System

Abstract

An object of a system according to an embodiment is to optimize crime prevention measures on the basis of regional characteristics and past crime data.SOLUTION: A system includes an analysis part, a provision part, a proposal part, a patrol part, and a detection part. The analysis unit optimizes crime prevention measures on the basis of past crime data and regional characteristics. The provision unit provides the risk information obtained by the analysis unit. The proposal unit proposes a specific crime prevention measure based on the risk information provided by the provision unit. The patrol unit patrols the risk area. The detection unit detects an abnormality in the area patrolled by the patrol unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately implement crime prevention measures based on local characteristics and past crime data, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize crime prevention measures based on local characteristics and past crime data. [Means for solving the problem]

[0006] The system according to the embodiment comprises an analysis unit, a provision unit, a proposal unit, a patrol unit, and a detection unit. The analysis unit optimizes crime prevention measures based on past crime data and local characteristics. The provision unit provides risk information obtained by the analysis unit. The proposal unit proposes specific crime prevention measures based on the risk information provided by the provision unit. The patrol unit patrols risk areas. The detection unit detects abnormalities in the areas patrolled by the patrol unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize crime prevention measures based on local characteristics and past crime data. [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 crime prevention system according to an embodiment of the present invention utilizes AI to raise crime prevention awareness and propose specific crime prevention measures. This crime prevention system optimizes crime prevention measures by taking into account past crime data and local characteristics, provides risk information, proposes specific crime prevention measures, patrols risk areas, and detects abnormalities. For example, the crime prevention system optimizes crime prevention measures by taking into account past crime data and local characteristics. Next, the crime prevention system provides risk information. For example, if a particular area experiences a high rate of crime, the system provides this information to residents and alerts them. Furthermore, the crime prevention system proposes specific crime prevention measures to residents based on the risk information. For example, the system suggests measures such as refraining from going out at night or installing security cameras. Furthermore, the crime prevention system uses a robot-type security system to strengthen crime prevention in risk areas. For example, the security system patrols risk areas and detects abnormalities. For example, if a suspicious person is detected, the system responds by sounding an alarm. This allows the crime prevention system to realize a safer society and develop businesses that address a variety of crime prevention targets. This allows the crime prevention system to realize a safer society and develop businesses that address a variety of crime prevention targets. For example, it can provide crime prevention measures in a variety of locations, including residential areas, commercial facilities, public facilities, etc. Furthermore, by combining AI with a robotic security system, it is possible to realize efficient and effective crime prevention measures.

[0029] A crime prevention system according to an embodiment includes an analysis unit, a provision unit, a proposal unit, a patrol unit, and a detection unit. The analysis unit optimizes crime prevention measures based on past crime data and local characteristics. Past crime data includes, for example, the type of crime, the date and time of the crime, and the location. Local characteristics include, for example, population density, public safety, and geographical conditions. The analysis unit, for example, analyzes past crime data and evaluates the crime risk in a specific local area. The analysis unit can also optimize crime prevention measures taking local characteristics into account. The provision unit provides risk information obtained by the analysis unit. The risk information includes, for example, the type of risk, the risk level, and a provision method. For example, if there is a high incidence of crime in a specific local area, the provision unit provides that information to residents to alert them. The provision unit can also propose specific crime prevention measures to residents based on the risk information. The proposal unit proposes specific crime prevention measures based on the risk information provided by the provision unit. Specific crime prevention measures include, for example, installing security cameras, deploying security guards, and introducing an alarm system. For example, the suggestion unit suggests to residents to refrain from going out at night based on the risk information. The suggestion unit can also suggest installing security cameras. The patrol unit patrols the risk area. Patrols include, for example, patrol frequency, patrol route, patrol time, etc. The patrol unit, for example, periodically patrols the risk area and detects abnormalities. The detection unit detects abnormalities in the area patrolled by the patrol unit. Abnormalities include, for example, detection of a suspicious person or abnormal behavior. The detection unit, for example, issues an alarm when a suspicious person is discovered. The detection unit can also issue an alarm when abnormal behavior is detected. This allows the crime prevention system according to the embodiment to realize a safer society. For example, the crime prevention system can optimize crime prevention measures taking into account past crime data and regional characteristics, provide risk information, suggest specific crime prevention measures, patrol the risk area, and detect abnormalities, thereby realizing a safer society.

[0030] The analysis unit can optimize crime prevention measures based on weather and event information in addition to past crime data. For example, the analysis unit uses AI to analyze weather data and strengthen indoor crime prevention measures when it rains. The analysis unit can also analyze local event information and increase security during large-scale events. The analysis unit can also combine past crime data and weather data to predict crime risks under specific weather conditions and adjust crime prevention measures. This allows for more accurate crime prevention measures to be implemented by taking weather and event information into consideration. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input weather data and event information into the generation AI and have the generation AI optimize the crime prevention measures.

[0031] The analysis unit can apply different crime prevention algorithms based on the characteristics of the area. For example, the analysis unit can use AI to analyze the characteristics of urban and suburban areas and propose crime prevention measures appropriate for each. The analysis unit can also use AI to analyze the characteristics of residential and commercial areas and apply different crime prevention measures. The analysis unit can also use AI to analyze crime trends in the area and strengthen crime prevention measures against specific crimes. This allows for more effective crime prevention measures to be provided by providing crime prevention measures tailored to the characteristics of the area. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input data on the characteristics of the area into the generation AI and have the generation AI apply the crime prevention algorithm.

