System

Abstract

An object of a system according to an embodiment is to perform a fair evaluation based on behavior data of people.SOLUTION: A system according to an embodiment includes a monitoring camera, a behavior data collection unit, and a rank setting unit. The monitoring camera monitors the actions of the people. The behavior data collection unit collects behavior data of the citizens. The rank setting unit sets a rank based on the action data collected by the action data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide fair evaluations based on citizens' behavioral data, and there is room for improvement.

[0005] The system according to the embodiment aims to perform fair evaluations based on behavioral data of citizens. [Means for solving the problem]

[0006] A system according to an embodiment includes a surveillance camera, a behavioral data collection unit, and a rank setting unit. The surveillance camera monitors the behavior of citizens. The behavioral data collection unit collects behavioral data of citizens. The rank setting unit sets a rank based on the behavioral data collected by the behavioral data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can perform fair evaluations based on behavioral data of citizens. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The citizen ranking system according to an embodiment of the present invention is a system that monitors the behavior of citizens and sets ranks based on that data, thereby correcting unfairness among citizens and maintaining order and fairness in society as a whole.

[0029] A citizen ranking system according to an embodiment includes a surveillance camera, a behavioral data collection unit, and a rank setting unit. The surveillance camera monitors citizens' behavior. For example, surveillance cameras throughout the city capture images of people's behavior and collect the video data. Surveillance cameras are also installed in public places and major facilities, allowing for real-time recording of citizens' behavior. The behavioral data collection unit collects the video data collected by the surveillance camera. For example, the surveillance camera video data is transmitted to a server and stored in a database. The behavioral data collection unit can also analyze the video data and extract specific behavioral patterns. The rank setting unit sets ranks based on the behavioral data collected by the behavioral data collection unit. For example, AI can analyze the behavioral data and assign higher ranks to individuals who actively participate in social contribution activities and volunteer activities. AI can also assign lower ranks to individuals who have engaged in illegal activities or socially condemned behavior. In this way, the citizen ranking system can correct inequality among citizens by monitoring citizens' behavior and assigning ranks based on that data.

[0030] The rank setting unit can assign a higher rank to people who actively participate in social contribution activities or volunteer activities. For example, the rank setting unit uses AI to analyze video data from surveillance cameras and assign a higher rank to people who actively participate in social contribution activities or volunteer activities. For example, AI evaluates actions such as picking up trash or following traffic rules and assigns a higher rank to them. AI can also perform evaluations based on the frequency of participation in social contribution activities or volunteer activities and the content of the activities. For example, AI can assign a higher rank to people who regularly participate in volunteer activities. This makes it possible to highly evaluate people who actively participate in social contribution activities or volunteer activities.

[0031] The rank setting unit can assign a lower rank to people who have committed illegal acts or socially reprehensible acts. For example, the rank setting unit uses AI to analyze video data from surveillance cameras and assign a lower rank to people who have committed illegal acts or socially reprehensible acts. For example, AI detects acts such as traffic violations or theft and assigns a lower rank to them. AI can also perform evaluations based on acts that are contrary to public order and morals or nuisance behavior. For example, AI detects nuisance behavior in public places and assigns a lower rank to them. This makes it possible to assign a lower rank to people who have committed illegal acts or socially reprehensible acts.

[0032] The behavioral data collection unit can collect video data from surveillance cameras. For example, the behavioral data collection unit collects video data from surveillance cameras in real time. For example, surveillance cameras around town capture people's behavior and send the video data to a server. The behavioral data collection unit can also analyze the recorded video data and extract specific behavioral patterns. For example, AI can analyze the video data and detect behaviors such as picking up trash and following traffic rules. In this way, by collecting video data from surveillance cameras, it is possible to monitor the behavior of citizens.

[0033] The ranking unit can evaluate the consistency or continuity of behavior and place emphasis on long-term social contribution. For example, the ranking unit uses AI to collect behavioral data of citizens over a long period of time and evaluate the consistency of behavior. For example, it highly rates people who regularly participate in volunteer activities. Furthermore, the AI ​​analyzes temporal patterns of the behavioral data to evaluate the continuity of behavior. For example, it highly rates people who continuously engage in social contribution activities over a long period of time. Furthermore, the AI ​​develops an algorithm that comprehensively evaluates the consistency and continuity of behavior. For example, it sets evaluation criteria that place emphasis on long-term social contribution over short-term actions. This makes it possible to promote sustainable social contribution activities by emphasizing long-term social contribution.

