system

Abstract

The system according to this embodiment aims to achieve crime prevention while protecting privacy. [Solution] The system according to the embodiment comprises an avatar display unit, a behavior analysis unit, a data integration unit, and an information disclosure unit. The avatar display unit represents a person as an avatar. The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit. The data integration unit integrates the data analyzed by the behavior analysis unit with statistical data. The information disclosure unit discloses information based on the data integrated by the data integration unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to achieve both privacy protection and crime prevention, and there is room for improvement.

[0005] The system according to the embodiment aims to achieve crime prevention while protecting privacy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an avatar display unit, a behavior analysis unit, a data integration unit, and an information disclosure unit. The avatar display unit represents a person using an avatar. The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit. The data integration unit integrates the data analyzed by the behavior analysis unit with statistical data. The information disclosure unit discloses information based on the data integrated by the data integration unit. [Effects of the Invention]

[0007] The system according to this embodiment can achieve crime prevention while protecting privacy. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The avatar AI camera system according to an embodiment of the present invention is a system that balances privacy protection and crime prevention in private zones. This avatar AI camera system represents people as avatars and performs behavioral analysis using image language generation AI while protecting privacy. In this case, by combining this with statistical data and pedestrian flow data, it can identify areas and places with low pedestrian flow and efficiently contribute to crime deterrence. When necessary, information disclosure by court order is possible, aiming to protect privacy and improve crime arrest rates. This system makes it possible to enhance the safety of society as a whole without infringing on individual privacy, and provides a comfortable and secure living environment. For example, the avatar AI camera system represents people as avatars. This protects privacy and prevents individuals from being identified in surveillance camera footage. For example, even in private zones such as public restrooms and changing rooms, privacy can be protected by displaying people as avatars. Next, the avatar AI camera system performs behavioral analysis using image language generation AI. The AI ​​analyzes people's behavior from camera footage and detects abnormal behavior. For example, it can detect people who stay in the same place for a long time or people who are moving suspiciously and issue a warning. This enables crime prevention and early detection. Furthermore, the avatar AI camera system, when combined with statistical data and pedestrian flow data, identifies areas and locations with low foot traffic. This allows for the identification of crime-prone locations and the implementation of more efficient crime prevention measures. For example, cameras can be strategically placed in parks and parking lots with low foot traffic at night to enhance surveillance. When necessary, the avatar AI camera system can disclose information by court order. This allows for the deactivation of the avatar display and the provision of actual footage in the event of a crime. This improves the crime detection rate while protecting privacy. This system makes it possible to enhance the safety of society as a whole without infringing on individual privacy. For example, it allows for safer use of private zones such as public restrooms and changing rooms. It also enables crime prevention and early detection, providing a comfortable and safe living environment.This allows avatar AI camera systems to effectively contribute to crime deterrence while protecting privacy.

[0029] The avatar AI camera system according to this embodiment comprises an avatar display unit, a behavior analysis unit, a data integration unit, and an information disclosure unit. The avatar display unit represents people as avatars. The avatar display unit can represent people using, for example, 2D avatars, 3D avatars, or real-time generated avatars. The avatar display unit can display people as avatars even in private zones such as public restrooms or changing rooms. This protects privacy while preventing the identification of individuals in surveillance camera footage. The behavior analysis unit analyzes the behavior of the avatars displayed by the avatar display unit. The behavior analysis unit can, for example, analyze a person's behavior from camera footage and detect abnormal behavior. The behavior analysis unit can, for example, detect a person who stays in the same place for a long time or a person who makes suspicious movements and issue a warning. The behavior analysis unit can analyze behavior using, for example, a motion recognition algorithm or a behavior pattern classification method. The data integration unit integrates the data analyzed by the behavior analysis unit with statistical data. The data integration unit can, for example, identify areas or locations with low pedestrian traffic by combining statistical data and pedestrian traffic data. The data integration unit can perform integration using, for example, demographic data and crime rate data. The data integration unit can collect and integrate pedestrian traffic data using, for example, GPS data and traffic volume data. The information disclosure unit discloses information based on the data integrated by the data integration unit. The information disclosure unit can, for example, enable information disclosure by court order. The information disclosure unit can disclose information based on, for example, the type of information to be disclosed and the timing of disclosure. The information disclosure unit can, for example, disable the avatar display and provide actual video footage. This allows the avatar AI camera system according to the embodiment to efficiently contribute to crime prevention while protecting privacy. Some or all of the above-described processing in the information disclosure unit may be performed using, for example, AI, or not using AI. For example, the information disclosure unit can disclose information using an AI model that disables the avatar display and provides actual video footage based on a court order.

[0030] The avatar display unit represents people using avatars. The avatar display unit can represent people using, for example, 2D avatars, 3D avatars, and real-time generated avatars. Specifically, 2D avatars represent people using flat characters or silhouettes, while 3D avatars enable more realistic representations using three-dimensional characters. Real-time generated avatars are instantly generated based on camera footage, reflecting a person's movements and expressions in real time. This allows the avatar display unit to flexibly generate and display avatars according to various situations and applications. Furthermore, the avatar display unit can display people as avatars even in private zones such as public restrooms and changing rooms. This protects privacy while preventing the identification of individuals in surveillance camera footage. For example, in private zones, abstracting the appearance and movements of avatars can prevent individual identification while maintaining necessary surveillance functions. The avatar display unit can use AI technology to analyze a person's movements and expressions and reflect them in the avatar. For example, using deep learning-based image recognition technology, the system can detect a person's posture and movements from camera footage and generate an avatar based on that. Furthermore, facial recognition technology can be used to analyze a person's facial expressions in real time and reflect them in the avatar's expressions. This allows the avatar display unit to generate and display more natural and realistic avatars. In addition, the avatar display unit can customize the avatar's appearance and movements to protect user privacy. For example, by setting the desired appearance and movements, the system can display an avatar that aligns with the user's intentions while preventing personal identification. This allows the avatar display unit to provide flexible and effective monitoring while maximizing user privacy protection.

[0031] The behavior analysis unit analyzes the behavior of avatars displayed by the avatar display unit. For example, the behavior analysis unit can analyze a person's behavior from camera footage and detect abnormal behavior. Specifically, it utilizes an AI-based motion recognition algorithm to analyze a person's movements and behavioral patterns. For example, it can detect a person who stays in the same place for a long time or a person who is moving suspiciously and issue a warning. The behavior analysis unit can distinguish between normal and abnormal behavior using a behavioral pattern classification method based on deep learning. For example, it can use a model trained on past data to identify normal behavioral patterns and detect behavior that differs from them as abnormal. Furthermore, the behavior analysis unit can analyze behavior in real time and issue a warning immediately if an abnormality is detected. This enables a rapid response and contributes to crime deterrence and early detection. In addition, the behavior analysis unit can integrate and analyze multiple camera videos. For example, it can integrate videos taken from different angles to perform more accurate behavioral analysis. This reduces blind spots and oversights, enabling more accurate surveillance. The behavioral analysis unit can be used not only to detect abnormal behavior, but also to analyze normal behavioral patterns and understand trends. For example, it can analyze people's behavioral patterns at specific times and locations to understand congestion levels and fluctuations in pedestrian flow. This can be used to plan efficient staffing and safety measures. The behavioral analysis unit can utilize AI technology to perform real-time, highly accurate behavioral analysis, improving the overall monitoring capabilities of the system.