[0032] When analyzing crime data, the analysis unit can use different analysis methods for each type of crime. For example, the analysis unit can have AI analyze data on theft and violent crimes separately and propose appropriate crime prevention measures for each. The analysis unit can also have AI analyze data on cybercrime and physical crime separately and apply different crime prevention measures. The analysis unit can also have AI use different analysis algorithms for each type of crime to propose highly accurate crime prevention measures. This allows for providing crime prevention measures appropriate for each type of crime, thereby achieving more accurate crime prevention measures. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input crime data into the generation AI and have the generation AI perform an analysis for each type of crime.

[0033] When providing risk information, the providing unit can provide the information at an appropriate time, taking into account the user's location information. For example, the providing unit uses an AI to analyze the user's location information and send a notification when the user approaches a risk area. The providing unit can also analyze the user's location information and refrain from sending a notification if the user is in a safe area. The providing unit can also monitor the user's location information in real time and provide risk information at the optimal time. This enables more effective crime prevention measures to be realized by providing risk information based on the user's location information. Some or all of the above-described processing in the providing unit may be performed using an AI, for example, or may be performed without using an AI. For example, the providing unit can input the user's location information to the generation AI and cause the generation AI to provide the risk information.

[0034] When providing risk information, the providing unit can provide customized information by referring to the user's past behavioral history. For example, the providing unit can have an AI analyze the user's past behavioral history and send a notification when the user approaches a risk area. The providing unit can also have an AI analyze the user's past behavioral history and suggest countermeasures for specific risks. The providing unit can also provide customized risk information based on the user's past behavioral history. This allows for more effective crime prevention measures to be realized by providing customized risk information based on the user's past behavioral history. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's past behavioral history data into the generation AI and cause the generation AI to provide customized risk information.

[0035] When providing risk information, the providing unit can determine the priority of notifications based on the importance of the information. For example, the providing unit can use AI to analyze the importance of risk information and prioritize notifying high-risk information. The providing unit can also use AI to analyze the importance of risk information and postpone low-risk information. The providing unit can also use AI to determine the optimal priority of notifications based on the importance of risk information. In this way, important risk information can be provided preferentially by determining the priority of notifications based on the importance of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input importance data of risk information to the generating AI and have the generating AI determine the priority of notifications.

[0036] When proposing crime prevention measures, the suggestion unit can make suggestions at appropriate times, taking into account the user's lifestyle rhythm. For example, the suggestion unit uses AI to analyze the user's lifestyle rhythm and suggest crime prevention measures at optimal times. The suggestion unit can also use AI to analyze the user's lifestyle rhythm and strengthen crime prevention measures at night. The suggestion unit can also use AI to suggest crime prevention measures at optimal times based on the user's lifestyle rhythm. This makes it possible to realize more effective crime prevention measures by suggesting crime prevention measures based on the user's lifestyle rhythm. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle rhythm data into the generation AI and have the generation AI execute the crime prevention measure suggestions.

[0037] When proposing a crime prevention measure, the suggestion unit can make a customized suggestion by referring to the user's past crime prevention measure history. For example, the suggestion unit uses an AI to analyze the user's past crime prevention measure history and suggest optimal crime prevention measures. The suggestion unit can also use an AI to analyze the user's past crime prevention measure history and suggest measures for specific risks. The suggestion unit can also use an AI to suggest customized crime prevention measures based on the user's past crime prevention measure history. This allows for more effective crime prevention measures to be realized by proposing customized crime prevention measures based on the user's past crime prevention measure history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's past crime prevention measure history data into the generation AI and have the generation AI execute the customized crime prevention measure proposal.

[0038] When proposing a crime prevention measure, the proposal unit can evaluate the effectiveness of the proposal and select an appropriate crime prevention measure. For example, the proposal unit uses AI to analyze the effectiveness of past crime prevention measures and propose the most effective crime prevention measure. The proposal unit can also use AI to evaluate the effectiveness of the proposal in real time and adjust the crime prevention measure as necessary. The proposal unit can also use AI to select and propose the optimal crime prevention measure based on data on past crime prevention measures. This makes it possible to provide more effective crime prevention measures by evaluating the effectiveness of the proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on past crime prevention measures into the generation AI and have the generation AI evaluate the effectiveness of the crime prevention measures.

[0039] When patrolling, the patrol unit can select an appropriate patrol route by referring to past patrol data. For example, the patrol unit uses AI to analyze past patrol data and select the most effective patrol route. The patrol unit can also analyze past patrol data and prioritize patrol of risk areas. The patrol unit can also use AI to propose an optimal patrol route based on past patrol data. In this way, more effective patrol can be achieved by selecting an optimal patrol route based on past patrol data. Some or all of the above-mentioned processing in the patrol unit may be performed using AI, for example, or may be performed without using AI. For example, the patrol unit can input past patrol data into a generation AI and have the generation AI select a patrol route.

[0040] The patrol unit can optimize the patrol route based on weather and traffic conditions during patrol. For example, the patrol unit uses AI to analyze weather data and prioritize indoor patrol routes in bad weather. The patrol unit can also analyze traffic conditions and propose patrol routes that avoid congestion. The patrol unit can also combine weather and traffic conditions to select the optimal patrol route. This enables more effective patrol by optimizing the patrol route based on weather and traffic conditions. Some or all of the above-mentioned processing in the patrol unit may be performed using AI, for example, or may be performed without using AI. For example, the patrol unit can input weather data and traffic condition data into the generation AI and have the generation AI optimize the patrol route.

