System

Abstract

An object of a system according to an embodiment is to extract a feature of a suspicious person from a video of a security camera and quickly and accurately report the suspicious person.SOLUTION: A system includes an image / video analysis unit, a feature extraction unit, a text conversion unit, and a reporting unit. The image / video analysis unit analyzes the video of the security camera. The feature extraction unit extracts a feature of the suspicious person from the video analyzed by the image / video analysis unit. The text conversion unit converts the feature extracted by the feature extraction unit into text. The reporting unit transmits the text information generated by the text conversion unit to a facility manager or a police station.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately extract the characteristics of suspicious individuals from security camera footage and report them.

[0005] The system according to the embodiment aims to extract the characteristics of a suspicious person from security camera footage and report the same quickly and accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes an image / video analysis unit, a feature extraction unit, a text conversion unit, and a reporting unit. The image / video analysis unit analyzes security camera footage. The feature extraction unit extracts features of a suspicious individual from the footage analyzed by the image / video analysis unit. The text conversion unit converts the features extracted by the feature extraction unit into text. The reporting unit transmits the text information generated by the text conversion unit to the facility manager or the police station. [Effects of the Invention]

[0007] The system according to the embodiment can extract the characteristics of a suspicious person from security camera footage and report the same quickly and accurately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The security system according to an embodiment of the present invention converts the characteristics of images and videos of suspicious individuals captured by security cameras into objective and specific text and reports that information to facility managers and nearby police stations in real time. This allows the security system to quickly and accurately report information about suspicious individuals, thereby improving the safety of facilities.

[0029] A security system according to an embodiment includes an image / video analysis unit, a feature extraction unit, a text conversion unit, and a reporting unit. The image / video analysis unit analyzes security camera footage. For example, the image / video analysis unit uses a generation AI to analyze the security camera footage and analyze the facial features, clothing, height, body type, and belongings of a suspicious individual. The feature extraction unit extracts the features of a suspicious individual from the footage analyzed by the image / video analysis unit. For example, the feature extraction unit uses a generation AI to extract the facial features, clothing, height, body type, and belongings of the suspicious individual. The text conversion unit converts the features extracted by the feature extraction unit into text. For example, the text conversion unit uses a generation AI to convert the text into text such as "The suspicious individual is a middle-aged man wearing a black jacket, approximately 180 cm tall, and carrying a black backpack." The reporting unit transmits the text information generated by the text conversion unit to a facility manager or a police station. For example, the reporting unit uses a generation AI to automatically transmit the text information via email or a messenger app. As a result, the security system according to the embodiment can quickly and accurately report information about suspicious individuals. For example, a facility manager can grasp information about suspicious individuals in real time and respond quickly. In addition, police stations can quickly receive information about suspicious individuals and respond appropriately.

[0030] The feature extraction unit can analyze the movement patterns of a suspicious individual and detect whether they are specific movements. For example, the feature extraction unit uses generative AI to analyze the movement patterns of a suspicious individual from security camera footage. For example, it detects movements such as frequently looking around the surroundings or movements searching for specific objects, and extracts these movements as features. This makes it possible to analyze the movement patterns of a suspicious individual and detect specific movements.

[0031] The feature extraction unit can integrate multiple camera viewpoints and generate a 3D model for extracting detailed features of a suspicious individual. The feature extraction unit can, for example, use generative AI to integrate footage from multiple camera viewpoints and generate a 3D model. For example, it can combine footage shot from different angles to extract detailed features of a suspicious individual. This makes it possible to integrate multiple camera viewpoints and generate a 3D model to extract detailed features of a suspicious individual.

[0032] The image and video analysis unit enhances image and video analysis at night or in bad weather, and can extract features with high accuracy even in low-light or noisy environments. The image and video analysis unit uses generative AI to analyze images and videos with high accuracy, even in low-light environments at night. For example, it can extract facial and clothing features of suspicious individuals even in the dark. This enhances image and video analysis at night or in bad weather, and can extract features with high accuracy, even in low-light or noisy environments.

