system

Abstract

The system according to the embodiment aims to analyze security camera footage and effectively prevent shoplifting. [Solution] A system according to an embodiment includes a detection unit, a clipping unit, and a report creation unit. The detection unit analyzes security camera footage and detects individuals behaving suspiciously. The clipping unit cuts out necessary video based on information detected by the detection unit. The report creation unit creates a report identifying the perpetrator based on the video clipped by the clipping unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, security camera footage only serves the purpose of capturing evidence at the crime scene, making it difficult to effectively prevent shoplifting.

[0005] The system according to the embodiment aims to analyze security camera footage and effectively prevent shoplifting. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a clipping unit, and a report creation unit. The detection unit analyzes security camera footage and detects individuals behaving suspiciously. The clipping unit clips out necessary video based on information detected by the detection unit. The report creation unit creates a report identifying the perpetrator based on the video clipped by the clipping unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze security camera footage and effectively prevent shoplifting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A security system according to an embodiment of the present invention is designed for supermarkets and convenience stores. Its purpose is to reduce shoplifting losses. Currently, security cameras only capture evidence at crime scenes, and shoplifting losses nationwide exceed 500 billion yen annually. This service reads video footage from multiple security cameras and provides the following functions: First, AI analyzes security camera footage and detects suspicious individuals. Information about detected suspicious individuals is communicated to personnel in real time, allowing them to respond quickly. Next, AI automatically extracts and generates only the necessary portions of video data, allowing personnel to efficiently review the necessary information. Finally, AI creates a report for identifying the perpetrator. This report includes details of the crime and the perpetrator's characteristics, providing personnel with important information for identifying the perpetrator. In this way, this service utilizes AI to analyze security camera footage, detect suspicious individuals, extract necessary video, and create a report to identify the perpetrator, thereby reducing shoplifting losses. For example, AI can analyze security camera footage in real time and detect suspicious individuals based on specific movements and patterns of behavior. Detected information is immediately notified to personnel, enabling a rapid response. AI can also automatically extract only the necessary parts from vast amounts of video data, providing information efficiently. Furthermore, AI can create detailed reports to identify the perpetrator, clarifying the details of the crime and the characteristics of the perpetrator. This makes it easier for personnel to identify the perpetrator, effectively reducing shoplifting. In this way, security systems can effectively reduce shoplifting.

[0029] A security system according to an embodiment includes a detection unit, a clipping unit, and a report creation unit. The detection unit analyzes security camera footage to detect individuals engaging in suspicious behavior. For example, the detection unit detects suspicious individuals based on specific movements or behavioral patterns. The detection unit can identify suspicious individuals by analyzing, for example, walking patterns, hand movements, and gaze direction. The detection unit can also analyze footage in real time using AI to quickly detect suspicious individuals. For example, the AI ​​tracks the movements of individuals in the footage and detects abnormal behavior. The clipping unit cuts out necessary video based on information detected by the detection unit. For example, the clipping unit clips out only necessary portions based on footage of detected suspicious individuals. The clipping unit can automatically clip out portions that include, for example, a specific time range or specific actions. The clipping unit can also analyze footage using AI to efficiently clip out necessary portions. For example, the AI ​​can automatically extract and clip out important scenes from the footage. The report creation unit creates a report identifying the perpetrator based on the video clipped by the clipping unit. For example, the report creation unit creates a report that clarifies the details of the crime and the characteristics of the perpetrator based on the clipped video. The report creation unit can create a detailed report that includes information such as the time and location of the crime and the perpetrator's appearance. The report creation unit can also analyze the video using AI to extract important information for identifying the perpetrator. For example, the AI ​​can analyze the characteristics of people in the video and provide information useful for identifying the perpetrator. This allows the security system according to the embodiment to analyze security camera footage, detect suspicious individuals, clip necessary video, and create a report to identify the perpetrator. This allows the security system to effectively reduce shoplifting incidents.

[0030] The detection unit can analyze security camera footage and detect individuals exhibiting suspicious behavior based on specific behaviors or behavior patterns. For example, the detection unit can analyze security camera footage in real time and detect suspicious individuals based on specific behaviors or behavior patterns. For example, the detection unit can analyze walking patterns and detect individuals with abnormal walking. The detection unit can also analyze hand movements and detect individuals exhibiting specific behaviors. For example, the detection unit can detect individuals with unnatural hand movements. The detection unit can also analyze gaze direction and detect individuals with specific gaze patterns. For example, the detection unit can detect individuals whose gaze moves frequently. This improves detection accuracy by detecting suspicious individuals based on specific behaviors or behavior patterns. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input security camera footage into AI, which can then detect suspicious individuals based on specific behaviors or behavior patterns.

[0031] The cutout unit can cut out necessary portions based on video of a detected person behaving suspiciously. For example, the cutout unit cuts out only necessary portions based on video of a detected suspicious person. For example, the cutout unit can automatically cut out portions that include a specific time range or a specific action. The cutout unit can also analyze the video using AI and efficiently cut out necessary portions. For example, the AI ​​can automatically extract and cut out important scenes within the video. This makes it possible to efficiently provide information by cutting out only necessary portions. Some or all of the above-mentioned processing in the cutout unit may be performed using AI, for example, or may be performed without using AI. For example, the cutout unit can input video of a detected suspicious person into AI, and the AI ​​can cut out necessary portions.