[0032] The Data Integration Department integrates data analyzed by the Behavioral Analysis Department with statistical data. For example, the Data Integration Department can combine statistical data and pedestrian flow data to identify areas or locations with low pedestrian traffic. Specifically, it can use demographic data and crime rate data to conduct risk assessments in specific areas. For instance, based on historical crime data, it can identify areas and times prone to crime and focus monitoring efforts. It can also collect and integrate pedestrian flow data using GPS data and traffic volume data. This allows for understanding people's movements in specific locations and times, enabling more efficient monitoring. The Data Integration Department can use AI to analyze this data and integrate multiple data sources to provide more accurate information. For example, deep learning-based data analysis techniques can integrate information from different data sources and identify correlations. This allows the Data Integration Department to provide insights not obtainable from a single data source, improving the overall monitoring capabilities of the system. Furthermore, the Data Integration Department can continuously integrate information based on real-time updated data, enabling it to respond to the latest situations. For example, if traffic volume data or pedestrian flow data is updated in real time, the data integration unit can immediately incorporate the new data and update the integration results. This enables monitoring based on the latest information at all times, supporting quick and appropriate responses. The data integration unit can utilize AI technology to integrate multiple data sources and improve the overall monitoring capabilities of the system.

[0033] The Information Disclosure Department discloses information based on data integrated by the Data Integration Department. The Information Disclosure Department can, for example, disclose information based on court orders. Specifically, it can remove avatar displays and provide actual video footage based on court orders. This allows for the provision of actual video footage while protecting privacy when necessary. The Information Disclosure Department can disclose information based on the type of information to be disclosed and the timing of disclosure. For example, it can selectively disclose only the necessary information depending on a specific case or situation. This allows for the provision of necessary information while protecting privacy to the greatest extent possible. The Information Disclosure Department can use AI to disclose information. For example, it can use an AI model to automate the process of removing avatar displays and providing actual video footage based on court orders. This enables rapid and accurate information disclosure and efficient responses. Furthermore, the Information Disclosure Department can manage and track disclosed information. For example, it can record the history of disclosed information and conduct tracking and audits as needed. This ensures transparency and reliability in the information disclosure process. The Information Disclosure Department can leverage AI technology to provide rapid and accurate information disclosure, improving the reliability and security of the entire system.

[0034] The behavioral analysis unit can analyze a person's behavior from camera footage and detect abnormal behavior. For example, the behavioral analysis unit can analyze a person's behavior from camera footage and detect abnormal behavior. For example, the behavioral analysis unit can detect a person who stays in the same place for a long time or a person who makes suspicious movements and issue a warning. The behavioral analysis unit can analyze behavior using, for example, motion recognition algorithms or behavioral pattern classification methods. This makes it possible to prevent or detect crime early by detecting abnormal behavior. Some or all of the above processing in the behavioral analysis unit may be performed using, for example, AI, or without AI. For example, the behavioral analysis unit can input camera footage into a generating AI and have the generating AI perform the detection of abnormal behavior.

[0035] The data integration unit can identify areas and locations with low pedestrian traffic by combining statistical data and pedestrian traffic data. For example, the data integration unit can integrate data using demographic data and crime rate data. For example, the data integration unit can collect and integrate pedestrian traffic data using GPS data and traffic volume data. This allows for the efficient implementation of crime prevention measures by identifying areas and locations with low pedestrian traffic. Some or all of the above-described processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input statistical data and pedestrian traffic data into a generating AI and have the generating AI identify areas and locations with low pedestrian traffic.

[0036] The Information Disclosure Department may enable information disclosure by court order. The Information Disclosure Department may enable information disclosure by court order, for example. The Information Disclosure Department may disclose information based on criteria such as the type of information to be disclosed and the timing of disclosure. The Information Disclosure Department may, for example, disable the avatar display and provide the actual video. This allows the avatar display to be disabled and the actual video to be provided when necessary. Some or all of the above processing in the Information Disclosure Department may be performed using AI, for example, or not using AI. For example, the Information Disclosure Department may disclose information using an AI model that disables the avatar display and provides the actual video based on a court order.

[0037] The avatar display unit can display a person's appearance as an avatar even in private zones such as public restrooms and changing rooms. The avatar display unit can represent a person using, for example, 2D avatars, 3D avatars, or real-time generated avatars. This ensures privacy even in private zones. Some or all of the above-described processing in the avatar display unit may be performed using, for example, AI, or without AI. For example, the avatar display unit can input the appearance of a person in a private zone into a generating AI and have the generating AI perform the avatar display.

[0038] The behavioral analysis unit can detect individuals who remain in the same place for extended periods or exhibit suspicious behavior and issue warnings. For example, the behavioral analysis unit can detect individuals who remain in the same place for extended periods or exhibit suspicious behavior and issue warnings. The behavioral analysis unit can analyze behavior using, for example, motion recognition algorithms or behavioral pattern classification methods. This enables crime prevention and early detection by detecting suspicious behavior and issuing warnings. Some or all of the above-described processes in the behavioral analysis unit may be performed using, for example, AI, or without AI. For example, the behavioral analysis unit can input camera footage into a generating AI and have the generating AI perform the detection of suspicious behavior.

[0039] The avatar display unit can select the most suitable avatar by referring to the user's past behavior history when displaying an avatar. For example, the avatar display unit can suggest the most suitable avatar based on the style of avatars the user has previously selected. For example, the avatar display unit can select an avatar suitable for a specific situation based on the user's past behavior patterns. For example, the avatar display unit can analyze the user's past avatar usage history and prioritize displaying the most frequently used avatars. This allows the system to select the most suitable avatar by referring to the user's past behavior history. Some or all of the above-described processes in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's past behavior history data into a generating AI and have the generating AI select the most suitable avatar.

[0040] The avatar display unit can customize the avatar's behavior based on the user's current situation and environment when displaying the avatar. For example, if the user is outdoors, the avatar display unit can make the avatar's behavior more subdued so that it blends in with the surrounding environment. For example, if the user is indoors, the avatar display unit can make the avatar's behavior more lively to emphasize friendliness. For example, if the user is participating in a specific event, the avatar display unit can customize the avatar's behavior to be appropriate for that event. This allows for more appropriate avatar display by customizing the avatar's behavior based on the user's current situation and environment. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the customization of the avatar's behavior.

[0041] The avatar display unit can display the most suitable avatar by considering the user's geographical location information when displaying an avatar. For example, if the user is in a specific region, the avatar display unit can display an avatar appropriate for that region. For example, if the user is traveling, the avatar display unit can display an avatar that matches the culture of the travel destination. For example, if the user is at home, the avatar display unit can display an avatar that conveys a relaxed atmosphere. In this way, the optimal avatar can be displayed by considering the user's geographical location information. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's geographical location information data into a generating AI and cause the generating AI to display the optimal avatar.

[0042] The avatar display unit can analyze the user's social media activity and display relevant avatars when displaying avatars. For example, the avatar display unit can display relevant avatars based on images and videos shared by the user on social media. For example, the avatar display unit can analyze the content of the user's social media posts and display avatars appropriate to that content. For example, the avatar display unit can consider the user's social media friendships and display avatars shared with friends. In this way, relevant avatars can be displayed by analyzing the user's social media activity. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's social media activity data into a generating AI and have the generating AI perform the display of relevant avatars.

[0043] The behavioral analysis unit can improve the accuracy of detecting abnormal behavior by referring to past behavioral data during behavioral analysis. For example, the behavioral analysis unit can learn patterns of abnormal behavior based on the user's past behavioral data and improve detection accuracy. For example, the behavioral analysis unit can analyze past behavioral data and predict abnormal behavior at specific times or locations. For example, the behavioral analysis unit can dynamically adjust the detection criteria for abnormal behavior by referring to the user's past behavioral data. This allows for improved detection accuracy of abnormal behavior by referring to past behavioral data. Some or all of the above processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of abnormal behavior detection.

[0044] The behavioral analysis unit can perform analysis while considering the attribute information of the person. For example, the behavioral analysis unit can adjust the detection criteria for abnormal behavior by considering the person's age and gender. For example, the behavioral analysis unit can improve the accuracy of behavioral analysis by considering the person's occupation and lifestyle. For example, the behavioral analysis unit can customize the detection criteria for abnormal behavior based on the person's past behavioral history. In this way, the accuracy of behavioral analysis can be improved by considering the attribute information of the person. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input the person's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of behavioral analysis.