[0033] The image and video analysis unit can simultaneously analyze audio data and extract the characteristics of the suspicious individual's voice or what they are saying. For example, the image and video analysis unit can use generative AI to simultaneously analyze audio data along with images and videos to extract the characteristics of the suspicious individual's voice. For example, it can analyze the tone of voice and speaking style. This allows the audio data to be simultaneously analyzed and the characteristics of the suspicious individual's voice and what they are saying to be extracted.

[0034] The text conversion unit can automatically translate the features into multiple languages, making it possible to respond to international reports. For example, the text conversion unit uses generative AI to automatically translate the extracted features into multiple languages, such as English and French. For example, the feature "middle-aged man wearing a black jacket, approximately 180 cm tall, carrying a black backpack" can be translated into each language. This allows the extracted features to be automatically translated into multiple languages, making it possible to respond to international reports.

[0035] The text conversion unit can add more specific and detailed information to the generated text by referring to past report histories and similar cases. The text conversion unit, for example, uses a generation AI to add more specific and detailed information to the generated text by referring to past report histories and similar cases. For example, it can describe the characteristics of a suspicious person in detail by comparing them with past cases. This allows more specific and detailed information to be added to the generated text by referring to past report histories and similar cases.

[0036] The text conversion unit converts text information into audio format, making it possible to also make audio reports. The text conversion unit converts extracted features from text information into audio format, for example, using generative AI. For example, it may report information such as "The suspicious person is a middle-aged man wearing a black jacket, is about 180 cm tall, and is carrying a black backpack" by audio. This allows the text information to be converted into audio format, making it possible to also make audio reports.

[0037] The text conversion unit adds geographic information to the generated text, making it possible to specifically indicate the location of the suspicious person. For example, the text conversion unit uses a generation AI to add the location information of the suspicious person to the generated text. For example, it describes the specific location information in the form, "The suspicious person is near the north entrance of the facility." This allows the geographic information to be added to the generated text, making it possible to specifically indicate the location information of the suspicious person.

[0038] The reporting department can customize the content of the report according to the recipient's position and authority, and provide appropriate information. The reporting department can use generation AI, for example, to customize the content of the report according to the recipient's position. For example, detailed information can be provided to facility managers, and concise information can be provided to security guards. This allows the content of the report to be customized according to the recipient's position and authority, and appropriate information can be provided.

[0039] The reporting unit can automatically determine the urgency of the report content and select the reporting means according to the urgency. The reporting unit can add a function to automatically determine the urgency of the report content, for example, using generation AI. For example, if the behavior of a suspicious person is judged to be dangerous, reporting by telephone will be prioritized. This makes it possible to automatically determine the urgency of the report content and select the reporting means according to the urgency.

[0040] The reporting unit can add thumbnails of images or videos to the report content, thereby visually conveying information. The reporting unit can add thumbnails of images or videos to the report content, for example, using generation AI. For example, the report content can include an image of the face or clothing of a suspicious person. This allows thumbnails of images or videos to be added to the report content, thereby visually conveying information.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The security system also includes an environmental sensor unit. The environmental sensor unit acquires environmental data such as temperature, humidity, and illuminance, and can analyze the behavior of suspicious individuals based on this data. For example, it can identify the behavioral patterns of suspicious individuals at night or in bad weather, and detect unusual behavior. It can also analyze changes in the behavior of suspicious individuals based on the environmental data, enabling more accurate reporting. This allows the system to utilize environmental data to analyze the behavior of suspicious individuals in more detail and enhance the content of reports.

[0043] The security system also includes a biometric authentication unit. The biometric authentication unit can acquire biometric information such as fingerprints and irises of suspicious individuals and identify them based on this information. For example, it can scan fingerprints and irises in synchronization with security camera footage to confirm the identity of the suspicious individual. It can also compare the information with a past database to determine whether the suspicious individual is someone who has been reported previously. This makes it possible to use biometric information to identify suspicious individuals and enrich the content of reports.