[0032] The report creation unit can create a specific report for identifying the perpetrator based on the clipped video. For example, the report creation unit can create a report that clarifies the details of the crime and the characteristics of the perpetrator based on the clipped video. For example, the report creation unit can create a detailed report that includes information such as the time and location of the crime and the perpetrator's appearance. The report creation unit can also analyze the video using AI to extract important information for identifying the perpetrator. For example, the AI ​​can analyze the characteristics of people in the video and provide information useful for identifying the perpetrator. This makes it easier to identify the perpetrator by creating a detailed report. Some or all of the above-mentioned processing in the report creation unit may be performed using AI, for example, or may be performed without using AI. For example, the report creation unit can input the clipped video into AI, which can analyze the details of the crime and the characteristics of the perpetrator and create a report.

[0033] When analyzing security camera footage, the detection unit can change the detection criteria based on a specific time period or day of the week. For example, when analyzing security camera footage, the detection unit changes the detection criteria based on a specific time period or day of the week. For example, the detection criteria can be tightened at night or close to closing time, so that even slight abnormal behavior is deemed suspicious. The detection criteria can also be relaxed during busy times such as weekends and holidays, so that normal crowded behavior is not deemed suspicious. Furthermore, normal detection criteria can be applied during relatively quiet times such as weekday daytime, so that abnormal behavior can be accurately detected. Thus, by changing the detection criteria based on a specific time period or day of the week, detection accuracy can be improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input security camera footage into AI, which can then change the detection criteria based on a specific time period or day of the week.

[0034] The detection unit can adjust the detection algorithm by referring to past detection history during detection. The detection unit can adjust the detection algorithm by referring to past detection history during detection, for example. For example, the detection unit can learn the behavioral patterns of suspicious people detected in the past and detect similar behavior again. It can also analyze false detection patterns from past detection history and adjust the algorithm to reduce false detections. Furthermore, it can also improve detection accuracy for specific time periods or locations based on past detection history. Thus, by referring to past detection history, the accuracy of the detection algorithm is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past detection history data into AI, which can then optimize the detection algorithm based on the past data.

[0035] The detection unit can improve detection accuracy based on store layout information when analyzing security camera footage. For example, when analyzing security camera footage, the detection unit improves detection accuracy by taking store layout information into consideration. For example, blind spots and hard-to-see locations can be identified based on the store layout information, thereby improving detection accuracy. Furthermore, abnormal behavior in specific areas can be preferentially detected based on the store layout information. Furthermore, camera placement can be optimized based on the store layout information, thereby improving detection accuracy. In this way, consideration of the store layout information improves detection accuracy. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input store layout information into AI, which can improve detection accuracy based on the layout information.

[0036] The detection unit can improve detection accuracy by combining data from other sensors during detection. For example, the detection unit can improve detection accuracy by integrating data from other sensors (e.g., audio sensors and temperature sensors) during detection. For example, it can detect abnormal sounds based on data from an audio sensor and integrate the data with video to identify a suspicious person. It can also detect abnormal temperature changes based on data from a temperature sensor and integrate the data with video to identify a suspicious person. It can also integrate data from other sensors to simultaneously detect multiple abnormal patterns and identify a suspicious person. In this way, by integrating data from other sensors, detection accuracy is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data from an audio sensor or a temperature sensor into AI, which can then integrate the data to improve detection accuracy.

[0037] The cropping unit can select an appropriate cropping method based on the resolution and frame rate of the video when cropping. For example, the cropping unit selects the optimal cropping method based on the resolution and frame rate of the video when cropping. For example, for high-resolution video, a method that crops out details clearly can be selected. For low-resolution video, a method that crops out important parts while emphasizing them can also be selected. Furthermore, for high-frame-rate video, a method that accurately crops out moving parts can also be selected. In this way, by selecting the optimal cropping method based on the resolution and frame rate of the video, cropping accuracy is improved. Some or all of the above-mentioned processing in the cropping unit may be performed using, for example, AI, or may be performed without using AI. For example, the cropping unit can input data on the resolution and frame rate of the video into AI, which can then select the optimal cropping method.

[0038] The cropping unit can adjust the cropping algorithm by referring to past cropping history when cropping. For example, the cropping unit can adjust the cropping algorithm by referring to past cropping history when cropping. For example, the cropping unit can learn an effective cropping method from the past cropping history and optimize the algorithm. Also, the cropping unit can analyze erroneous cropping patterns based on the past cropping history and adjust the algorithm to reduce erroneous cropping. Furthermore, the cropping accuracy for a specific scene can be improved based on the past cropping history. In this way, the accuracy of the cropping algorithm is improved by referring to the past cropping history. Some or all of the above-described processing in the cropping unit may be performed using, for example, AI, or may be performed without using AI. For example, the cropping unit can input past cropping history data into AI, which can optimize the cropping algorithm based on the past data.

[0039] The cutout unit can improve the cutout accuracy based on background information of the video when cutting out. For example, the cutout unit improves the cutout accuracy by taking the background information of the video into consideration when cutting out. For example, based on the background information of the video, it can distinguish between a moving person and a static background and accurately cut them out. It can also improve the cutout accuracy in a specific area based on the background information of the video. Furthermore, it can preferentially cut out moving parts based on the background information of the video. In this way, by taking the background information of the video into consideration, the cutout accuracy is improved. Some or all of the above-mentioned processing in the cutout unit may be performed using, for example, AI, or may be performed without using AI. For example, the cutout unit can input the background information of the video into AI, and the AI ​​can improve the cutout accuracy based on the background information.