[0041] The patrol unit can apply different patrol algorithms based on the characteristics of the patrol area during patrol. For example, the patrol unit can use AI to analyze the characteristics of urban and suburban areas and apply patrol algorithms appropriate for each. The patrol unit can also use AI to analyze the characteristics of residential and commercial areas and apply different patrol algorithms. The patrol unit can also select the optimal patrol algorithm based on the characteristics of the patrol area. In this way, more effective patrol can be achieved by applying different patrol algorithms based on the characteristics of the patrol area. Some or all of the above-mentioned processing in the patrol unit can be performed using AI, for example, or without AI. For example, the patrol unit can input characteristic data of the patrol area to the generation AI and have the generation AI apply the patrol algorithm.

[0042] When detecting an anomaly, the detection unit can optimize the detection algorithm by referring to past detection data. For example, the detection unit uses AI to analyze past detection data and select the most effective detection algorithm. The detection unit can also analyze past detection data and adjust the detection algorithm for a specific risk. The detection unit can also propose an optimal detection algorithm based on past detection data. In this way, more effective anomaly detection can be achieved by optimizing the detection algorithm based on past detection data. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past detection data to a generation AI and have the generation AI optimize the detection algorithm.

[0043] When detecting an anomaly, the detection unit can apply different detection methods based on the characteristics of the area. For example, the detection unit uses AI to analyze the characteristics of urban and suburban areas and apply appropriate detection methods for each. The detection unit can also use AI to analyze the characteristics of residential and commercial areas and apply different detection methods. The detection unit can also select the optimal detection method based on the characteristics of the area. In this way, more effective anomaly detection can be achieved by applying different detection methods based on the characteristics of the area. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input area characteristic data to the generation AI and have the generation AI apply the detection method.

[0044] The detection unit can integrate multiple sensor information to improve detection accuracy when detecting an anomaly. For example, the detection unit uses AI to analyze multiple sensor information and improve the accuracy of anomaly detection. The detection unit can also integrate different types of sensor information and improve the accuracy of anomaly detection. The detection unit can also analyze sensor information in real time and improve the accuracy of anomaly detection. In this way, the accuracy of anomaly detection can be improved by integrating multiple sensor information. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input multiple sensor information to a generation AI and have the generation AI integrate the sensor information.

[0045] When detecting an anomaly, the detection unit can optimize the detection algorithm based on local crime trends. For example, the detection unit uses AI to analyze local crime trends and select the most effective detection algorithm. The detection unit can also use AI to analyze local crime trends and adjust the detection algorithm for specific risks. The detection unit can also use AI to propose the optimal detection algorithm based on local crime trends. This enables more effective anomaly detection by optimizing the detection algorithm based on local crime trends. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input local crime trend data into the generation AI and have the generation AI optimize the detection algorithm.

[0046] When an anomaly is detected, the detection unit can analyze social media posts to obtain real-time risk information. For example, the detection unit uses AI to analyze social media posts and detect crime risks in real time. The detection unit can also use AI to monitor specific keywords and issue a notification when crime risks increase. The detection unit can also use AI to analyze social media posts and predict crime risks in specific areas. In this way, by analyzing social media posts, real-time risk information can be obtained and the accuracy of anomaly detection can be improved. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input social media post data to a generation AI and cause the generation AI to obtain real-time risk information.

[0047] When detecting an anomaly, the detection unit can evaluate the effectiveness of past detection data and select an appropriate detection method. For example, the detection unit uses AI to analyze past detection data and select the most effective detection method. The detection unit can also analyze past detection data and adjust the detection method for a specific risk. The detection unit can also use AI to propose the optimal detection method based on past detection data. In this way, by evaluating the effectiveness of past detection data, the optimal detection method can be selected and the accuracy of anomaly detection can be improved. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past detection data to a generation AI and have the generation AI select a detection method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When analyzing crime data, the analysis unit can use different analysis methods depending on the time of day when crimes occur. For example, AI can analyze daytime and nighttime crime data separately and propose appropriate crime prevention measures for each. The analysis unit can also analyze weekend and weekday crime data separately and apply different crime prevention measures. Furthermore, the analysis unit can predict the risk of crimes concentrating during specific time periods and propose crime prevention measures specific to those time periods. This allows for more accurate crime prevention measures to be implemented by providing appropriate crime prevention measures for each time period when crimes occur.

[0050] When providing risk information, the providing unit can provide the information at an appropriate time, taking into account the user's location information. For example, AI can analyze the user's location information and send a notification when the user approaches a risk area. The providing unit can also analyze the user's location information and refrain from sending a notification if the user is in a safe area. Furthermore, the providing unit can monitor the user's location information in real time and provide risk information at the optimal time. This makes it possible to provide risk information based on the user's location information, thereby realizing more effective crime prevention measures.

[0051] When proposing crime prevention measures, the suggestion unit can make suggestions at appropriate times, taking into account the user's lifestyle rhythm. For example, AI can analyze the user's lifestyle rhythm and suggest crime prevention measures at the optimal time. The suggestion unit can also analyze the user's lifestyle rhythm and strengthen crime prevention measures at night. Furthermore, the suggestion unit can also suggest crime prevention measures at the optimal time based on the user's lifestyle rhythm. This makes it possible to realize more effective crime prevention measures by suggesting crime prevention measures based on the user's lifestyle rhythm.