[0045] The behavioral analysis unit can perform analysis while considering the geographical distribution of individuals. For example, the behavioral analysis unit can analyze the behavioral patterns of individuals in a specific area and detect abnormal behavior. For example, the behavioral analysis unit can predict the frequency of abnormal behavior based on geographical distribution. For example, the behavioral analysis unit can adjust the detection criteria for abnormal behavior for each region, taking geographical distribution into consideration. This improves the accuracy of behavioral analysis by considering the geographical distribution of individuals. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of behavioral analysis.

[0046] The behavioral analysis unit can improve the accuracy of its analysis by referring to relevant literature during behavioral analysis. For example, the behavioral analysis unit can improve its behavioral analysis algorithm by referring to the latest research papers. For example, the behavioral analysis unit can update the criteria for detecting abnormal behavior based on relevant literature. For example, the behavioral analysis unit can improve the accuracy of its behavioral analysis by utilizing past research results. In this way, the accuracy of behavioral analysis can be improved by referring to relevant literature. Some or all of the above processes in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of behavioral analysis.

[0047] The data integration unit can optimize the integration algorithm by referring to historical statistical data during data integration. For example, the data integration unit can adjust the parameters of the integration algorithm based on historical statistical data. For example, the data integration unit can analyze statistical data and select the optimal integration method. For example, the data integration unit can improve the accuracy of the integration algorithm by referring to historical statistical data. Some or all of the above processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input historical statistical data into a generating AI and have the generating AI perform the optimization of the integration algorithm.

[0048] The data integration unit can perform data integration while considering the attribute information of individuals. For example, the data integration unit can improve the accuracy of the integrated data by considering the age and gender of individuals. For example, the data integration unit can select data to integrate by considering the occupation and lifestyle of individuals. For example, the data integration unit can improve the accuracy of the integrated data based on the past behavioral history of individuals. In this way, the accuracy of the integrated data can be improved by considering the attribute information of individuals. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without using AI. For example, the data integration unit can input individual attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of the integrated data.

[0049] The data integration unit can perform data integration while considering geographical distribution. For example, the data integration unit can improve the accuracy of the integrated data based on geographical distribution. For example, the data integration unit can select data to integrate while considering geographical distribution. For example, the data integration unit can adjust the parameters of the integration algorithm based on geographical distribution. This allows for improved accuracy of the integrated data by considering geographical distribution. Some or all of the above-described processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of the integrated data.

[0050] The data integration unit can improve the accuracy of data integration by referring to relevant literature during the integration process. For example, the data integration unit can improve the integration algorithm by referring to the latest research papers. For example, the data integration unit can select data to integrate based on relevant literature. For example, the data integration unit can improve the accuracy of the integration algorithm by utilizing past research results. This allows for improved integration accuracy by referring to relevant literature. Some or all of the above processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving integration accuracy.

[0051] The Information Disclosure Department can optimize its disclosure algorithm by referring to past disclosure data when disclosing information. For example, the Information Disclosure Department can adjust the parameters of the disclosure algorithm based on past disclosure data. For example, the Information Disclosure Department can analyze the disclosure data and select the optimal disclosure method. For example, the Information Disclosure Department can improve the accuracy of the disclosure algorithm by referring to past disclosure data. In this way, the accuracy of the disclosure algorithm can be improved by referring to past disclosure data. Some or all of the above processes in the Information Disclosure Department may be performed using AI, for example, or without using AI. For example, the Information Disclosure Department can input past disclosure data into a generating AI and have the generating AI perform the optimization of the disclosure algorithm.

[0052] The Information Disclosure Department may consider the attribute information of individuals when disclosing information. For example, the Information Disclosure Department may improve the accuracy of disclosed information by considering the age and gender of individuals. For example, the Information Disclosure Department may select disclosed information by considering the occupation and lifestyle of individuals. For example, the Information Disclosure Department may improve the accuracy of disclosed information based on the past behavioral history of individuals. In this way, the accuracy of disclosed information can be improved by considering the attribute information of individuals. Some or all of the above processing in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department may input individual attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of disclosed information.

[0053] The Information Disclosure Department may disclose information while considering its geographical distribution. The Information Disclosure Department may, for example, improve the accuracy of the disclosed information based on its geographical distribution. The Information Disclosure Department may, for example, select the information to be disclosed while considering its geographical distribution. The Information Disclosure Department may, for example, adjust the parameters of the disclosure algorithm based on its geographical distribution. This allows for an improvement in the accuracy of the disclosed information by considering its geographical distribution. Some or all of the above-described processes in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department may input geographical distribution data into a generating AI and have the generating AI perform an improvement in the accuracy of the disclosed information.

[0054] The Information Disclosure Department can improve the accuracy of disclosure by referring to relevant literature when disclosing information. For example, the Information Disclosure Department can improve the disclosure algorithm by referring to the latest research papers. For example, the Information Disclosure Department can select information to disclose based on relevant literature. For example, the Information Disclosure Department can improve the accuracy of the disclosure algorithm by utilizing past research results. This allows for improved disclosure accuracy by referring to relevant literature. Some or all of the above processes in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department can input relevant literature data into a generating AI and have the generating AI perform the improvement of disclosure accuracy.

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

[0056] The avatar display unit can select the most suitable avatar by referring to the user's past behavior history when displaying an avatar. For example, it can suggest the most suitable avatar based on the style of avatars the user has previously selected. It can also select an avatar suitable for a specific situation based on the user's past behavior patterns. Furthermore, it can analyze the user's past avatar usage history and prioritize displaying the most frequently used avatar. In this way, the optimal avatar can be selected by referring to the user's past behavior history. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's past behavior history data into a generating AI and have the generating AI perform the selection of the optimal avatar.

[0057] The avatar display unit can customize the avatar's behavior based on the user's current situation and environment when displaying the avatar. For example, if the user is outdoors, the avatar's behavior can be made more subdued to blend in with the surrounding environment. If the user is indoors, the avatar's behavior can be made more lively to emphasize friendliness. Furthermore, if the user is participating in a specific event, the avatar's behavior can be customized to suit that event. By customizing the avatar's behavior based on the user's current situation and environment, a more appropriate avatar display becomes possible. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the customization of the avatar's behavior.

[0058] The avatar display unit can display the most suitable avatar by considering the user's geographical location information. For example, if the user is in a specific region, it can display an avatar appropriate for that region. If the user is traveling, it can display an avatar that matches the culture of the travel destination. Furthermore, if the user is at home, it can display an avatar that conveys a relaxed atmosphere. In this way, the optimal avatar can be displayed by considering the user's geographical location information. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's geographical location information data into a generating AI and cause the generating AI to display the optimal avatar.

[0059] The avatar display unit can analyze the user's social media activity and display relevant avatars when displaying avatars. For example, it can display relevant avatars based on images and videos shared by the user on social media. It can also analyze the content of the user's social media posts and display avatars appropriate to that content. Furthermore, it can consider the user's social media friendships and display avatars shared with friends. In this way, relevant avatars can be displayed by analyzing the user's social media activity. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's social media activity data into a generating AI and cause the generating AI to display relevant avatars.

[0060] The behavioral analysis unit can improve the accuracy of abnormal behavior detection by referring to past behavioral data during behavioral analysis. For example, it can learn patterns of abnormal behavior based on the user's past behavioral data and improve detection accuracy. It can also analyze past behavioral data and predict abnormal behavior at specific times or locations. Furthermore, it can dynamically adjust the detection criteria for abnormal behavior by referring to the user's past behavioral data. In this way, the accuracy of abnormal behavior detection can be improved by referring to past behavioral data. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of abnormal behavior detection.