[0040] The cropping unit can improve cropping accuracy based on other video sources during cropping. For example, the cropping unit can improve cropping accuracy by referring to other video sources (e.g., video from adjacent cameras). For example, the cropping unit can improve accuracy by cropping the same scene from multiple viewpoints based on video from adjacent cameras. The cropping unit can also improve cropping accuracy by referencing other video sources and integrating video from different angles. Furthermore, moving parts can be accurately cropped based on video from multiple cameras. This improves cropping accuracy by referring to other video sources. Some or all of the above-described processing in the cropping unit may be performed using, for example, AI, or may be performed without using AI. For example, the cropping unit can input video data from adjacent cameras into AI, which can then integrate data from multiple viewpoints to improve cropping accuracy.

[0041] The report creation unit can automatically classify the details of the crime and the characteristics of the perpetrator when creating a report and reflect the results in the report. For example, the report creation unit can automatically classify the details of the crime and the characteristics of the perpetrator when creating a report and reflect the results in the report. For example, the report creation unit can automatically classify the details of the crime and reflect the results in the report. The report creation unit can also automatically classify the characteristics of the perpetrator and reflect the results in the report. Furthermore, the details of the crime and the characteristics of the perpetrator can be integrated to create a detailed report. This automatically classifies the details of the crime and the characteristics of the perpetrator, thereby improving the accuracy of the report. Some or all of the above-mentioned processing in the report creation unit may be performed using, or without, AI. For example, the report creation unit can input data on the details of the crime and the characteristics of the perpetrator into AI, which can then automatically classify the data and reflect the results in the report.

[0042] The report creation unit can adjust the report creation algorithm by referring to past report data when creating a report. The report creation unit, for example, adjusts the report creation algorithm by referring to past report data when creating a report. For example, the report creation unit can learn effective report creation methods based on past report data and optimize the algorithm. Also, the report creation unit can analyze patterns of erroneous reports based on past report data and adjust the algorithm to reduce erroneous reports. Furthermore, the report creation accuracy for specific scenes can be improved based on past report data. In this way, by referring to past report data, the accuracy of the report creation algorithm is improved. Some or all of the above-described processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input past report data into AI, which can optimize the report creation algorithm based on the past data.

[0043] The report creation unit can improve the accuracy of the report by referring to other data sources when creating a report. For example, the report creation unit can improve the accuracy of the report by referring to other data sources (e.g., police databases) when creating a report. For example, the report creation unit can refer to the police database to describe the characteristics of the perpetrator in detail. It can also refer to other data sources to supplement the details of the crime. Furthermore, it can analyze past crime patterns based on the police database to improve the accuracy of the report. In this way, the accuracy of the report is improved by referring to other data sources. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input the police database into AI, which analyzes the data to improve the accuracy of the report.

[0044] The report creation unit can adjust the content of the report based on the time and location of the crime when creating the report. The report creation unit, for example, customizes the content of the report based on the time and location of the crime when creating the report. For example, based on the time of the crime, it can describe crime patterns that tend to occur during specific time periods. It can also describe crime patterns that tend to occur in specific locations based on the location of the crime. Furthermore, it can integrate the time and location of the crime to create a detailed report. In this way, by customizing the content of the report based on the time and location of the crime, it is possible to provide more detailed information. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input data on the time and location of the crime into AI, which can analyze the data and customize the content of the report.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The detection unit can analyze a user's purchasing history and adjust the detection criteria for detecting people behaving suspiciously based on the analyzed purchasing history. For example, if a user who frequently purchases a particular product picks up that product, the detection criteria can be determined to be normal behavior and the detection criteria can be relaxed. Also, if a user visiting a store for the first time picks up an expensive product, the detection criteria can be tightened and the behavior can be determined to be suspicious. Furthermore, the detection criteria can be dynamically adjusted based on the behavior of users who tend to purchase specific products during specific time periods. In this way, adjusting the detection criteria based on the user's purchasing history improves detection accuracy.

[0047] The detection unit can analyze the store's congestion status and adjust the detection criteria for people behaving suspiciously based on the analyzed congestion status. For example, during busy times, the detection criteria can be relaxed so that normal crowded behavior is not judged as suspicious. Also, during quiet times, the detection criteria can be tightened so that even slight abnormal behavior is judged as suspicious. Furthermore, if a specific area is crowded, the detection criteria for that area can be dynamically adjusted. In this way, by adjusting the detection criteria based on the store's congestion status, detection accuracy can be improved.

[0048] The clipping unit can analyze a user's purchase history and adjust the range of clipping based on the analyzed purchase history. For example, it can clip a wide range of the behavior of a user who frequently purchases a specific product to provide detailed information. It can also clip only the minimum necessary range of the behavior of a user visiting the store for the first time to provide concise information. Furthermore, it can dynamically adjust the range of clipping based on the behavior of a user who tends to purchase specific products during a specific time period. This allows the necessary information to be provided efficiently by adjusting the range of clipping based on the user's purchase history.

[0049] The report creation unit can analyze a user's purchasing history and adjust the contents of the report based on the analyzed purchasing history. For example, the behavior of users who frequently purchase specific products can be described in detail to improve the accuracy of the report. It can also provide a report that covers the main points by briefly describing the behavior of users who visit the store for the first time. Furthermore, it can dynamically adjust the contents of the report based on the behavior of users who tend to purchase specific products during specific time periods. This allows detailed information to be provided by adjusting the contents of the report based on the user's purchasing history.