[0052] When patrolling, the patrol unit can optimize its route based on weather and traffic conditions. For example, AI analyzes weather data and prioritizes indoor routes in bad weather. The patrol unit can also analyze traffic conditions and suggest routes that avoid traffic jams. Furthermore, the patrol unit can combine weather and traffic conditions to select the optimal route. This allows for more effective patrol by optimizing the route based on weather and traffic conditions.

[0053] When an anomaly is detected, the detection unit can analyze social media posts to obtain real-time risk information. For example, AI can analyze social media posts and detect crime risks in real time. The detection unit can also use AI to monitor specific keywords and notify users when crime risks increase. Furthermore, the detection unit can also use AI to analyze social media posts and predict crime risks in specific areas. This makes it possible to obtain real-time risk information by analyzing social media posts, improving the accuracy of anomaly detection.

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

[0055] Step 1: The analysis unit optimizes crime prevention measures based on past crime data and local characteristics. Past crime data includes the type of crime, the date and time of the crime, and the location, while local characteristics include population density, security situation, and geographical conditions. The analysis unit analyzes this data to evaluate the crime risk in a specific area and optimize crime prevention measures. Step 2: The provider provides the risk information obtained by the analyzer. The risk information includes the type of risk, risk level, and method of provision. If there are frequent crimes in a specific area, the provider provides this information to residents to warn them. Step 3: The proposal department proposes specific crime prevention measures based on the risk information provided by the provision department. Specific crime prevention measures include installing security cameras, deploying security guards, and introducing alarm systems, and suggests that residents refrain from going out at night based on the risk information. Step 4: The patrol department patrols the risk area. Patrols include the frequency, route, and time of patrol, and the department periodically patrols the risk area to detect abnormalities. Step 5: The detection unit detects abnormalities in the area patrolled by the patrol unit. Abnormalities include the detection of suspicious individuals and abnormal behavior, and if a suspicious individual is found or abnormal behavior is detected, an alarm is issued.

[0056] (Example 2) A crime prevention system according to an embodiment of the present invention utilizes AI to raise crime prevention awareness and propose specific crime prevention measures. This crime prevention system optimizes crime prevention measures by taking into account past crime data and local characteristics, provides risk information, proposes specific crime prevention measures, patrols risk areas, and detects abnormalities. For example, the crime prevention system optimizes crime prevention measures by taking into account past crime data and local characteristics. Next, the crime prevention system provides risk information. For example, if a particular area experiences a high rate of crime, the system provides this information to residents and alerts them. Furthermore, the crime prevention system proposes specific crime prevention measures to residents based on the risk information. For example, the system suggests measures such as refraining from going out at night or installing security cameras. Furthermore, the crime prevention system uses a robot-type security system to strengthen crime prevention in risk areas. For example, the security system patrols risk areas and detects abnormalities. For example, if a suspicious person is detected, the system responds by sounding an alarm. This allows the crime prevention system to realize a safer society and develop businesses that address a variety of crime prevention targets. This allows the crime prevention system to realize a safer society and develop businesses that address a variety of crime prevention targets. For example, it can provide crime prevention measures in a variety of locations, including residential areas, commercial facilities, public facilities, etc. Furthermore, by combining AI with a robotic security system, it is possible to realize efficient and effective crime prevention measures.

[0057] A crime prevention system according to an embodiment includes an analysis unit, a provision unit, a proposal unit, a patrol unit, and a detection unit. The analysis unit optimizes crime prevention measures based on past crime data and local characteristics. Past crime data includes, for example, the type of crime, the date and time of the crime, and the location. Local characteristics include, for example, population density, public safety, and geographical conditions. The analysis unit, for example, analyzes past crime data and evaluates the crime risk in a specific local area. The analysis unit can also optimize crime prevention measures taking local characteristics into account. The provision unit provides risk information obtained by the analysis unit. The risk information includes, for example, the type of risk, the risk level, and a provision method. For example, if there is a high incidence of crime in a specific local area, the provision unit provides that information to residents to alert them. The provision unit can also propose specific crime prevention measures to residents based on the risk information. The proposal unit proposes specific crime prevention measures based on the risk information provided by the provision unit. Specific crime prevention measures include, for example, installing security cameras, deploying security guards, and introducing an alarm system. For example, the suggestion unit suggests to residents to refrain from going out at night based on the risk information. The suggestion unit can also suggest installing security cameras. The patrol unit patrols the risk area. Patrols include, for example, patrol frequency, patrol route, patrol time, etc. The patrol unit, for example, periodically patrols the risk area and detects abnormalities. The detection unit detects abnormalities in the area patrolled by the patrol unit. Abnormalities include, for example, detection of a suspicious person or abnormal behavior. The detection unit, for example, issues an alarm when a suspicious person is discovered. The detection unit can also issue an alarm when abnormal behavior is detected. This allows the crime prevention system according to the embodiment to realize a safer society. For example, the crime prevention system can optimize crime prevention measures taking into account past crime data and regional characteristics, provide risk information, suggest specific crime prevention measures, patrol the risk area, and detect abnormalities, thereby realizing a safer society.