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

[0062] Step 1: The avatar display unit represents people using avatars. The avatar display unit can represent people using, for example, 2D avatars, 3D avatars, or real-time generated avatars. The avatar display unit can also display people as avatars in private zones such as public restrooms and changing rooms. This protects privacy while preventing individuals from being identified in surveillance camera footage. Step 2: The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit. The behavior analysis unit can, for example, analyze a person's behavior from camera footage and detect abnormal behavior. The behavior analysis unit can, for example, detect a person who stays in the same place for a long time or a person who makes suspicious movements and issue a warning. The behavior analysis unit can, for example, analyze behavior using motion recognition algorithms or behavior pattern classification methods. Step 3: The data integration unit integrates the data analyzed by the behavioral analysis unit with statistical data. The data integration unit can, for example, combine statistical data and pedestrian flow data to identify areas or locations with low pedestrian flow. The data integration unit can perform integration using, for example, demographic data and crime rate data. The data integration unit can collect and integrate pedestrian flow data using, for example, GPS data and traffic volume data. Step 4: The Information Disclosure Department discloses information based on the data integrated by the Data Integration Department. The Information Disclosure Department may, for example, enable information disclosure by court order. The Information Disclosure Department may disclose information based on, for example, the type of information to be disclosed or the timing of disclosure. The Information Disclosure Department may, for example, disable the avatar display and provide actual video footage. This allows the avatar AI camera system according to the embodiment to efficiently contribute to crime deterrence while protecting privacy. Some or all of the above-described processes in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department may disclose information using an AI model that disables the avatar display and provides actual video footage based on a court order.

[0063] (Example of form 2) The avatar AI camera system according to an embodiment of the present invention is a system that balances privacy protection and crime prevention in private zones. This avatar AI camera system represents people as avatars and performs behavioral analysis using image language generation AI while protecting privacy. In this case, by combining this with statistical data and pedestrian flow data, it can identify areas and places with low pedestrian flow and efficiently contribute to crime deterrence. When necessary, information disclosure by court order is possible, aiming to protect privacy and improve crime arrest rates. This system makes it possible to enhance the safety of society as a whole without infringing on individual privacy, and provides a comfortable and secure living environment. For example, the avatar AI camera system represents people as avatars. This protects privacy and prevents individuals from being identified in surveillance camera footage. For example, even in private zones such as public restrooms and changing rooms, privacy can be protected by displaying people as avatars. Next, the avatar AI camera system performs behavioral analysis using image language generation AI. The AI ​​analyzes people's behavior from camera footage and detects abnormal behavior. For example, it can detect people who stay in the same place for a long time or people who are moving suspiciously and issue a warning. This enables crime prevention and early detection. Furthermore, the avatar AI camera system, when combined with statistical data and pedestrian flow data, identifies areas and locations with low foot traffic. This allows for the identification of crime-prone locations and the implementation of more efficient crime prevention measures. For example, cameras can be strategically placed in parks and parking lots with low foot traffic at night to enhance surveillance. When necessary, the avatar AI camera system can disclose information by court order. This allows for the deactivation of the avatar display and the provision of actual footage in the event of a crime. This improves the crime detection rate while protecting privacy. This system makes it possible to enhance the safety of society as a whole without infringing on individual privacy. For example, it allows for safer use of private zones such as public restrooms and changing rooms. It also enables crime prevention and early detection, providing a comfortable and safe living environment.This allows avatar AI camera systems to effectively contribute to crime deterrence while protecting privacy.

[0064] The avatar AI camera system according to this embodiment comprises an avatar display unit, a behavior analysis unit, a data integration unit, and an information disclosure unit. The avatar display unit represents people as avatars. The avatar display unit can represent people using, for example, 2D avatars, 3D avatars, or real-time generated avatars. The avatar display unit can display people as avatars even in private zones such as public restrooms or changing rooms. This protects privacy while preventing the identification of individuals in surveillance camera footage. The behavior analysis unit analyzes the behavior of the avatars displayed by the avatar display unit. The behavior analysis unit can, for example, analyze a person's behavior from camera footage and detect abnormal behavior. The behavior analysis unit can, for example, detect a person who stays in the same place for a long time or a person who makes suspicious movements and issue a warning. The behavior analysis unit can analyze behavior using, for example, a motion recognition algorithm or a behavior pattern classification method. The data integration unit integrates the data analyzed by the behavior analysis unit with statistical data. The data integration unit can, for example, identify areas or locations with low pedestrian traffic by combining statistical data and pedestrian traffic data. The data integration unit can perform integration using, for example, demographic data and crime rate data. The data integration unit can collect and integrate pedestrian traffic data using, for example, GPS data and traffic volume data. The information disclosure unit discloses information based on the data integrated by the data integration unit. The information disclosure unit can, for example, enable information disclosure by court order. The information disclosure unit can disclose information based on, for example, the type of information to be disclosed and the timing of disclosure. The information disclosure unit can, for example, disable the avatar display and provide actual video footage. This allows the avatar AI camera system according to the embodiment to efficiently contribute to crime prevention while protecting privacy. Some or all of the above-described processing in the information disclosure unit may be performed using, for example, AI, or not using AI. For example, the information disclosure unit can disclose information using an AI model that disables the avatar display and provides actual video footage based on a court order.

[0065] The avatar display unit represents people using avatars. The avatar display unit can represent people using, for example, 2D avatars, 3D avatars, and real-time generated avatars. Specifically, 2D avatars represent people using flat characters or silhouettes, while 3D avatars enable more realistic representations using three-dimensional characters. Real-time generated avatars are instantly generated based on camera footage, reflecting a person's movements and expressions in real time. This allows the avatar display unit to flexibly generate and display avatars according to various situations and applications. Furthermore, the avatar display unit can display people as avatars even in private zones such as public restrooms and changing rooms. This protects privacy while preventing the identification of individuals in surveillance camera footage. For example, in private zones, abstracting the appearance and movements of avatars can prevent individual identification while maintaining necessary surveillance functions. The avatar display unit can use AI technology to analyze a person's movements and expressions and reflect them in the avatar. For example, using deep learning-based image recognition technology, the system can detect a person's posture and movements from camera footage and generate an avatar based on that. Furthermore, facial recognition technology can be used to analyze a person's facial expressions in real time and reflect them in the avatar's expressions. This allows the avatar display unit to generate and display more natural and realistic avatars. In addition, the avatar display unit can customize the avatar's appearance and movements to protect user privacy. For example, by setting the desired appearance and movements, the system can display an avatar that aligns with the user's intentions while preventing personal identification. This allows the avatar display unit to provide flexible and effective monitoring while maximizing user privacy protection.

[0066] The behavior analysis unit analyzes the behavior of avatars displayed by the avatar display unit. For example, the behavior analysis unit can analyze a person's behavior from camera footage and detect abnormal behavior. Specifically, it utilizes an AI-based motion recognition algorithm to analyze a person's movements and behavioral patterns. For example, it can detect a person who stays in the same place for a long time or a person who is moving suspiciously and issue a warning. The behavior analysis unit can distinguish between normal and abnormal behavior using a behavioral pattern classification method based on deep learning. For example, it can use a model trained on past data to identify normal behavioral patterns and detect behavior that differs from them as abnormal. Furthermore, the behavior analysis unit can analyze behavior in real time and issue a warning immediately if an abnormality is detected. This enables a rapid response and contributes to crime deterrence and early detection. In addition, the behavior analysis unit can integrate and analyze multiple camera videos. For example, it can integrate videos taken from different angles to perform more accurate behavioral analysis. This reduces blind spots and oversights, enabling more accurate surveillance. The behavioral analysis unit can be used not only to detect abnormal behavior, but also to analyze normal behavioral patterns and understand trends. For example, it can analyze people's behavioral patterns at specific times and locations to understand congestion levels and fluctuations in pedestrian flow. This can be used to plan efficient staffing and safety measures. The behavioral analysis unit can utilize AI technology to perform real-time, highly accurate behavioral analysis, improving the overall monitoring capabilities of the system.