[0050] During detection, the detection unit can improve detection accuracy by combining data from other sensors. For example, it can detect abnormal sounds based on data from an audio sensor and integrate the data with video to identify a suspicious person. It can also detect abnormal temperature changes based on data from a temperature sensor and integrate the data with video to identify a suspicious person. It can also integrate data from other sensors to simultaneously detect multiple abnormal patterns and identify a suspicious person. In this way, by integrating data from other sensors, detection accuracy can be improved.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The detection unit analyzes security camera footage to detect individuals behaving suspiciously. For example, the detection unit detects suspicious individuals based on specific movements or patterns of behavior. The detection unit can identify suspicious individuals by analyzing, for example, walking patterns, hand movements, and gaze direction. The detection unit can also use AI to analyze footage in real time and quickly detect suspicious individuals. For example, AI can track the movements of individuals in the footage and detect abnormal behavior. Step 2: The cutout unit cuts out the necessary video based on the information detected by the detection unit. For example, the cutout unit cuts out only the necessary parts based on the video of a detected suspicious person. The cutout unit can automatically cut out parts that include a specific time range or specific actions, for example. The cutout unit can also use AI to analyze the video and efficiently cut out the necessary parts. For example, AI can automatically extract and cut out important scenes within the video. Step 3: The report creation unit creates a report to identify the perpetrator based on the video clipped by the clipping unit. For example, the report creation unit creates a report that clarifies the details of the crime and the characteristics of the perpetrator based on the clipped video. The report creation unit can create a detailed report that includes information such as the time and location of the crime and the appearance of the perpetrator. The report creation unit can also use AI to analyze the video and extract important information for identifying the perpetrator. For example, AI can analyze the characteristics of people in the video and provide information that is useful for identifying the perpetrator.

[0053] (Example 2) A security system according to an embodiment of the present invention is designed for supermarkets and convenience stores. Its purpose is to reduce shoplifting losses. Currently, security cameras only capture evidence at crime scenes, and shoplifting losses nationwide exceed 500 billion yen annually. This service reads video footage from multiple security cameras and provides the following functions: First, AI analyzes security camera footage and detects suspicious individuals. Information about detected suspicious individuals is communicated to personnel in real time, allowing them to respond quickly. Next, AI automatically extracts and generates only the necessary portions of video data, allowing personnel to efficiently review the necessary information. Finally, AI creates a report for identifying the perpetrator. This report includes details of the crime and the perpetrator's characteristics, providing personnel with important information for identifying the perpetrator. In this way, this service utilizes AI to analyze security camera footage, detect suspicious individuals, extract necessary video, and create a report to identify the perpetrator, thereby reducing shoplifting losses. For example, AI can analyze security camera footage in real time and detect suspicious individuals based on specific movements and patterns of behavior. Detected information is immediately notified to personnel, enabling a rapid response. AI can also automatically extract only the necessary parts from vast amounts of video data, providing information efficiently. Furthermore, AI can create detailed reports to identify the perpetrator, clarifying the details of the crime and the characteristics of the perpetrator. This makes it easier for personnel to identify the perpetrator, effectively reducing shoplifting. In this way, security systems can effectively reduce shoplifting.

[0054] A security system according to an embodiment includes a detection unit, a clipping unit, and a report creation unit. The detection unit analyzes security camera footage to detect individuals engaging in suspicious behavior. For example, the detection unit detects suspicious individuals based on specific movements or behavioral patterns. The detection unit can identify suspicious individuals by analyzing, for example, walking patterns, hand movements, and gaze direction. The detection unit can also analyze footage in real time using AI to quickly detect suspicious individuals. For example, the AI ​​tracks the movements of individuals in the footage and detects abnormal behavior. The clipping unit cuts out necessary video based on information detected by the detection unit. For example, the clipping unit clips out only necessary portions based on footage of detected suspicious individuals. The clipping unit can automatically clip out portions that include, for example, a specific time range or specific actions. The clipping unit can also analyze footage using AI to efficiently clip out necessary portions. For example, the AI ​​can automatically extract and clip out important scenes from the footage. The report creation unit creates a report identifying the perpetrator based on the video clipped by the clipping unit. For example, the report creation unit creates a report that clarifies the details of the crime and the characteristics of the perpetrator based on the clipped video. The report creation unit can create a detailed report that includes information such as the time and location of the crime and the perpetrator's appearance. The report creation unit can also analyze the video using AI to extract important information for identifying the perpetrator. For example, the AI ​​can analyze the characteristics of people in the video and provide information useful for identifying the perpetrator. This allows the security system according to the embodiment to analyze security camera footage, detect suspicious individuals, clip necessary video, and create a report to identify the perpetrator. This allows the security system to effectively reduce shoplifting incidents.

[0055] The detection unit can analyze security camera footage and detect individuals exhibiting suspicious behavior based on specific behaviors or behavior patterns. For example, the detection unit can analyze security camera footage in real time and detect suspicious individuals based on specific behaviors or behavior patterns. For example, the detection unit can analyze walking patterns and detect individuals with abnormal walking. The detection unit can also analyze hand movements and detect individuals exhibiting specific behaviors. For example, the detection unit can detect individuals with unnatural hand movements. The detection unit can also analyze gaze direction and detect individuals with specific gaze patterns. For example, the detection unit can detect individuals whose gaze moves frequently. This improves detection accuracy by detecting suspicious individuals based on specific behaviors or behavior patterns. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input security camera footage into AI, which can then detect suspicious individuals based on specific behaviors or behavior patterns.