[0058] The analysis unit can estimate the user's emotions and adjust the priority of crime prevention measures based on the estimated user emotions. For example, if the user feels anxious, the analysis unit uses the AI ​​to detect that emotion and immediately prioritize crime prevention measures in high-risk areas. The analysis unit can also detect the user's feeling of relief and maintain normal crime prevention measures. The analysis unit can also detect the user's feeling of tension and suggest additional crime prevention measures. This allows for more effective crime prevention measures by adjusting the priority of crime prevention measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the analysis unit may be performed using an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0059] The analysis unit can optimize crime prevention measures based on weather and event information in addition to past crime data. For example, the analysis unit uses AI to analyze weather data and strengthen indoor crime prevention measures when it rains. The analysis unit can also analyze local event information and increase security during large-scale events. The analysis unit can also combine past crime data and weather data to predict crime risks under specific weather conditions and adjust crime prevention measures. This allows for more accurate crime prevention measures to be implemented by taking weather and event information into consideration. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input weather data and event information into the generation AI and have the generation AI optimize the crime prevention measures.

[0060] The analysis unit can apply different crime prevention algorithms based on the characteristics of the area. For example, the analysis unit can use AI to analyze the characteristics of urban and suburban areas and propose crime prevention measures appropriate for each. The analysis unit can also use AI to analyze the characteristics of residential and commercial areas and apply different crime prevention measures. The analysis unit can also use AI to analyze crime trends in the area and strengthen crime prevention measures against specific crimes. This allows for more effective crime prevention measures to be provided by providing crime prevention measures tailored to the characteristics of the area. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input data on the characteristics of the area into the generation AI and have the generation AI apply the crime prevention algorithm.

[0061] When analyzing crime data, the analysis unit can use different analysis methods for each type of crime. For example, the analysis unit can have AI analyze data on theft and violent crimes separately and propose appropriate crime prevention measures for each. The analysis unit can also have AI analyze data on cybercrime and physical crime separately and apply different crime prevention measures. The analysis unit can also have AI use different analysis algorithms for each type of crime to propose highly accurate crime prevention measures. This allows for providing crime prevention measures appropriate for each type of crime, thereby achieving more accurate crime prevention measures. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input crime data into the generation AI and have the generation AI perform an analysis for each type of crime.

[0062] The providing unit can estimate the user's emotions and adjust the method of providing risk information based on the estimated user emotions. For example, if the user feels anxious, the providing unit can have the AI ​​detect the emotion and send an emergency notification. Furthermore, if the user feels relieved, the providing unit can also have the AI ​​detect the emotion and maintain the normal notification method. Furthermore, if the user feels nervous, the providing unit can also have the AI ​​detect the emotion and provide detailed risk information. This allows for more appropriate risk information to be provided by adjusting the method of providing risk information based on 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 providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0063] When providing risk information, the providing unit can provide the information at an appropriate time, taking into account the user's location information. For example, the providing unit uses an AI to analyze the user's location information and send a notification when the user approaches a risk area. The providing unit can also analyze the user's location information and refrain from sending a notification if the user is in a safe area. The providing unit can also monitor the user's location information in real time and provide risk information at the optimal time. This enables more effective crime prevention measures to be realized by providing risk information based on the user's location information. Some or all of the above-described processing in the providing unit may be performed using an AI, for example, or may be performed without using an AI. For example, the providing unit can input the user's location information to the generation AI and cause the generation AI to provide the risk information.

[0064] When providing risk information, the providing unit can provide customized information by referring to the user's past behavioral history. For example, the providing unit can have an AI analyze the user's past behavioral history and send a notification when the user approaches a risk area. The providing unit can also have an AI analyze the user's past behavioral history and suggest countermeasures for specific risks. The providing unit can also provide customized risk information based on the user's past behavioral history. This allows for more effective crime prevention measures to be realized by providing customized risk information based on the user's past behavioral history. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's past behavioral history data into the generation AI and cause the generation AI to provide customized risk information.

[0065] When providing risk information, the providing unit can determine the priority of notifications based on the importance of the information. For example, the providing unit can use AI to analyze the importance of risk information and prioritize notifying high-risk information. The providing unit can also use AI to analyze the importance of risk information and postpone low-risk information. The providing unit can also use AI to determine the optimal priority of notifications based on the importance of risk information. In this way, important risk information can be provided preferentially by determining the priority of notifications based on the importance of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input importance data of risk information to the generating AI and have the generating AI determine the priority of notifications.

[0066] The suggestion unit can estimate the user's emotions and adjust the method of proposing crime prevention measures based on the estimated user emotions. For example, if the user feels anxious, the suggestion unit can use the AI ​​to detect that emotion and suggest emergency crime prevention measures. Furthermore, if the user feels relieved, the suggestion unit can use the AI ​​to detect that emotion and suggest regular crime prevention measures. Furthermore, if the user feels nervous, the suggestion unit can use the AI ​​to detect that emotion and suggest detailed crime prevention measures. This allows for more appropriate crime prevention measures to be provided by adjusting the method of proposing crime prevention measures based on 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, 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of proposing crime prevention measures.