[0067] The Data Integration Department integrates data analyzed by the Behavioral Analysis Department with statistical data. For example, the Data Integration Department can combine statistical data and pedestrian flow data to identify areas or locations with low pedestrian traffic. Specifically, it can use demographic data and crime rate data to conduct risk assessments in specific areas. For instance, based on historical crime data, it can identify areas and times prone to crime and focus monitoring efforts. It can also collect and integrate pedestrian flow data using GPS data and traffic volume data. This allows for understanding people's movements in specific locations and times, enabling more efficient monitoring. The Data Integration Department can use AI to analyze this data and integrate multiple data sources to provide more accurate information. For example, deep learning-based data analysis techniques can integrate information from different data sources and identify correlations. This allows the Data Integration Department to provide insights not obtainable from a single data source, improving the overall monitoring capabilities of the system. Furthermore, the Data Integration Department can continuously integrate information based on real-time updated data, enabling it to respond to the latest situations. For example, if traffic volume data or pedestrian flow data is updated in real time, the data integration unit can immediately incorporate the new data and update the integration results. This enables monitoring based on the latest information at all times, supporting quick and appropriate responses. The data integration unit can utilize AI technology to integrate multiple data sources and improve the overall monitoring capabilities of the system.

[0068] The Information Disclosure Department discloses information based on data integrated by the Data Integration Department. The Information Disclosure Department can, for example, disclose information based on court orders. Specifically, it can remove avatar displays and provide actual video footage based on court orders. This allows for the provision of actual video footage while protecting privacy when necessary. The Information Disclosure Department can disclose information based on the type of information to be disclosed and the timing of disclosure. For example, it can selectively disclose only the necessary information depending on a specific case or situation. This allows for the provision of necessary information while protecting privacy to the greatest extent possible. The Information Disclosure Department can use AI to disclose information. For example, it can use an AI model to automate the process of removing avatar displays and providing actual video footage based on court orders. This enables rapid and accurate information disclosure and efficient responses. Furthermore, the Information Disclosure Department can manage and track disclosed information. For example, it can record the history of disclosed information and conduct tracking and audits as needed. This ensures transparency and reliability in the information disclosure process. The Information Disclosure Department can leverage AI technology to provide rapid and accurate information disclosure, improving the reliability and security of the entire system.

[0069] The behavioral analysis unit can analyze a person's behavior from camera footage and detect abnormal behavior. For example, the behavioral analysis unit can analyze a person's behavior from camera footage and detect abnormal behavior. For example, the behavioral analysis unit can detect a person who stays in the same place for a long time or a person who makes suspicious movements and issue a warning. The behavioral analysis unit can analyze behavior using, for example, motion recognition algorithms or behavioral pattern classification methods. This makes it possible to prevent or detect crime early by detecting abnormal behavior. Some or all of the above processing in the behavioral analysis unit may be performed using, for example, AI, or without AI. For example, the behavioral analysis unit can input camera footage into a generating AI and have the generating AI perform the detection of abnormal behavior.

[0070] The data integration unit can identify areas and locations with low pedestrian traffic by combining statistical data and pedestrian traffic data. For example, the data integration unit can integrate data using demographic data and crime rate data. For example, the data integration unit can collect and integrate pedestrian traffic data using GPS data and traffic volume data. This allows for the efficient implementation of crime prevention measures by identifying areas and locations with low pedestrian traffic. Some or all of the above-described processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input statistical data and pedestrian traffic data into a generating AI and have the generating AI identify areas and locations with low pedestrian traffic.

[0071] The Information Disclosure Department may enable information disclosure by court order. The Information Disclosure Department may enable information disclosure by court order, for example. The Information Disclosure Department may disclose information based on criteria such as the type of information to be disclosed and the timing of disclosure. The Information Disclosure Department may, for example, disable the avatar display and provide the actual video. This allows the avatar display to be disabled and the actual video to be provided when necessary. Some or all of the above processing in the Information Disclosure Department may be performed using AI, for example, or not using AI. For example, the Information Disclosure Department may disclose information using an AI model that disables the avatar display and provides the actual video based on a court order.

[0072] The avatar display unit can display a person's appearance as an avatar even in private zones such as public restrooms and changing rooms. The avatar display unit can represent a person using, for example, 2D avatars, 3D avatars, or real-time generated avatars. This ensures privacy even in private zones. Some or all of the above-described processing in the avatar display unit may be performed using, for example, AI, or without AI. For example, the avatar display unit can input the appearance of a person in a private zone into a generating AI and have the generating AI perform the avatar display.

[0073] The behavioral analysis unit can detect individuals who remain in the same place for extended periods or exhibit suspicious behavior and issue warnings. For example, the behavioral analysis unit can detect individuals who remain in the same place for extended periods or exhibit suspicious behavior and issue warnings. The behavioral analysis unit can analyze behavior using, for example, motion recognition algorithms or behavioral pattern classification methods. This enables crime prevention and early detection by detecting suspicious behavior and issuing warnings. Some or all of the above-described processes in the behavioral analysis unit may be performed using, for example, AI, or without AI. For example, the behavioral analysis unit can input camera footage into a generating AI and have the generating AI perform the detection of suspicious behavior.

[0074] The avatar display unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is nervous, the avatar display unit can make the avatar's expression calmer to convey a sense of reassurance. For example, if the user is relaxed, the avatar display unit can make the avatar's expression brighter to emphasize friendliness. For example, if the user is excited, the avatar display unit can make the avatar's movements more subdued to help the user regain composure. This allows for more appropriate avatar display by adjusting the avatar's expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's emotion data into the generative AI and have the generative AI adjust the avatar's expression.

[0075] The avatar display unit can select the most suitable avatar by referring to the user's past behavior history when displaying an avatar. For example, the avatar display unit can suggest the most suitable avatar based on the style of avatars the user has previously selected. For example, the avatar display unit can select an avatar suitable for a specific situation based on the user's past behavior patterns. For example, the avatar display unit can analyze the user's past avatar usage history and prioritize displaying the most frequently used avatars. This allows the system to select the most suitable avatar by referring to the user's past behavior history. Some or all of the above-described processes in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's past behavior history data into a generating AI and have the generating AI select the most suitable avatar.

[0076] The avatar display unit can customize the avatar's behavior based on the user's current situation and environment when displaying the avatar. For example, if the user is outdoors, the avatar display unit can make the avatar's behavior more subdued so that it blends in with the surrounding environment. For example, if the user is indoors, the avatar display unit can make the avatar's behavior more lively to emphasize friendliness. For example, if the user is participating in a specific event, the avatar display unit can customize the avatar's behavior to be appropriate for that event. This allows for more appropriate avatar display by customizing the avatar's behavior based on the user's current situation and environment. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the customization of the avatar's behavior.

[0077] The avatar display unit can estimate the user's emotions and adjust the timing of avatar display based on the estimated emotions. For example, if the user is stressed, the avatar display unit can delay the display of the avatar to give the user time to relax. For example, if the user is relaxed, the avatar display unit can speed up the display of the avatar to emphasize friendliness. For example, if the user is in a hurry, the avatar display unit can quickly display the avatar to immediately provide the necessary information. By adjusting the timing of avatar display according to the user's emotions, the avatar can be displayed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input user emotion data into a generative AI and have the generative AI adjust the timing of avatar display.