[0056] The cutout unit can cut out necessary portions based on video of a detected person behaving suspiciously. For example, the cutout unit cuts out only necessary portions based on video of a detected suspicious person. For example, the cutout unit can automatically cut out portions that include a specific time range or a specific action. The cutout unit can also analyze the video using AI and efficiently cut out necessary portions. For example, the AI ​​can automatically extract and cut out important scenes within the video. This makes it possible to efficiently provide information by cutting out only necessary portions. Some or all of the above-mentioned processing in the cutout unit may be performed using AI, for example, or may be performed without using AI. For example, the cutout unit can input video of a detected suspicious person into AI, and the AI ​​can cut out necessary portions.

[0057] The report creation unit can create a specific report for identifying the perpetrator based on the clipped video. For example, the report creation unit can create a report that clarifies the details of the crime and the characteristics of the perpetrator based on the clipped video. For example, the report creation unit can create a detailed report that includes information such as the time and location of the crime and the perpetrator's appearance. The report creation unit can also analyze the video using AI to extract important information for identifying the perpetrator. For example, the AI ​​can analyze the characteristics of people in the video and provide information useful for identifying the perpetrator. This makes it easier to identify the perpetrator by creating a detailed report. Some or all of the above-mentioned processing in the report creation unit may be performed using AI, for example, or may be performed without using AI. For example, the report creation unit can input the clipped video into AI, which can analyze the details of the crime and the characteristics of the perpetrator and create a report.

[0058] The detection unit can analyze the user's emotions and adjust the detection criteria for detecting people behaving suspiciously based on the analyzed user emotions. For example, the detection unit can analyze the user's emotions and adjust the detection criteria based on the analyzed user emotions. For example, if the user is nervous, the detection criteria can be tightened to determine more movements and behavioral patterns as suspicious. Also, if the user is relaxed, the detection criteria can be loosened to prevent normal movements from being determined as suspicious. Furthermore, if the user is impatient, the detection criteria can be quickly adjusted to instantly detect suspicious people. This improves detection accuracy by adjusting the detection criteria according to the user's emotions. The user's emotions can be analyzed using techniques such as facial expression analysis, voice analysis, and behavior analysis. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's facial expression data into AI, which then analyzes the user's emotions and adjusts the detection criteria.

[0059] When analyzing security camera footage, the detection unit can change the detection criteria based on a specific time period or day of the week. For example, when analyzing security camera footage, the detection unit changes the detection criteria based on a specific time period or day of the week. For example, the detection criteria can be tightened at night or close to closing time, so that even slight abnormal behavior is deemed suspicious. The detection criteria can also be relaxed during busy times such as weekends and holidays, so that normal crowded behavior is not deemed suspicious. Furthermore, normal detection criteria can be applied during relatively quiet times such as weekday daytime, so that abnormal behavior can be accurately detected. Thus, by changing the detection criteria based on a specific time period or day of the week, detection accuracy can be improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input security camera footage into AI, which can then change the detection criteria based on a specific time period or day of the week.

[0060] The detection unit can adjust the detection algorithm by referring to past detection history during detection. The detection unit can adjust the detection algorithm by referring to past detection history during detection, for example. For example, the detection unit can learn the behavioral patterns of suspicious people detected in the past and detect similar behavior again. It can also analyze false detection patterns from past detection history and adjust the algorithm to reduce false detections. Furthermore, it can also improve detection accuracy for specific time periods or locations based on past detection history. Thus, by referring to past detection history, the accuracy of the detection algorithm is improved. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past detection history data into AI, which can then optimize the detection algorithm based on the past data.

[0061] The detection unit can analyze the user's emotions and adjust the display method of the detection results based on the analyzed user's emotions. For example, the detection unit can analyze the user's emotions and adjust the display method of the detection results based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This improves visibility by adjusting the display method according to the user's emotions. The user's emotions are analyzed using techniques such as facial expression analysis, voice analysis, and behavior analysis. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's facial expression data into AI, which then analyzes the user's emotions and adjusts the display method.

[0062] The detection unit can improve detection accuracy based on store layout information when analyzing security camera footage. For example, when analyzing security camera footage, the detection unit improves detection accuracy by taking store layout information into consideration. For example, blind spots and hard-to-see locations can be identified based on the store layout information, thereby improving detection accuracy. Furthermore, abnormal behavior in specific areas can be preferentially detected based on the store layout information. Furthermore, camera placement can be optimized based on the store layout information, thereby improving detection accuracy. In this way, consideration of the store layout information improves detection accuracy. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input store layout information into AI, which can improve detection accuracy based on the layout information.

[0063] The detection unit can improve detection accuracy by combining data from other sensors during detection. For example, the detection unit can improve detection accuracy by integrating data from other sensors (e.g., audio sensors and temperature sensors) during detection. For example, it can detect abnormal sounds based on data from an audio sensor and integrate the data with video to identify a suspicious person. It can also detect abnormal temperature changes based on data from a temperature sensor and integrate the data with video to identify a suspicious person. It can also integrate data from other sensors to simultaneously detect multiple abnormal patterns and identify a suspicious person. In this way, by integrating data from other sensors, detection accuracy is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data from an audio sensor or a temperature sensor into AI, which can then integrate the data to improve detection accuracy.