[0067] When proposing crime prevention measures, the suggestion unit can make suggestions at appropriate times, taking into account the user's lifestyle rhythm. For example, the suggestion unit uses AI to analyze the user's lifestyle rhythm and suggest crime prevention measures at optimal times. The suggestion unit can also use AI to analyze the user's lifestyle rhythm and strengthen crime prevention measures at night. The suggestion unit can also use AI to suggest crime prevention measures at optimal times based on the user's lifestyle rhythm. This makes it possible to realize more effective crime prevention measures by suggesting crime prevention measures based on the user's lifestyle rhythm. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's lifestyle rhythm data into the generation AI and have the generation AI execute the crime prevention measure suggestions.

[0068] When proposing a crime prevention measure, the suggestion unit can make a customized suggestion by referring to the user's past crime prevention measure history. For example, the suggestion unit uses an AI to analyze the user's past crime prevention measure history and suggest optimal crime prevention measures. The suggestion unit can also use an AI to analyze the user's past crime prevention measure history and suggest measures for specific risks. The suggestion unit can also use an AI to suggest customized crime prevention measures based on the user's past crime prevention measure history. This allows for more effective crime prevention measures to be realized by proposing customized crime prevention measures based on the user's past crime prevention measure history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's past crime prevention measure history data into the generation AI and have the generation AI execute the customized crime prevention measure proposal.

[0069] When proposing a crime prevention measure, the proposal unit can evaluate the effectiveness of the proposal and select an appropriate crime prevention measure. For example, the proposal unit uses AI to analyze the effectiveness of past crime prevention measures and propose the most effective crime prevention measure. The proposal unit can also use AI to evaluate the effectiveness of the proposal in real time and adjust the crime prevention measure as necessary. The proposal unit can also use AI to select and propose the optimal crime prevention measure based on data on past crime prevention measures. This makes it possible to provide more effective crime prevention measures by evaluating the effectiveness of the proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data on past crime prevention measures into the generation AI and have the generation AI evaluate the effectiveness of the crime prevention measures.

[0070] The patrol unit can estimate the user's emotions and adjust the patrol route based on the estimated user emotions. For example, if the user feels anxious, the AI ​​in the patrol unit can detect that emotion and prioritize patrol in high-risk areas. Furthermore, if the user feels safe, the AI ​​in the patrol unit can detect that emotion and maintain the normal patrol route. Furthermore, if the user feels nervous, the AI ​​in the patrol unit can detect that emotion and suggest an additional patrol route. This allows for more effective patrol by adjusting the patrol route based on 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 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 patrol unit can be performed using, for example, an AI. For example, the patrol unit can input the user's emotion data into the generation AI and have the generation AI adjust the patrol route.

[0071] When patrolling, the patrol unit can select an appropriate patrol route by referring to past patrol data. For example, the patrol unit uses AI to analyze past patrol data and select the most effective patrol route. The patrol unit can also analyze past patrol data and prioritize patrol of risk areas. The patrol unit can also use AI to propose an optimal patrol route based on past patrol data. In this way, more effective patrol can be achieved by selecting an optimal patrol route based on past patrol data. Some or all of the above-mentioned processing in the patrol unit may be performed using AI, for example, or may be performed without using AI. For example, the patrol unit can input past patrol data into a generation AI and have the generation AI select a patrol route.

[0072] The patrol unit can optimize the patrol route based on weather and traffic conditions during patrol. For example, the patrol unit uses AI to analyze weather data and prioritize indoor patrol routes in bad weather. The patrol unit can also analyze traffic conditions and propose patrol routes that avoid congestion. The patrol unit can also combine weather and traffic conditions to select the optimal patrol route. This enables more effective patrol by optimizing the patrol route based on weather and traffic conditions. Some or all of the above-mentioned processing in the patrol unit may be performed using AI, for example, or may be performed without using AI. For example, the patrol unit can input weather data and traffic condition data into the generation AI and have the generation AI optimize the patrol route.

[0073] The patrol unit can apply different patrol algorithms based on the characteristics of the patrol area during patrol. For example, the patrol unit can use AI to analyze the characteristics of urban and suburban areas and apply patrol algorithms appropriate for each. The patrol unit can also use AI to analyze the characteristics of residential and commercial areas and apply different patrol algorithms. The patrol unit can also select the optimal patrol algorithm based on the characteristics of the patrol area. In this way, more effective patrol can be achieved by applying different patrol algorithms based on the characteristics of the patrol area. Some or all of the above-mentioned processing in the patrol unit can be performed using AI, for example, or without AI. For example, the patrol unit can input characteristic data of the patrol area to the generation AI and have the generation AI apply the patrol algorithm.

[0074] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user feels anxious, the AI ​​in the detection unit can detect the user's emotions and tighten the anomaly detection criteria. Alternatively, if the user feels relieved, the AI ​​in the detection unit can detect the user's emotions and maintain the normal anomaly detection criteria. Alternatively, if the user feels nervous, the AI ​​in the detection unit can detect the user's emotions and adjust the anomaly detection criteria. This allows for more effective anomaly detection by adjusting the anomaly detection criteria based on 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 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 detection unit can be performed using, for example, an AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the anomaly detection criteria.

[0075] When detecting an anomaly, the detection unit can optimize the detection algorithm by referring to past detection data. For example, the detection unit uses AI to analyze past detection data and select the most effective detection algorithm. The detection unit can also analyze past detection data and adjust the detection algorithm for a specific risk. The detection unit can also propose an optimal detection algorithm based on past detection data. In this way, more effective anomaly detection can be achieved by optimizing the detection algorithm based on past detection data. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past detection data to a generation AI and have the generation AI optimize the detection algorithm.