[0078] The avatar display unit can display the most suitable avatar by considering the user's geographical location information when displaying an avatar. For example, if the user is in a specific region, the avatar display unit can display an avatar appropriate for that region. For example, if the user is traveling, the avatar display unit can display an avatar that matches the culture of the travel destination. For example, if the user is at home, the avatar display unit can display an avatar that conveys a relaxed atmosphere. In this way, the optimal avatar can be displayed by considering the user's geographical location information. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's geographical location information data into a generating AI and cause the generating AI to display the optimal avatar.

[0079] The avatar display unit can analyze the user's social media activity and display relevant avatars when displaying avatars. For example, the avatar display unit can display relevant avatars based on images and videos shared by the user on social media. For example, the avatar display unit can analyze the content of the user's social media posts and display avatars appropriate to that content. For example, the avatar display unit can consider the user's social media friendships and display avatars shared with friends. In this way, relevant avatars can be displayed by analyzing the user's social media activity. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's social media activity data into a generating AI and have the generating AI perform the display of relevant avatars.

[0080] The behavioral analysis unit can estimate the user's emotions and adjust the behavioral analysis criteria based on the estimated user emotions. For example, if the user is tense, the behavioral analysis unit can relax the behavioral analysis criteria and carefully detect abnormal behavior. For example, if the user is relaxed, the behavioral analysis unit can tighten the behavioral analysis criteria to improve the accuracy of abnormal behavior detection. For example, if the user is excited, the behavioral analysis unit can adjust the behavioral analysis criteria to prevent false positives. This allows for more appropriate behavioral analysis by adjusting the behavioral analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user emotion data into the generative AI and have the generative AI adjust the behavioral analysis criteria.

[0081] The behavioral analysis unit can improve the accuracy of detecting abnormal behavior by referring to past behavioral data during behavioral analysis. For example, the behavioral analysis unit can learn patterns of abnormal behavior based on the user's past behavioral data and improve detection accuracy. For example, the behavioral analysis unit can analyze past behavioral data and predict abnormal behavior at specific times or locations. For example, the behavioral analysis unit can dynamically adjust the detection criteria for abnormal behavior by referring to the user's past behavioral data. This allows for improved detection accuracy of abnormal behavior by referring to past behavioral data. Some or all of the above processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of abnormal behavior detection.

[0082] The behavioral analysis unit can perform analysis while considering the attribute information of the person. For example, the behavioral analysis unit can adjust the detection criteria for abnormal behavior by considering the person's age and gender. For example, the behavioral analysis unit can improve the accuracy of behavioral analysis by considering the person's occupation and lifestyle. For example, the behavioral analysis unit can customize the detection criteria for abnormal behavior based on the person's past behavioral history. In this way, the accuracy of behavioral analysis can be improved by considering the attribute information of the person. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input the person's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of behavioral analysis.

[0083] The behavioral analysis unit can estimate the user's emotions and adjust the order in which the behavioral analysis results are displayed based on the estimated user emotions. For example, if the user is nervous, the behavioral analysis unit can display important results first to provide a sense of reassurance. For example, if the user is relaxed, the behavioral analysis unit can display detailed results sequentially to deepen understanding. For example, if the user is in a hurry, the behavioral analysis unit can quickly display results that summarize the key points. By adjusting the order in which the behavioral analysis results are displayed according to the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input the user's emotion data into the generative AI and have the generative AI execute the order in which the behavioral analysis results are displayed.

[0084] The behavioral analysis unit can perform analysis while considering the geographical distribution of individuals. For example, the behavioral analysis unit can analyze the behavioral patterns of individuals in a specific area and detect abnormal behavior. For example, the behavioral analysis unit can predict the frequency of abnormal behavior based on geographical distribution. For example, the behavioral analysis unit can adjust the detection criteria for abnormal behavior for each region, taking geographical distribution into consideration. This improves the accuracy of behavioral analysis by considering the geographical distribution of individuals. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of behavioral analysis.

[0085] The behavioral analysis unit can improve the accuracy of its analysis by referring to relevant literature during behavioral analysis. For example, the behavioral analysis unit can improve its behavioral analysis algorithm by referring to the latest research papers. For example, the behavioral analysis unit can update the criteria for detecting abnormal behavior based on relevant literature. For example, the behavioral analysis unit can improve the accuracy of its behavioral analysis by utilizing past research results. In this way, the accuracy of behavioral analysis can be improved by referring to relevant literature. Some or all of the above processes in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of behavioral analysis.

[0086] The data integration unit can estimate the user's emotions and select data to integrate based on the estimated emotions. For example, if the user is stressed, the data integration unit can prioritize integrating important data to provide a sense of security. If the user is relaxed, the data integration unit can integrate detailed data to deepen understanding. If the user is in a hurry, the data integration unit can quickly integrate key data. This allows for more appropriate data integration by selecting data to integrate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data integration unit may be performed using AI or not. For example, the data integration unit can input user emotion data into a generative AI and have the generative AI perform the selection of data to integrate.

[0087] The data integration unit can optimize the integration algorithm by referring to historical statistical data during data integration. For example, the data integration unit can adjust the parameters of the integration algorithm based on historical statistical data. For example, the data integration unit can analyze statistical data and select the optimal integration method. For example, the data integration unit can improve the accuracy of the integration algorithm by referring to historical statistical data. Some or all of the above processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input historical statistical data into a generating AI and have the generating AI perform the optimization of the integration algorithm.

[0088] The data integration unit can perform data integration while considering the attribute information of individuals. For example, the data integration unit can improve the accuracy of the integrated data by considering the age and gender of individuals. For example, the data integration unit can select data to integrate by considering the occupation and lifestyle of individuals. For example, the data integration unit can improve the accuracy of the integrated data based on the past behavioral history of individuals. In this way, the accuracy of the integrated data can be improved by considering the attribute information of individuals. Some or all of the above processing in the data integration unit may be performed using AI, for example, or without using AI. For example, the data integration unit can input individual attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of the integrated data.

[0089] The data integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is tense, the data integration unit can provide a simple and highly visible display method. For example, if the user is relaxed, the data integration unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the data integration unit can provide a display method that gets straight to the point. By adjusting the display method of the integrated data according to the user's emotions, more appropriate data display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data integration unit may be performed using AI, for example, or not using AI. For example, the data integration unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the integrated data.

[0090] The data integration unit can perform data integration while considering geographical distribution. For example, the data integration unit can improve the accuracy of the integrated data based on geographical distribution. For example, the data integration unit can select data to integrate while considering geographical distribution. For example, the data integration unit can adjust the parameters of the integration algorithm based on geographical distribution. This allows for improved accuracy of the integrated data by considering geographical distribution. Some or all of the above-described processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of the integrated data.

[0091] The data integration unit can improve the accuracy of data integration by referring to relevant literature during the integration process. For example, the data integration unit can improve the integration algorithm by referring to the latest research papers. For example, the data integration unit can select data to integrate based on relevant literature. For example, the data integration unit can improve the accuracy of the integration algorithm by utilizing past research results. This allows for improved integration accuracy by referring to relevant literature. Some or all of the above processes in the data integration unit may be performed using AI, or without AI. For example, the data integration unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving integration accuracy.

[0092] The information disclosure unit can estimate the user's emotions and adjust the method of information disclosure based on the estimated user emotions. For example, if the user is nervous, the information disclosure unit can provide a simple and highly visible method of information disclosure. For example, if the user is relaxed, the information disclosure unit can provide a method of information disclosure that includes detailed information. For example, if the user is in a hurry, the information disclosure unit can provide a method of information disclosure that gets straight to the point. This allows for more appropriate information disclosure by adjusting the method of information disclosure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information disclosure unit may be performed using AI, for example, or not using AI. For example, the information disclosure unit can input user emotion data into a generative AI and have the generative AI adjust the method of information disclosure.