[0064] The cropping unit can analyze a user's emotions and adjust the cropping range based on the analyzed user's emotions. For example, the cropping unit can analyze a user's emotions and adjust the cropping range based on the analyzed user's emotions. For example, if a user is nervous, the cropping unit can crop a wide range of video to provide detailed information. Alternatively, if a user is relaxed, the cropping unit can crop only the minimum necessary range to provide concise information. Furthermore, if a user is in a hurry, the cropping unit can quickly crop only important parts to enable immediate confirmation. This allows for efficient provision of necessary information by adjusting the cropping range according to the user's emotions. The analysis of a user's emotions is performed using techniques such as facial expression analysis, voice analysis, and behavior analysis. Some or all of the above-described processing in the cropping unit may be performed using, for example, AI, or without AI. For example, the cropping unit can input the user's facial expression data into AI, which then analyzes the user's emotions and adjusts the cropping range.

[0065] The cropping unit can select an appropriate cropping method based on the resolution and frame rate of the video when cropping. For example, the cropping unit selects the optimal cropping method based on the resolution and frame rate of the video when cropping. For example, for high-resolution video, a method that crops out details clearly can be selected. For low-resolution video, a method that crops out important parts while emphasizing them can also be selected. Furthermore, for high-frame-rate video, a method that accurately crops out moving parts can also be selected. In this way, by selecting the optimal cropping method based on the resolution and frame rate of the video, cropping accuracy is improved. Some or all of the above-mentioned processing in the cropping unit may be performed using, for example, AI, or may be performed without using AI. For example, the cropping unit can input data on the resolution and frame rate of the video into AI, which can then select the optimal cropping method.

[0066] The cropping unit can adjust the cropping algorithm by referring to past cropping history when cropping. For example, the cropping unit can adjust the cropping algorithm by referring to past cropping history when cropping. For example, the cropping unit can learn an effective cropping method from the past cropping history and optimize the algorithm. Also, the cropping unit can analyze erroneous cropping patterns based on the past cropping history and adjust the algorithm to reduce erroneous cropping. Furthermore, the cropping accuracy for a specific scene can be improved based on the past cropping history. In this way, the accuracy of the cropping algorithm is improved by referring to the past cropping history. Some or all of the above-described processing in the cropping unit may be performed using, for example, AI, or may be performed without using AI. For example, the cropping unit can input past cropping history data into AI, which can optimize the cropping algorithm based on the past data.

[0067] The cutout unit can analyze the user's emotions and adjust the display method of the cutout results based on the analyzed user's emotions. For example, the cutout unit can analyze the user's emotions and adjust the display method of the cutout results based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This improves visibility by adjusting the display method according to the user's emotions. The analysis of the user's emotions is performed using techniques such as facial expression analysis, voice analysis, and behavior analysis. Some or all of the above-described processing in the cutout unit may be performed using, for example, AI, or may be performed without AI. For example, the cutout unit can input the user's facial expression data into AI, which then analyzes the user's emotions and adjusts the display method.

[0068] The cutout unit can improve the cutout accuracy based on background information of the video when cutting out. For example, the cutout unit improves the cutout accuracy by taking the background information of the video into consideration when cutting out. For example, based on the background information of the video, it can distinguish between a moving person and a static background and accurately cut them out. It can also improve the cutout accuracy in a specific area based on the background information of the video. Furthermore, it can preferentially cut out moving parts based on the background information of the video. In this way, by taking the background information of the video into consideration, the cutout accuracy is improved. Some or all of the above-mentioned processing in the cutout unit may be performed using, for example, AI, or may be performed without using AI. For example, the cutout unit can input the background information of the video into AI, and the AI ​​can improve the cutout accuracy based on the background information.

[0069] The cropping unit can improve cropping accuracy based on other video sources during cropping. For example, the cropping unit can improve cropping accuracy by referring to other video sources (e.g., video from adjacent cameras). For example, the cropping unit can improve accuracy by cropping the same scene from multiple viewpoints based on video from adjacent cameras. The cropping unit can also improve cropping accuracy by referencing other video sources and integrating video from different angles. Furthermore, moving parts can be accurately cropped based on video from multiple cameras. This improves cropping accuracy by referring to other video sources. Some or all of the above-described processing in the cropping unit may be performed using, for example, AI, or may be performed without using AI. For example, the cropping unit can input video data from adjacent cameras into AI, which can then integrate data from multiple viewpoints to improve cropping accuracy.

[0070] The report creation unit can analyze the user's emotions and adjust the presentation style of the report based on the analyzed user's emotions. For example, the report creation unit can analyze the user's emotions and adjust the presentation style of the report based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible report can be provided. If the user is relaxed, a report containing detailed information can be provided. Furthermore, if the user is in a hurry, a report that focuses on the main points can be provided. By adjusting the presentation style of the report according to the user's emotions, visibility is improved. The analysis of the user's emotions is performed using techniques such as facial expression analysis, voice analysis, and behavior analysis. Some or all of the above-described processing in the report creation unit may be performed using, for example, AI, or may be performed without AI. For example, the report creation unit can input the user's facial expression data into AI, which then analyzes the user's emotions and adjusts the presentation style of the report.

[0071] The report creation unit can automatically classify the details of the crime and the characteristics of the perpetrator when creating a report and reflect the results in the report. For example, the report creation unit can automatically classify the details of the crime and the characteristics of the perpetrator when creating a report and reflect the results in the report. For example, the report creation unit can automatically classify the details of the crime and reflect the results in the report. The report creation unit can also automatically classify the characteristics of the perpetrator and reflect the results in the report. Furthermore, the details of the crime and the characteristics of the perpetrator can be integrated to create a detailed report. This automatically classifies the details of the crime and the characteristics of the perpetrator, thereby improving the accuracy of the report. Some or all of the above-mentioned processing in the report creation unit may be performed using, or without, AI. For example, the report creation unit can input data on the details of the crime and the characteristics of the perpetrator into AI, which can then automatically classify the data and reflect the results in the report.