[0044] The security system also includes a behavior prediction unit. The behavior prediction unit can predict future behavior based on the suspicious individual's past behavior data. For example, if a suspicious individual frequently appears in a specific area, the unit can predict future behavior in that area and strengthen vigilance in advance. It is also possible to analyze the suspicious individual's behavior patterns based on past data and detect abnormal behavior early. This makes it possible to use behavior prediction to grasp the suspicious individual's behavior in advance and improve the content of reports.

[0045] The security system also includes a drone monitoring unit. The drone monitoring unit uses drones to monitor a wide area and track the movements of suspicious individuals in real time. For example, the drone can automatically track a suspicious individual and transmit the video footage to the security system. It is also possible to analyze the suspicious individual's detailed characteristics using the drone's onboard cameras and sensors. This allows the drone to monitor a wide area, quickly and accurately grasp information about the suspicious individual, and enrich the content of reports.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The image and video analysis unit analyzes security camera footage. For example, the image and video analysis unit uses generative AI to analyze security camera footage and identify the suspicious individual's facial features, clothing, height, body type, and belongings. Step 2: The feature extraction unit extracts the suspicious individual's features from the video analyzed by the image and video analysis unit. For example, the feature extraction unit uses generative AI to extract the suspicious individual's facial features, clothing, height, body type, belongings, etc. Step 3: The text conversion unit converts the features extracted by the feature extraction unit into text. For example, the text conversion unit uses a generative AI to generate text such as "The suspicious person is a middle-aged man wearing a black jacket, is about 180 cm tall, and is carrying a black backpack." Step 4: The reporting department sends the text information generated by the text conversion department to the facility manager or police station. For example, the reporting department can use the generation AI to automatically send the text information via email or a messenger app.

[0048] (Example 2) The security system according to an embodiment of the present invention converts the characteristics of images and videos of suspicious individuals captured by security cameras into objective and specific text and reports that information to facility managers and nearby police stations in real time. This allows the security system to quickly and accurately report information about suspicious individuals, thereby improving the safety of facilities.

[0049] A security system according to an embodiment includes an image / video analysis unit, a feature extraction unit, a text conversion unit, and a reporting unit. The image / video analysis unit analyzes security camera footage. For example, the image / video analysis unit uses a generation AI to analyze the security camera footage and analyze the facial features, clothing, height, body type, and belongings of a suspicious individual. The feature extraction unit extracts the features of a suspicious individual from the footage analyzed by the image / video analysis unit. For example, the feature extraction unit uses a generation AI to extract the facial features, clothing, height, body type, and belongings of the suspicious individual. The text conversion unit converts the features extracted by the feature extraction unit into text. For example, the text conversion unit uses a generation AI to convert the text into text such as "The suspicious individual is a middle-aged man wearing a black jacket, approximately 180 cm tall, and carrying a black backpack." The reporting unit transmits the text information generated by the text conversion unit to a facility manager or a police station. For example, the reporting unit uses a generation AI to automatically transmit the text information via email or a messenger app. As a result, the security system according to the embodiment can quickly and accurately report information about suspicious individuals. For example, a facility manager can grasp information about suspicious individuals in real time and respond quickly. In addition, police stations can quickly receive information about suspicious individuals and respond appropriately.

[0050] The feature extraction unit can analyze the movement patterns of a suspicious individual and detect whether they are specific movements. For example, the feature extraction unit uses generative AI to analyze the movement patterns of a suspicious individual from security camera footage. For example, it detects movements such as frequently looking around the surroundings or movements searching for specific objects, and extracts these movements as features. This makes it possible to analyze the movement patterns of a suspicious individual and detect specific movements.

[0051] The feature extraction unit can integrate multiple camera viewpoints and generate a 3D model for extracting detailed features of a suspicious individual. The feature extraction unit can, for example, use generative AI to integrate footage from multiple camera viewpoints and generate a 3D model. For example, it can combine footage shot from different angles to extract detailed features of a suspicious individual. This makes it possible to integrate multiple camera viewpoints and generate a 3D model to extract detailed features of a suspicious individual.

[0052] The feature extraction unit can infer the emotional state of a suspicious individual from their facial expressions or actions, and extract that information as features. The feature extraction unit, for example, uses generative AI to analyze the facial expressions of a suspicious individual and infer their emotional state. For example, it detects facial expressions of tension or anxiety and extracts that information as features. This makes it possible to infer the emotional state of a suspicious individual from their facial expressions and actions, and extract that information as features.