[0076] When detecting an anomaly, the detection unit can apply different detection methods based on the characteristics of the area. For example, the detection unit uses AI to analyze the characteristics of urban and suburban areas and apply appropriate detection methods for each. The detection unit can also use AI to analyze the characteristics of residential and commercial areas and apply different detection methods. The detection unit can also select the optimal detection method based on the characteristics of the area. In this way, more effective anomaly detection can be achieved by applying different detection methods based on the characteristics of the area. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input area characteristic data to the generation AI and have the generation AI apply the detection method.

[0077] The detection unit can integrate multiple sensor information to improve detection accuracy when detecting an anomaly. For example, the detection unit uses AI to analyze multiple sensor information and improve the accuracy of anomaly detection. The detection unit can also integrate different types of sensor information and improve the accuracy of anomaly detection. The detection unit can also analyze sensor information in real time and improve the accuracy of anomaly detection. In this way, the accuracy of anomaly detection can be improved by integrating multiple sensor information. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input multiple sensor information to a generation AI and have the generation AI integrate the sensor information.

[0078] The detection unit can estimate the user's emotions and adjust the notification method for anomaly detection based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit can have the AI ​​detect the emotion and send an emergency notification. Alternatively, if the user is feeling relieved, the detection unit can have the AI ​​detect the emotion and maintain the normal notification method. Alternatively, if the user is feeling nervous, the detection unit can have the AI ​​detect the emotion and provide detailed anomaly detection information. This allows for more appropriate notifications to be provided by adjusting the notification method for anomaly detection based on the user's emotions. The emotion estimation is achieved 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 detection unit can be performed using, for example, an AI, or without an AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0079] When detecting an anomaly, the detection unit can optimize the detection algorithm based on local crime trends. For example, the detection unit uses AI to analyze local crime trends and select the most effective detection algorithm. The detection unit can also use AI to analyze local crime trends and adjust the detection algorithm for specific risks. The detection unit can also use AI to propose the optimal detection algorithm based on local crime trends. This enables more effective anomaly detection by optimizing the detection algorithm based on local crime trends. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input local crime trend data into the generation AI and have the generation AI optimize the detection algorithm.

[0080] When an anomaly is detected, the detection unit can analyze social media posts to obtain real-time risk information. For example, the detection unit uses AI to analyze social media posts and detect crime risks in real time. The detection unit can also use AI to monitor specific keywords and issue a notification when crime risks increase. The detection unit can also use AI to analyze social media posts and predict crime risks in specific areas. In this way, by analyzing social media posts, real-time risk information can be obtained and the accuracy of anomaly detection can be improved. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input social media post data to a generation AI and cause the generation AI to obtain real-time risk information.

[0081] When detecting an anomaly, the detection unit can evaluate the effectiveness of past detection data and select an appropriate detection method. For example, the detection unit uses AI to analyze past detection data and select the most effective detection method. The detection unit can also analyze past detection data and adjust the detection method for a specific risk. The detection unit can also use AI to propose the optimal detection method based on past detection data. In this way, by evaluating the effectiveness of past detection data, the optimal detection method can be selected and the accuracy of anomaly detection can be improved. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input past detection data to a generation AI and have the generation AI select a detection method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, suggestion unit, patrol unit, and detection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the patrol unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, suggestion unit, patrol unit, and detection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the patrol unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, suggestion unit, patrol unit, and detection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the patrol unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, provision unit, suggestion unit, patrol unit, and detection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the patrol unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the detection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

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

[0083] When analyzing crime data, the analysis unit can use different analysis methods depending on the time of day when crimes occur. For example, AI can analyze daytime and nighttime crime data separately and propose appropriate crime prevention measures for each. The analysis unit can also analyze weekend and weekday crime data separately and apply different crime prevention measures. Furthermore, the analysis unit can predict the risk of crimes concentrating during specific time periods and propose crime prevention measures specific to those time periods. This allows for more accurate crime prevention measures to be implemented by providing appropriate crime prevention measures for each time period when crimes occur.

[0084] The analysis unit can estimate the user's emotions and adjust the priority of crime prevention measures based on the estimated user emotions. For example, if the user is feeling anxious, the AI ​​can detect that emotion and immediately prioritize crime prevention measures in high-risk areas. The analysis unit can also detect if the user is feeling safe and maintain normal crime prevention measures. Furthermore, if the user is feeling nervous, the AI ​​can detect that emotion and suggest additional crime prevention measures. This allows for more effective crime prevention measures to be achieved by adjusting the priority of crime prevention measures based on the user's emotions.

[0085] When providing risk information, the providing unit can provide the information at an appropriate time, taking into account the user's location information. For example, AI can analyze the user's location information and send a notification when the user approaches a risk area. The providing unit can also analyze the user's location information and refrain from sending a notification if the user is in a safe area. Furthermore, the providing unit can monitor the user's location information in real time and provide risk information at the optimal time. This makes it possible to provide risk information based on the user's location information, thereby realizing more effective crime prevention measures.

[0086] The provision unit can estimate the user's emotions and adjust the method of providing risk information based on the estimated user's emotions. For example, if the user is feeling anxious, the AI ​​can detect that emotion and send an emergency notification. The provision unit can also detect if the user is feeling relieved, and maintain the normal notification method. Furthermore, if the user is feeling nervous, the AI ​​can detect that emotion and provide detailed risk information. This makes it possible to provide more appropriate risk information by adjusting the method of providing risk information based on the user's emotions.