[0072] The report creation unit can adjust the report creation algorithm by referring to past report data when creating a report. The report creation unit, for example, adjusts the report creation algorithm by referring to past report data when creating a report. For example, the report creation unit can learn effective report creation methods based on past report data and optimize the algorithm. Also, the report creation unit can analyze patterns of erroneous reports based on past report data and adjust the algorithm to reduce erroneous reports. Furthermore, the report creation accuracy for specific scenes can be improved based on past report data. In this way, by referring to past report data, the accuracy of the report creation algorithm is improved. Some or all of the above-described processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input past report data into AI, which can optimize the report creation algorithm based on the past data.

[0073] The report creation unit can analyze the user's emotions and determine the priority of reports based on the analyzed user's emotions. For example, the report creation unit can analyze the user's emotions and determine the priority of reports based on the analyzed user's emotions. For example, if the user is nervous, important reports can be displayed with priority. Also, if the user is relaxed, detailed reports can be displayed with priority. Furthermore, if the user is in a hurry, reports that focus on the main points can be displayed with priority. By determining the priority of reports based on the user's emotions, important information can be provided quickly. The analysis of the user's emotions can be performed using techniques such as facial expression analysis, voice analysis, and behavior analysis. Some or all of the above-described processing in the report creation unit can be performed using, for example, AI, or without AI. For example, the report creation unit can input the user's facial expression data into AI, which then analyzes the user's emotions and determines the priority of reports.

[0074] The report creation unit can improve the accuracy of the report by referring to other data sources when creating a report. For example, the report creation unit can improve the accuracy of the report by referring to other data sources (e.g., police databases) when creating a report. For example, the report creation unit can refer to the police database to describe the characteristics of the perpetrator in detail. It can also refer to other data sources to supplement the details of the crime. Furthermore, it can analyze past crime patterns based on the police database to improve the accuracy of the report. In this way, the accuracy of the report is improved by referring to other data sources. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input the police database into AI, which analyzes the data to improve the accuracy of the report.

[0075] The report creation unit can adjust the content of the report based on the time and location of the crime when creating the report. The report creation unit, for example, customizes the content of the report based on the time and location of the crime when creating the report. For example, based on the time of the crime, it can describe crime patterns that tend to occur during specific time periods. It can also describe crime patterns that tend to occur in specific locations based on the location of the crime. Furthermore, it can integrate the time and location of the crime to create a detailed report. In this way, by customizing the content of the report based on the time and location of the crime, it is possible to provide more detailed information. Some or all of the above-mentioned processing in the report creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the report creation unit can input data on the time and location of the crime into AI, which can analyze the data and customize the content of the report. === Hard Collateral 1-1 === Each of the multiple elements including the detection unit, clipping unit, and report creation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit detects a person engaging in suspicious behavior using the camera 42 or microphone 38B of the smart device 14, and the person is analyzed by the control unit 46A. The clipping unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and clips out the necessary video based on the detected information. The report creation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and creates a report identifying the perpetrator based on the clipped video. === Hard Collateral 1-2 === Each of the multiple elements including the detection unit, clipping unit, and report creation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit detects a person engaging in suspicious behavior using the camera 42 and microphone 238 of the smart glasses 214, and the person is analyzed by the control unit 46A. The clipping unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and clips out the necessary video based on the detected information. The report creation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and creates a report identifying the perpetrator based on the clipped video. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, clipping unit, and report creation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit detects a person behaving suspiciously using the camera 42 or microphone 238 of the headset type terminal 314, and the person is analyzed by the control unit 46A. The clipping unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and clips out the necessary video based on the detected information. The report creation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and creates a report identifying the perpetrator based on the clipped video. === Hard Collateral 1-4 === Each of the multiple elements including the detection unit, clipping unit, and report creation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit detects a person behaving suspiciously using the camera 42 or microphone 238 of the robot 414, and the person is analyzed by the control unit 46A. The clipping unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and clips out the necessary video based on the detected information. The report creation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and creates a report identifying the perpetrator based on the clipped video.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The detection unit can analyze a user's purchasing history and adjust the detection criteria for detecting people behaving suspiciously based on the analyzed purchasing history. For example, if a user who frequently purchases a particular product picks up that product, the detection criteria can be determined to be normal behavior and the detection criteria can be relaxed. Also, if a user visiting a store for the first time picks up an expensive product, the detection criteria can be tightened and the behavior can be determined to be suspicious. Furthermore, the detection criteria can be dynamically adjusted based on the behavior of users who tend to purchase specific products during specific time periods. In this way, adjusting the detection criteria based on the user's purchasing history improves detection accuracy.

[0078] The detection unit can analyze the store's congestion status and adjust the detection criteria for people behaving suspiciously based on the analyzed congestion status. For example, during busy times, the detection criteria can be relaxed so that normal crowded behavior is not judged as suspicious. Also, during quiet times, the detection criteria can be tightened so that even slight abnormal behavior is judged as suspicious. Furthermore, if a specific area is crowded, the detection criteria for that area can be dynamically adjusted. In this way, by adjusting the detection criteria based on the store's congestion status, detection accuracy can be improved.

[0079] The clipping unit can analyze a user's purchase history and adjust the range of clipping based on the analyzed purchase history. For example, it can clip a wide range of the behavior of a user who frequently purchases a specific product to provide detailed information. It can also clip only the minimum necessary range of the behavior of a user visiting the store for the first time to provide concise information. Furthermore, it can dynamically adjust the range of clipping based on the behavior of a user who tends to purchase specific products during a specific time period. This allows the necessary information to be provided efficiently by adjusting the range of clipping based on the user's purchase history.