[0053] The image and video analysis unit enhances image and video analysis at night or in bad weather, and can extract features with high accuracy even in low-light or noisy environments. The image and video analysis unit uses generative AI to analyze images and videos with high accuracy, even in low-light environments at night. For example, it can extract facial and clothing features of suspicious individuals even in the dark. This enhances image and video analysis at night or in bad weather, and can extract features with high accuracy, even in low-light or noisy environments.

[0054] The image and video analysis unit can simultaneously analyze audio data and extract the characteristics of the suspicious individual's voice or what they are saying. For example, the image and video analysis unit can use generative AI to simultaneously analyze audio data along with images and videos to extract the characteristics of the suspicious individual's voice. For example, it can analyze the tone of voice and speaking style. This allows the audio data to be simultaneously analyzed and the characteristics of the suspicious individual's voice and what they are saying to be extracted.

[0055] The feature extraction unit can analyze the emotional reactions of surrounding people and evaluate the impact of the suspicious person's behavior on the surroundings. The feature extraction unit, for example, uses an emotion estimation function to analyze the facial expressions of surrounding people and evaluate the emotional reactions. For example, it detects facial expressions of anxiety or fear and evaluates the impact of the suspicious person's behavior on the surroundings based on that information. This makes it possible to analyze the emotional reactions of surrounding people and evaluate the impact of the suspicious person's behavior on the surroundings.

[0056] The text conversion unit can automatically translate the features into multiple languages, making it possible to respond to international reports. For example, the text conversion unit uses generative AI to automatically translate the extracted features into multiple languages, such as English and French. For example, the feature "middle-aged man wearing a black jacket, approximately 180 cm tall, carrying a black backpack" can be translated into each language. This allows the extracted features to be automatically translated into multiple languages, making it possible to respond to international reports.

[0057] The text conversion unit can add more specific and detailed information to the generated text by referring to past report histories and similar cases. The text conversion unit, for example, uses a generation AI to add more specific and detailed information to the generated text by referring to past report histories and similar cases. For example, it can describe the characteristics of a suspicious person in detail by comparing them with past cases. This allows more specific and detailed information to be added to the generated text by referring to past report histories and similar cases.

[0058] The text conversion unit uses the emotion estimation function to reflect the emotional state of the suspicious person when generating text, allowing for a more realistic description of the situation. The text conversion unit, for example, uses the emotion estimation function to reflect the emotional state of the suspicious person when generating text. For example, the emotional state is described in the form, "The suspicious person appears nervous and is frequently looking around." This allows for a more realistic description of the situation to be generated by reflecting the emotional state of the suspicious person when generating text.

[0059] The text conversion unit converts text information into audio format, making it possible to also make audio reports. The text conversion unit converts extracted features from text information into audio format, for example, using generative AI. For example, it may report information such as "The suspicious person is a middle-aged man wearing a black jacket, is about 180 cm tall, and is carrying a black backpack" by audio. This allows the text information to be converted into audio format, making it possible to also make audio reports.

[0060] The text conversion unit adds geographic information to the generated text, making it possible to specifically indicate the location of the suspicious person. For example, the text conversion unit uses a generation AI to add the location information of the suspicious person to the generated text. For example, it describes the specific location information in the form, "The suspicious person is near the north entrance of the facility." This allows the geographic information to be added to the generated text, making it possible to specifically indicate the location information of the suspicious person.

[0061] The text conversion unit can use the emotion estimation function to predict the receiver's emotional reaction to the message content and select the most appropriate expression. The text conversion unit can, for example, use the emotion estimation function to predict the receiver's emotional reaction to the message content and select the most appropriate expression. For example, a calm expression can be selected so that the receiver does not feel nervous. In this way, the receiver's emotional reaction to the message content can be predicted and the most appropriate expression can be selected.