[0093] The Information Disclosure Department can optimize its disclosure algorithm by referring to past disclosure data when disclosing information. For example, the Information Disclosure Department can adjust the parameters of the disclosure algorithm based on past disclosure data. For example, the Information Disclosure Department can analyze the disclosure data and select the optimal disclosure method. For example, the Information Disclosure Department can improve the accuracy of the disclosure algorithm by referring to past disclosure data. In this way, the accuracy of the disclosure algorithm can be improved by referring to past disclosure data. Some or all of the above processes in the Information Disclosure Department may be performed using AI, for example, or without using AI. For example, the Information Disclosure Department can input past disclosure data into a generating AI and have the generating AI perform the optimization of the disclosure algorithm.

[0094] The Information Disclosure Department may consider the attribute information of individuals when disclosing information. For example, the Information Disclosure Department may improve the accuracy of disclosed information by considering the age and gender of individuals. For example, the Information Disclosure Department may select disclosed information by considering the occupation and lifestyle of individuals. For example, the Information Disclosure Department may improve the accuracy of disclosed information based on the past behavioral history of individuals. In this way, the accuracy of disclosed information can be improved by considering the attribute information of individuals. Some or all of the above processing in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department may input individual attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of disclosed information.

[0095] The information disclosure unit can estimate the user's emotions and determine the priority of information disclosure based on the estimated emotions. For example, if the user is nervous, the information disclosure unit can prioritize the disclosure of important information to provide reassurance. For example, if the user is relaxed, the information disclosure unit can sequentially disclose detailed information to deepen understanding. For example, if the user is in a hurry, the information disclosure unit can quickly disclose concise information. This allows for more appropriate information disclosure by determining the priority of information disclosure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information disclosure unit may be performed using AI or not using AI. For example, the information disclosure unit can input user emotion data into a generative AI and have the generative AI determine the priority of information disclosure.

[0096] The Information Disclosure Department may disclose information while considering its geographical distribution. The Information Disclosure Department may, for example, improve the accuracy of the disclosed information based on its geographical distribution. The Information Disclosure Department may, for example, select the information to be disclosed while considering its geographical distribution. The Information Disclosure Department may, for example, adjust the parameters of the disclosure algorithm based on its geographical distribution. This allows for an improvement in the accuracy of the disclosed information by considering its geographical distribution. Some or all of the above-described processes in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department may input geographical distribution data into a generating AI and have the generating AI perform an improvement in the accuracy of the disclosed information.

[0097] The Information Disclosure Department can improve the accuracy of disclosure by referring to relevant literature when disclosing information. For example, the Information Disclosure Department can improve the disclosure algorithm by referring to the latest research papers. For example, the Information Disclosure Department can select information to disclose based on relevant literature. For example, the Information Disclosure Department can improve the accuracy of the disclosure algorithm by utilizing past research results. This allows for improved disclosure accuracy by referring to relevant literature. Some or all of the above processes in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department can input relevant literature data into a generating AI and have the generating AI perform the improvement of disclosure accuracy.

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

[0099] The avatar display unit can estimate the user's emotions and adjust the avatar's expression based on the estimated emotions. For example, if the user is nervous, the avatar's expression can be made calmer to convey a sense of reassurance. If the user is relaxed, the avatar's expression can be made brighter to emphasize friendliness. Furthermore, if the user is excited, the avatar's movements can be made more subdued to help the user regain composure. By adjusting the avatar's expression according to the user's emotions, a more appropriate avatar display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the avatar display unit may be performed using AI, or not using AI. For example, the avatar display unit can input the user's emotion data into the generative AI and have the generative AI adjust the avatar's expression.

[0100] The behavioral analysis unit can estimate the user's emotions and adjust the behavioral analysis criteria based on the estimated emotions. For example, if the user is tense, the behavioral analysis criteria can be relaxed to allow for careful detection of abnormal behavior. Conversely, if the user is relaxed, the behavioral analysis criteria can be made stricter to improve the accuracy of abnormal behavior detection. Furthermore, if the user is excited, the behavioral analysis criteria can be adjusted to prevent false positives. In this way, adjusting the behavioral analysis criteria according to the user's emotions enables more appropriate behavioral analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, or not using AI. For example, the behavioral analysis unit can input user emotion data into the generative AI and have the generative AI adjust the behavioral analysis criteria.

[0101] The data integration unit can estimate the user's emotions and select data to integrate based on those emotions. For example, if the user is stressed, important data can be prioritized for integration to provide reassurance. If the user is relaxed, detailed data can be integrated to deepen understanding. Furthermore, if the user is in a hurry, key data can be quickly integrated. This allows for more appropriate data integration by selecting data to integrate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data integration unit may be performed using AI or not. For example, the data integration unit can input user emotion data into a generative AI and have the generative AI perform the selection of data to integrate.

[0102] The information disclosure unit can estimate the user's emotions and adjust the method of information disclosure based on the estimated emotions. For example, if the user is nervous, a simple and highly visible method of information disclosure can be provided. If the user is relaxed, a method of information disclosure including detailed information can be provided. Furthermore, if the user is in a hurry, a method of information disclosure that gets straight to the point can be provided. In this way, more appropriate information disclosure becomes possible by adjusting the method of information disclosure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information disclosure unit may be performed using AI, for example, or not using AI. For example, the information disclosure unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the method of information disclosure.

[0103] The information disclosure unit can estimate the user's emotions and determine the priority of information disclosure based on the estimated emotions. For example, if the user is nervous, important information can be disclosed first to provide reassurance. If the user is relaxed, detailed information can be disclosed sequentially to deepen understanding. Furthermore, if the user is in a hurry, concise information can be disclosed quickly. This allows for more appropriate information disclosure by determining the priority of information disclosure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information disclosure unit may be performed using AI or not. For example, the information disclosure unit can input user emotion data into a generative AI and have the generative AI determine the priority of information disclosure.

[0104] The avatar display unit can select the most suitable avatar by referring to the user's past behavior history when displaying an avatar. For example, it can suggest the most suitable avatar based on the style of avatars the user has previously selected. It can also select an avatar suitable for a specific situation based on the user's past behavior patterns. Furthermore, it can analyze the user's past avatar usage history and prioritize displaying the most frequently used avatar. In this way, the optimal avatar can be selected by referring to the user's past behavior history. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's past behavior history data into a generating AI and have the generating AI perform the selection of the optimal avatar.

[0105] The avatar display unit can customize the avatar's behavior based on the user's current situation and environment when displaying the avatar. For example, if the user is outdoors, the avatar's behavior can be made more subdued to blend in with the surrounding environment. If the user is indoors, the avatar's behavior can be made more lively to emphasize friendliness. Furthermore, if the user is participating in a specific event, the avatar's behavior can be customized to suit that event. By customizing the avatar's behavior based on the user's current situation and environment, a more appropriate avatar display becomes possible. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's current situation and environment data into a generating AI and have the generating AI perform the customization of the avatar's behavior.

[0106] The avatar display unit can display the most suitable avatar by considering the user's geographical location information. For example, if the user is in a specific region, it can display an avatar appropriate for that region. If the user is traveling, it can display an avatar that matches the culture of the travel destination. Furthermore, if the user is at home, it can display an avatar that conveys a relaxed atmosphere. In this way, the optimal avatar can be displayed by considering the user's geographical location information. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's geographical location information data into a generating AI and cause the generating AI to display the optimal avatar.

[0107] The avatar display unit can analyze the user's social media activity and display relevant avatars when displaying avatars. For example, it can display relevant avatars based on images and videos shared by the user on social media. It can also analyze the content of the user's social media posts and display avatars appropriate to that content. Furthermore, it can consider the user's social media friendships and display avatars shared with friends. In this way, relevant avatars can be displayed by analyzing the user's social media activity. Some or all of the above processing in the avatar display unit may be performed using AI, for example, or without AI. For example, the avatar display unit can input the user's social media activity data into a generating AI and cause the generating AI to display relevant avatars.