[0087] When proposing crime prevention measures, the suggestion unit can make suggestions at appropriate times, taking into account the user's lifestyle rhythm. For example, AI can analyze the user's lifestyle rhythm and suggest crime prevention measures at the optimal time. The suggestion unit can also analyze the user's lifestyle rhythm and strengthen crime prevention measures at night. Furthermore, the suggestion unit can also suggest crime prevention measures at the optimal time based on the user's lifestyle rhythm. This makes it possible to realize more effective crime prevention measures by suggesting crime prevention measures based on the user's lifestyle rhythm.

[0088] The suggestion unit can estimate the user's emotions and adjust the method of suggesting crime prevention measures based on the estimated user emotions. For example, if the user is feeling anxious, the AI ​​can detect that emotion and suggest emergency crime prevention measures. The suggestion unit can also detect if the user is feeling relieved and suggest regular crime prevention measures. Furthermore, if the user is feeling nervous, the AI ​​can detect that emotion and suggest detailed crime prevention measures. In this way, by adjusting the method of suggesting crime prevention measures based on the user's emotions, more appropriate crime prevention measures can be provided.

[0089] When patrolling, the patrol unit can optimize its route based on weather and traffic conditions. For example, AI analyzes weather data and prioritizes indoor routes in bad weather. The patrol unit can also analyze traffic conditions and suggest routes that avoid traffic jams. Furthermore, the patrol unit can combine weather and traffic conditions to select the optimal route. This allows for more effective patrol by optimizing the route based on weather and traffic conditions.

[0090] The patrol unit can estimate the user's emotions and adjust the patrol route based on the estimated user emotions. For example, if the user is feeling anxious, the AI ​​can detect that emotion and prioritize patrol in high-risk areas. Also, if the user is feeling safe, the AI ​​can detect that emotion and maintain the normal patrol route. Furthermore, if the user is feeling nervous, the AI ​​can detect that emotion and suggest an additional patrol route. This allows for more effective patrol by adjusting the patrol route based on the user's emotions.

[0091] When an anomaly is detected, the detection unit can analyze social media posts to obtain real-time risk information. For example, AI can analyze social media posts and detect crime risks in real time. The detection unit can also use AI to monitor specific keywords and notify users when crime risks increase. Furthermore, the detection unit can also use AI to analyze social media posts and predict crime risks in specific areas. This makes it possible to obtain real-time risk information by analyzing social media posts, improving the accuracy of anomaly detection.

[0092] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is feeling anxious, the AI ​​can detect that emotion and tighten the anomaly detection criteria. The detection unit can also detect if the user is feeling relieved, and maintain the normal anomaly detection criteria. Furthermore, the detection unit can detect if the user is feeling nervous, and adjust the anomaly detection criteria. This makes it possible to achieve more effective anomaly detection by adjusting the anomaly detection criteria based on the user's emotions.

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

[0094] Step 1: The analysis unit optimizes crime prevention measures based on past crime data and local characteristics. Past crime data includes the type of crime, the date and time of the crime, and the location, while local characteristics include population density, security situation, and geographical conditions. The analysis unit analyzes this data to evaluate the crime risk in a specific area and optimize crime prevention measures. Step 2: The provider provides the risk information obtained by the analyzer. The risk information includes the type of risk, risk level, and method of provision. If there are frequent crimes in a specific area, the provider provides this information to residents to warn them. Step 3: The proposal department proposes specific crime prevention measures based on the risk information provided by the provision department. Specific crime prevention measures include installing security cameras, deploying security guards, and introducing alarm systems, and suggests that residents refrain from going out at night based on the risk information. Step 4: The patrol department patrols the risk area. Patrols include the frequency, route, and time of patrol, and the department periodically patrols the risk area to detect abnormalities. Step 5: The detection unit detects abnormalities in the area patrolled by the patrol unit. Abnormalities include the detection of suspicious individuals and abnormal behavior, and if a suspicious individual is found or abnormal behavior is detected, an alarm is issued.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis section that optimizes crime prevention measures based on past crime data and local characteristics, and a providing unit that provides the risk information obtained by the analysis unit; a suggestion unit that proposes specific crime prevention measures based on the risk information provided by the provision unit; A patrol department that patrols risk areas; a detection unit that detects an abnormality in the area patrolled by the patrol unit. A system characterized by:

2. The analysis unit Estimating user emotions and adjusting the priority of crime prevention measures based on the estimated user emotions 2. The system of claim 1.

3. The analysis unit Optimize crime prevention measures based on past crime data as well as weather and event information 2. The system of claim 1.

4. The analysis unit Apply different crime prevention algorithms based on local characteristics 2. The system of claim 1.

5. The analysis unit When analyzing crime data, different analytical methods are used for different types of crime.

2. The system of claim 1.

6. The providing unit Estimates user emotions and adjusts how risk information is provided based on the estimated user emotions 2. The system of claim 1.

7. The providing unit When providing risk information, provide the information at the appropriate time taking into account the user's location information.

2. The system of claim 1.

8. The providing unit When providing risk information, provide customized information by referring to the user's past behavior history.

2. The system of claim 1.

Images