[0062] The reporting department can customize the content of the report according to the recipient's position and authority, and provide appropriate information. The reporting department can use generation AI, for example, to customize the content of the report according to the recipient's position. For example, detailed information can be provided to facility managers, and concise information can be provided to security guards. This allows the content of the report to be customized according to the recipient's position and authority, and appropriate information can be provided.

[0063] The reporting unit can automatically determine the urgency of the report content and select the reporting means according to the urgency. The reporting unit can add a function to automatically determine the urgency of the report content, for example, using generation AI. For example, if the behavior of a suspicious person is judged to be dangerous, reporting by telephone will be prioritized. This makes it possible to automatically determine the urgency of the report content and select the reporting means according to the urgency.

[0064] The reporting unit uses the emotion estimation function to monitor the receiver's emotional response to the report content in real time, and can make a follow-up report as necessary. The reporting unit, for example, uses the emotion estimation function to monitor the receiver's emotional response to the report content in real time. For example, if the receiver is nervous, a follow-up report is made. This makes it possible to monitor the receiver's emotional response to the report content in real time, and can make a follow-up report as necessary.

[0065] The reporting unit can add thumbnails of images or videos to the report content, thereby visually conveying information. The reporting unit can add thumbnails of images or videos to the report content, for example, using generation AI. For example, the report content can include an image of the face or clothing of a suspicious person. This allows thumbnails of images or videos to be added to the report content, thereby visually conveying information.

[0066] The reporting unit uses the emotion estimation function to analyze the emotional reaction of the recipient to the content of the report, and can use the results to improve the content of the report. The reporting unit, for example, uses the emotion estimation function to analyze the emotional reaction of the recipient to the content of the report. For example, if the recipient is nervous, the content of the report is improved. In this way, the analysis of the emotional reaction of the recipient to the content of the report can be used to improve the content of the report.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The security system also includes a voice recognition unit. The voice recognition unit analyzes surrounding sounds in sync with security camera footage and can extract the content of a suspicious individual's speech and voice characteristics. For example, if a suspicious individual utters a specific phrase, the phrase can be converted into text and included in the report. It is also possible to analyze the tone of voice and speaking characteristics to estimate the suspicious individual's emotional state. This makes it possible to use voice data to obtain more detailed information about the suspicious individual and enrich the report content.

[0069] The security system also includes an environmental sensor unit. The environmental sensor unit acquires environmental data such as temperature, humidity, and illuminance, and can analyze the behavior of suspicious individuals based on this data. For example, it can identify the behavioral patterns of suspicious individuals at night or in bad weather, and detect unusual behavior. It can also analyze changes in the behavior of suspicious individuals based on the environmental data, enabling more accurate reporting. This allows the system to utilize environmental data to analyze the behavior of suspicious individuals in more detail and enhance the content of reports.

[0070] The security system also includes a biometric authentication unit. The biometric authentication unit can acquire biometric information such as fingerprints and irises of suspicious individuals and identify them based on this information. For example, it can scan fingerprints and irises in synchronization with security camera footage to confirm the identity of the suspicious individual. It can also compare the information with a past database to determine whether the suspicious individual is someone who has been reported previously. This makes it possible to use biometric information to identify suspicious individuals and enrich the content of reports.

[0071] The security system also includes a behavior prediction unit. The behavior prediction unit can predict future behavior based on the suspicious individual's past behavior data. For example, if a suspicious individual frequently appears in a specific area, the unit can predict future behavior in that area and strengthen vigilance in advance. It is also possible to analyze the suspicious individual's behavior patterns based on past data and detect abnormal behavior early. This makes it possible to use behavior prediction to grasp the suspicious individual's behavior in advance and improve the content of reports.

[0072] The security system also includes a drone monitoring unit. The drone monitoring unit uses drones to monitor a wide area and track the movements of suspicious individuals in real time. For example, the drone can automatically track a suspicious individual and transmit the video footage to the security system. It is also possible to analyze the suspicious individual's detailed characteristics using the drone's onboard cameras and sensors. This allows the drone to monitor a wide area, quickly and accurately grasp information about the suspicious individual, and enrich the content of reports.