[0108] The behavioral analysis unit can improve the accuracy of abnormal behavior detection by referring to past behavioral data during behavioral analysis. For example, it can learn patterns of abnormal behavior based on the user's past behavioral data and improve detection accuracy. It can also analyze past behavioral data and predict abnormal behavior at specific times or locations. Furthermore, it can dynamically adjust the detection criteria for abnormal behavior by referring to the user's past behavioral data. In this way, the accuracy of abnormal behavior detection can be improved by referring to past behavioral data. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of abnormal behavior detection.

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

[0110] Step 1: The avatar display unit represents people using avatars. The avatar display unit can represent people using, for example, 2D avatars, 3D avatars, or real-time generated avatars. The avatar display unit can also display people as avatars in private zones such as public restrooms and changing rooms. This protects privacy while preventing individuals from being identified in surveillance camera footage. Step 2: The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit. The behavior analysis unit can, for example, analyze a person's behavior from camera footage and detect abnormal behavior. The behavior analysis unit can, for example, detect a person who stays in the same place for a long time or a person who makes suspicious movements and issue a warning. The behavior analysis unit can, for example, analyze behavior using motion recognition algorithms or behavior pattern classification methods. Step 3: The data integration unit integrates the data analyzed by the behavioral analysis unit with statistical data. The data integration unit can, for example, combine statistical data and pedestrian flow data to identify areas or locations with low pedestrian flow. The data integration unit can perform integration using, for example, demographic data and crime rate data. The data integration unit can collect and integrate pedestrian flow data using, for example, GPS data and traffic volume data. Step 4: The Information Disclosure Department discloses information based on the data integrated by the Data Integration Department. The Information Disclosure Department may, for example, enable information disclosure by court order. The Information Disclosure Department may disclose information based on, for example, the type of information to be disclosed or the timing of disclosure. The Information Disclosure Department may, for example, disable the avatar display and provide actual video footage. This allows the avatar AI camera system according to the embodiment to efficiently contribute to crime deterrence while protecting privacy. Some or all of the above-described processes in the Information Disclosure Department may be performed using AI, for example, or without AI. For example, the Information Disclosure Department may disclose information using an AI model that disables the avatar display and provides actual video footage based on a court order.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0114] Each of the multiple elements described above, including the avatar display unit, behavior analysis unit, data integration unit, and information disclosure unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the avatar display unit is implemented by the control unit 46A of the smart device 14 and represents a person as an avatar. The behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes a person's behavior from camera footage. The data integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the analyzed data with statistical data. The information disclosure unit is implemented by the specific processing unit 290 of the data processing unit 12 and discloses information based on a court order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the avatar display unit, behavior analysis unit, data integration unit, and information disclosure unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the avatar display unit is implemented by the control unit 46A of the smart glasses 214 and represents a person as an avatar. The behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes a person's behavior from camera images. The data integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the analyzed data with statistical data. The information disclosure unit is implemented by the specific processing unit 290 of the data processing unit 12 and discloses information based on a court order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the avatar display unit, behavior analysis unit, data integration unit, and information disclosure unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the avatar display unit is implemented by the control unit 46A of the headset terminal 314 and represents a person as an avatar. The behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes a person's behavior from camera footage. The data integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the analyzed data with statistical data. The information disclosure unit is implemented by the specific processing unit 290 of the data processing unit 12 and discloses information based on a court order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the avatar display unit, behavior analysis unit, data integration unit, and information disclosure unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the avatar display unit is implemented by the control unit 46A of the robot 414 and represents a person as an avatar. The behavior analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes a person's behavior from camera footage. The data integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates the analyzed data with statistical data. The information disclosure unit is implemented by the specific processing unit 290 of the data processing unit 12 and discloses information based on a court order. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0182] (Note 1) An avatar display unit that represents a person using an avatar, The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit, A data integration unit that integrates the data analyzed by the aforementioned behavioral analysis unit with statistical data, The system includes an information disclosure unit that discloses information based on the data integrated by the data integration unit. A system characterized by the following features. (Note 2) The aforementioned behavioral analysis unit, Analyzes human behavior from camera footage to detect abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data integration unit, By combining statistical data and pedestrian flow data, we can identify areas and locations with low pedestrian traffic. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information disclosure department, This will enable the disclosure of information by court order. The system described in Appendix 1, characterized by the features described herein. (Note 5) The avatar display unit is, Even in private areas such as public restrooms and changing rooms, people's appearances will be displayed as avatars. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned behavioral analysis unit, It detects individuals who remain in the same place for extended periods or exhibit suspicious behavior, and issues a warning. The system described in Appendix 1, characterized by the features described herein. (Note 7) The avatar display unit is, It estimates the user's emotions and adjusts the avatar's representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The avatar display unit is, When displaying an avatar, the system selects the most suitable avatar by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The avatar display unit is, When displaying an avatar, the avatar's behavior is customized based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The avatar display unit is, It estimates the user's emotions and adjusts the timing of avatar display based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The avatar display unit is, When displaying avatars, the system takes the user's geographical location into consideration to display the most suitable avatar. The system described in Appendix 1, characterized by the features described herein. (Note 12) The avatar display unit is, When displaying avatars, the system analyzes the user's social media activity and displays relevant avatars. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned behavioral analysis unit, We estimate the user's emotions and adjust the behavioral analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned behavioral analysis unit, During behavioral analysis, past behavioral data is referenced to improve the accuracy of detecting abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned behavioral analysis unit, When performing behavioral analysis, the analysis takes into account the attribute information of the individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned behavioral analysis unit, It estimates the user's emotions and adjusts the order in which the behavioral analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned behavioral analysis unit, When performing behavioral analysis, the geographical distribution of individuals should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned behavioral analysis unit, When performing behavioral analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data integration unit, The system estimates user emotions and selects integrated data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data integration unit, During data integration, the integration algorithm is optimized by referring to historical statistical data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data integration unit, When integrating data, the data will be integrated while taking into account the attribute information of individuals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data integration unit, It estimates the user's emotions and adjusts how the integrated data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned data integration unit, When integrating data, consider geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned data integration unit, When integrating data, refer to relevant literature to improve the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information disclosure department, We estimate user sentiment and adjust the method of information disclosure based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned information disclosure department, When disclosing information, the disclosure algorithm is optimized by referring to past disclosure data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned information disclosure department, When disclosing information, the personal attributes of the individuals involved will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned information disclosure department, We estimate user sentiment and determine the priority of information disclosure based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information disclosure department, When disclosing information, consider its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned information disclosure department, When disclosing information, we will improve the accuracy of the disclosure by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. An avatar display unit that represents a person using an avatar, The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit, A data integration unit that integrates the data analyzed by the aforementioned behavioral analysis unit with statistical data, The system includes an information disclosure unit that discloses information based on the data integrated by the data integration unit. A system characterized by the following features.

2. The aforementioned behavioral analysis unit, Analyzes human behavior from camera footage to detect abnormal behavior. The system according to feature 1.

3. The aforementioned data integration unit, By combining statistical data and pedestrian flow data, we can identify areas and locations with low pedestrian traffic. The system according to feature 1.

4. The aforementioned information disclosure department, This will enable the disclosure of information by court order. The system according to feature 1.

5. The avatar display unit is, Even in private areas such as public restrooms and changing rooms, people's appearances will be displayed as avatars. The system according to feature 1.

6. The aforementioned behavioral analysis unit, It detects individuals who remain in the same place for extended periods or exhibit suspicious behavior, and issues a warning. The system according to feature 1.

7. The avatar display unit is, It estimates the user's emotions and adjusts the avatar's representation based on those estimated emotions. The system according to feature 1.

8. The avatar display unit is, When displaying an avatar, the system selects the most suitable avatar by referring to the user's past behavior history. The system according to feature 1.

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