the collection of data related to that location. This enables more effective data collection by optimizing the timing of data collection based on the user's behavioral patterns. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's behavioral data into a generation AI, which can then determine the optimal timing for data collection.

[0110] The allocation unit can estimate the user's emotions and adjust the allocation method of security resources based on the estimated user's emotions. For example, if the user is feeling anxious, security resources can be allocated in a focused manner. If the user is relaxed, security resources can be allocated normally. If the user is excited, security resources can be allocated over a wider area. This allows for more appropriate security by adjusting the allocation method of security resources according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the allocation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the allocation unit can input the user's emotion data into the generation AI, which can then adjust the allocation method of security resources.

[0111] The adjustment unit can estimate the user's emotions and adjust the method of adjusting security resources based on the estimated user emotions. For example, if the user is feeling anxious, the adjustment of security resources can be focused. If the user is relaxed, the adjustment of security resources can be normal. If the user is excited, the adjustment of security resources can be broadened. This allows for more appropriate security by adjusting the method of adjusting security resources according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the adjustment unit can input the user's emotion data into the generation AI, which can then adjust the method of adjusting security resources.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling anxious, a simple and highly visible analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Also, if the user is excited, a visually stimulating analysis result can be provided. By adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0113] When collecting data, the collection unit can collect detailed data by focusing on specific areas or time periods. For example, the collection unit can collect detailed information on crime occurrences in a specific area at night. The collection unit can also collect data during specific time periods when a large-scale event is being held. The collection unit can also collect data on weekends in a specific area to predict the occurrence of trouble. This enables more detailed data collection by focusing on specific areas or time periods. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input data on a specific area or time period into the generation AI, which can then collect detailed data.

[0114] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A simplified analysis can also be performed on data with low importance. The analysis unit can also allocate analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0115] During deployment, the deployment unit can prioritize deploying security resources to highly relevant locations by taking geographical location information into consideration. For example, the deployment unit can deploy security resources by taking into consideration the crime occurrence situation in a specific area. The deployment unit can also prioritize deploying security resources around an event venue. The deployment unit can also deploy security resources by taking into consideration the content of social media posts in a specific area. In this way, by taking geographical location information into consideration, security resources can be prioritized to highly relevant locations. Some or all of the above-mentioned processing in the deployment unit may be performed using, or without, a generation AI. For example, the deployment unit can input geographical location information into the generation AI, and the generation AI can prioritize deploying security resources to highly relevant locations.

[0116] During adjustment, the adjustment unit can analyze data collected in real time and optimize the allocation of security resources. For example, the adjustment unit optimizes the allocation of security resources based on data collected in real time. The adjustment unit can also analyze data collected in real time and create a security resource allocation plan. The adjustment unit can also improve the security resource allocation method based on data collected in real time. In this way, the allocation of security resources can be optimized by analyzing data collected in real time. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, a generation AI, for example. For example, the adjustment unit can input data collected in real time into a generation AI, which can then optimize the allocation of security resources.

[0117] During adjustment, the adjustment unit can set detailed response procedures to be performed when an abnormality is detected. For example, the adjustment unit sets detailed response procedures to be performed when an abnormality is detected. The adjustment unit can also adjust security resources based on the response procedures to be performed when an abnormality is detected. The adjustment unit can also use the response procedures to be performed when an abnormality is detected as feedback to improve the method of adjusting security resources. In this way, setting detailed response procedures to be performed when an abnormality is detected enables a rapid response. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the adjustment unit can input response procedures to be performed when an abnormality is detected into the generation AI, and the generation AI can set the response procedures in detail.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects data. The data includes past crime occurrences, the number of event participants, the type of event, weather data, etc. For example, the collection unit obtains past crime occurrences from a database, obtains the number of event participants from the event organizer, and obtains weather data from a weather database. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the occurrence of problems. The analysis is performed using generative AI and machine learning algorithms (e.g., deep learning, support vector machines, random forests). Step 3: The deployment unit deploys security resources according to the location and time of the trouble predicted by the analysis unit. Security resources include security guards, surveillance cameras, drones, etc. For example, security guards are deployed, surveillance cameras are installed, and drones are deployed in the location where the trouble is predicted to occur. Step 4: The coordination unit monitors the deployment status of the security resources deployed by the deployment unit in real time and adjusts the security force as necessary. Adjustments are made when an abnormality is detected, and include, for example, deploying additional security guards, adjusting the field of view of surveillance cameras, and changing the flight route of drones.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit and predicts the occurrence of a problem; a deployment unit that deploys security resources according to the location and time period of the occurrence of the trouble predicted by the analysis unit; and an adjustment unit that monitors in real time the deployment status of the security resources deployed by the deployment unit and adjusts the security force as necessary. A system characterized by:

2. The collecting unit Collect historical crime occurrences, event attendance, event type, weather data and other relevant data 2. The system of claim 1.

3. The analysis unit Analyzing collected data using machine learning to predict the occurrence of problems 2. The system of claim 1.

4. The placement unit Deploy security guards in predicted trouble spots 2. The system of claim 1.

5. The placement unit Installing surveillance cameras in predicted trouble locations 2. The system of claim 1.

6. The adjustment unit Real-time monitoring of security resource deployment status and deployment of additional security personnel if an abnormality is detected 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Collects social media posts, along with historical crime and event data, to detect signs of trouble 2. The system of claim 1.

Images