[0080] The report creation unit can analyze a user's purchasing history and adjust the contents of the report based on the analyzed purchasing history. For example, the behavior of users who frequently purchase specific products can be described in detail to improve the accuracy of the report. It can also provide a report that covers the main points by briefly describing the behavior of users who visit the store for the first time. Furthermore, it can dynamically adjust the contents of the report based on the behavior of users who tend to purchase specific products during specific time periods. This allows detailed information to be provided by adjusting the contents of the report based on the user's purchasing history.

[0081] The detection unit can analyze the user's emotions and adjust the detection criteria for detecting people behaving suspiciously based on the analyzed user emotions. For example, if the user is nervous, the detection criteria can be tightened to determine more movements and behavior patterns as suspicious. Also, if the user is relaxed, the detection criteria can be loosened to prevent normal movements from being determined as suspicious. Furthermore, if the user is impatient, the detection criteria can be quickly adjusted to instantly detect suspicious people. In this way, by adjusting the detection criteria according to the user's emotions, detection accuracy can be improved.

[0082] The detection unit can analyze the user's emotions and adjust the display method of the detection results based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, visibility can be improved by adjusting the display method according to the user's emotions.

[0083] The cropping unit can analyze the user's emotions and adjust the crop range based on the analyzed user's emotions. For example, if the user is nervous, it can crop a wide range of video to provide detailed information. If the user is relaxed, it can crop only the minimum necessary range to provide concise information. Furthermore, if the user is in a hurry, it can quickly crop only important parts so that they can be checked immediately. In this way, by adjusting the crop range according to the user's emotions, it is possible to efficiently provide necessary information.

[0084] The report creation unit can analyze the user's emotions and adjust the way the report is presented based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible report can be provided. If the user is relaxed, a report containing detailed information can be provided. Furthermore, if the user is in a hurry, a report that focuses on the main points can be provided. In this way, by adjusting the way the report is presented according to the user's emotions, visibility can be improved.

[0085] The report creation unit can analyze the user's emotions and determine the priority of reports based on the analyzed user's emotions. For example, if the user is nervous, important reports can be displayed with priority. If the user is relaxed, detailed reports can be displayed with priority. Furthermore, if the user is in a hurry, reports that focus on the main points can be displayed with priority. In this way, by determining the priority of reports according to the user's emotions, important information can be provided quickly.

[0086] During detection, the detection unit can improve detection accuracy by combining data from other sensors. For example, it can detect abnormal sounds based on data from an audio sensor and integrate the data with video to identify a suspicious person. It can also detect abnormal temperature changes based on data from a temperature sensor and integrate the data with video to identify a suspicious person. It can also integrate data from other sensors to simultaneously detect multiple abnormal patterns and identify a suspicious person. In this way, by integrating data from other sensors, detection accuracy can be improved.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The detection unit analyzes security camera footage to detect individuals behaving suspiciously. For example, the detection unit detects suspicious individuals based on specific movements or patterns of behavior. The detection unit can identify suspicious individuals by analyzing, for example, walking patterns, hand movements, and gaze direction. The detection unit can also use AI to analyze footage in real time and quickly detect suspicious individuals. For example, AI can track the movements of individuals in the footage and detect abnormal behavior. Step 2: The cutout unit cuts out the necessary video based on the information detected by the detection unit. For example, the cutout unit cuts out only the necessary parts based on the video of a detected suspicious person. The cutout unit can automatically cut out parts that include a specific time range or specific actions, for example. The cutout unit can also use AI to analyze the video and efficiently cut out the necessary parts. For example, AI can automatically extract and cut out important scenes within the video. Step 3: The report creation unit creates a report to identify the perpetrator based on the video clipped by the clipping unit. For example, the report creation unit creates a report that clarifies the details of the crime and the characteristics of the perpetrator based on the clipped video. The report creation unit can create a detailed report that includes information such as the time and location of the crime and the appearance of the perpetrator. The report creation unit can also use AI to analyze the video and extract important information for identifying the perpetrator. For example, AI can analyze the characteristics of people in the video and provide information that is useful for identifying the perpetrator.

[0089] 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.

[0090] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a detection unit that analyzes security camera footage and detects people behaving suspiciously; a cutout unit that cuts out a necessary video based on the information detected by the detection unit; a report creation unit that creates a report on identifying a criminal based on the video clipped by the clipping unit. A system characterized by:

2. The detection unit Analyze security camera footage to detect suspicious behavior based on specific movements or patterns of behavior 2. The system of claim 1.

3. The cutout portion is Cut out the necessary parts based on the video of the person exhibiting suspicious behavior.

2. The system of claim 1.

4. The report creation unit Based on the extracted footage, a specific report will be prepared to identify the perpetrator.

2. The system of claim 1.

5. The detection unit Analyze user emotions and adjust the criteria for detecting people exhibiting suspicious behavior based on the analyzed user emotions.

2. The system of claim 1.

6. The detection unit When analyzing security camera footage, change the detection criteria based on specific times of day or days of the week.

2. The system of claim 1.

7. The detection unit Upon detection, the detection algorithm is adjusted by referencing past detection history.

2. The system of claim 1.

8. The detection unit Analyze user emotions and adjust the display method of detection results based on the analyzed user emotions 2. The system of claim 1.

Images