[0073] The crime prevention system can also use its emotion estimation function to predict the recipient's emotional response to the content of the call and select the most appropriate expression. For example, it can select calm expressions to prevent the recipient from feeling nervous. It can also emphasize information with a high level of urgency so that the recipient can respond quickly. This allows the system to predict the recipient's emotional response to the content of the call and select the most appropriate expression to help the recipient respond appropriately.

[0074] The security system can also use its emotion estimation function to monitor the recipient's emotional response to the report in real time and make a follow-up report as necessary. For example, if the recipient is nervous, a follow-up report can be made to alleviate the recipient's anxiety. It can also provide additional information to help the recipient respond appropriately. This allows the system to monitor the recipient's emotional response to the report in real time and make a follow-up report as necessary to help the recipient respond appropriately.

[0075] The crime prevention system can further use its emotion estimation function to analyze the recipient's emotional response to the report content and use the information to improve the report. For example, if the recipient is nervous, the report content can be improved to allow the recipient to respond calmly. It can also optimize the wording of the report content and the method of providing information based on the recipient's emotional response. This allows the system to analyze the recipient's emotional response to the report content and use the information to improve the report content, thereby supporting the recipient to respond appropriately.

[0076] The crime prevention system can also use its emotion estimation function to predict the recipient's emotional response to the content of the call and select the most appropriate expression. For example, it can select calm expressions to prevent the recipient from feeling nervous. It can also emphasize information with a high level of urgency so that the recipient can respond quickly. This allows the system to predict the recipient's emotional response to the content of the call and select the most appropriate expression to help the recipient respond appropriately.

[0077] The security system can also use its emotion estimation function to monitor the recipient's emotional response to the report in real time and make a follow-up report as necessary. For example, if the recipient is nervous, a follow-up report can be made to alleviate the recipient's anxiety. It can also provide additional information to help the recipient respond appropriately. This allows the system to monitor the recipient's emotional response to the report in real time and make a follow-up report as necessary to help the recipient respond appropriately.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The image and video analysis unit analyzes security camera footage. For example, the image and video analysis unit uses generative AI to analyze security camera footage and identify the suspicious individual's facial features, clothing, height, body type, and belongings. Step 2: The feature extraction unit extracts the suspicious individual's features from the video analyzed by the image and video analysis unit. For example, the feature extraction unit uses generative AI to extract the suspicious individual's facial features, clothing, height, body type, belongings, etc. Step 3: The text conversion unit converts the features extracted by the feature extraction unit into text. For example, the text conversion unit uses a generative AI to generate text such as "The suspicious person is a middle-aged man wearing a black jacket, is about 180 cm tall, and is carrying a black backpack." Step 4: The reporting department sends the text information generated by the text conversion department to the facility manager or police station. For example, the reporting department can use the generation AI to automatically send the text information via email or a messenger app.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An image and video analysis unit that analyzes security camera footage, a feature extraction unit that extracts features of a suspicious person from the video analyzed by the image / video analysis unit; a text conversion unit that converts the features extracted by the feature extraction unit into text; a reporting unit that transmits the text information generated by the text conversion unit to a facility manager or a police station. A system characterized by:

2. The feature extraction unit Integrating multiple camera views to generate a 3D model for extracting detailed features of the suspicious individual.

2. The system of claim 1.

3. The image / video analysis unit Enhance image and video analysis at night or in bad weather, extracting the above features with high accuracy even in low-light or noisy environments.

2. The system of claim 1.

4. The text conversion unit Automatically translating the features into multiple languages ​​to accommodate international notifications 2. The system of claim 1.

5. The reporting unit Customize the report content based on the recipient's role and authority, and provide appropriate information 2. The system of claim 1.

6. The feature extraction unit The emotional state of the suspicious person is estimated from the facial expression or movement of the suspicious person, and the information is extracted as a feature.

2. The system of claim 1.

7. The text conversion unit The emotional state of the suspicious person is reflected in the text generated, providing a more realistic description of the situation.

2. The system of claim 1.

8. The reporting unit Monitor recipients' emotional reactions to reports in real time and follow up as needed 2. The system of claim 1.

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