[0034] The ranking unit can highly evaluate actions that have a wide-ranging impact based on the impact range of the action. For example, the ranking unit uses AI to analyze action data and develop an algorithm to evaluate the impact range of an action. For example, it highly evaluates actions that have a positive impact on the entire local community. Furthermore, the AI ​​integrates action data with local data to evaluate the impact range of an action. For example, it highly evaluates actions that are involved in planning and running local events. Furthermore, the AI ​​quantifies the impact range of an action and builds a system that highly evaluates actions that have a wide-ranging impact. For example, it evaluates actions that contribute to solving problems in the local community. In this way, by highly evaluating actions that have a wide-ranging impact, it is possible to promote contributions to society as a whole.

[0035] The ranking unit can integrate different data sources and perform a comprehensive evaluation. For example, the ranking unit uses AI to collect social media posting data and evaluate citizens' online activities. For example, it may highly evaluate positive posts and posts related to social contributions. The AI ​​may also integrate different data sources and build a system to comprehensively evaluate citizens' behavior. For example, it may combine online activities with actual behavioral data to perform the evaluation. The AI ​​may also analyze social media data and evaluate citizens' social influence. For example, it may evaluate based on the number of followers and engagement rate. This allows for more accurate evaluations by integrating different data sources.

[0036] The ranking unit can perform evaluations from an international perspective based on the evaluation criteria of other countries or regions. For example, the ranking unit uses AI to collect evaluation criteria from other countries or regions and set ranks for citizens based on them. For example, it uses international volunteer activity standards as a reference. The AI ​​also builds a system that analyzes data from other countries to perform evaluations from an international perspective. For example, it incorporates evaluation criteria for social contribution activities from other countries. The AI ​​also develops algorithms that evaluate citizens' behavior based on international evaluation standards. For example, it gives high marks to behavior that has received international awards or certifications. In this way, evaluations from an international perspective make it possible to evaluate citizens' behavior based on a broader range of standards.

[0037] The behavioral data collection unit can analyze video data from surveillance cameras and evaluate the frequency or patterns of behavior. For example, the behavioral data collection unit uses AI to analyze video data from surveillance cameras and detect the frequency and patterns of behavior. For example, regularly picking up trash may be highly rated. The AI ​​also analyzes temporal patterns in the behavioral data and develops an algorithm that highly rates specific behaviors when they are repeated. For example, participating in volunteer activities every week may be highly rated. The AI ​​also builds a system that comprehensively evaluates the frequency and patterns of behavior. For example, it highly rates the behavior of continuously contributing to society over a long period of time. This makes it possible to evaluate the frequency and patterns of behavior and evaluate citizens' behavior in more detail.

[0038] The behavioral data collection unit can analyze video data from surveillance cameras and evaluate behavior based on the context of the behavior. For example, the behavioral data collection unit uses AI to analyze video data from surveillance cameras and develop algorithms that take the context of the behavior into account. For example, it evaluates behavior in specific locations and time periods. The AI ​​also integrates behavioral data and contextual data to build a system that makes appropriate evaluations. For example, it highly evaluates social contribution activities in public places. The AI ​​also quantifies the context of the behavior and develops algorithms that make appropriate evaluations. For example, it evaluates behavior in specific events or situations. This allows for a more accurate evaluation of citizens' behavior by taking the context of the behavior into account.

[0039] The behavioral data collection unit can integrate video data from different perspectives and perform comprehensive behavioral evaluations. For example, the behavioral data collection unit will build a system in which AI integrates video data from multiple surveillance cameras and performs comprehensive behavioral evaluations. For example, behavior will be evaluated by combining video from different angles. The AI ​​will also develop algorithms that analyze video data from different perspectives and grasp the overall picture of behavior. For example, it will synchronize and evaluate video from multiple cameras. The AI ​​will also integrate and analyze video data and develop a system that performs comprehensive behavioral evaluations. For example, it will evaluate behavior in different locations using a single evaluation standard. This will allow for a more detailed evaluation of citizens' behavior by integrating video data from different perspectives.

[0040] The behavioral data collection unit can integrate other sensor data and evaluate behavior. For example, the behavioral data collection unit builds a system in which AI integrates video data and audio data from surveillance cameras to evaluate behavior. For example, it combines video and audio to evaluate behavior. In addition, AI develops an algorithm that integrates video data and temperature data to evaluate behavior. For example, it evaluates behavior under specific temperature conditions. In addition, AI develops a system that integrates other sensor data and evaluates behavior. For example, it combines video data and environmental data to evaluate behavior. In this way, by integrating other sensor data, it is possible to more accurately evaluate citizens' behavior.

[0041] When analyzing behavioral data, the ranking unit takes into account the range of influence and can highly evaluate behaviors that have a wide-ranging impact. For example, the ranking unit develops an algorithm in which AI analyzes behavioral data and evaluates the range of influence of an action. For example, it highly evaluates actions that have a positive impact on the entire local community. Furthermore, the AI ​​integrates behavioral data with local data to evaluate the range of influence of an action. For example, it highly evaluates actions that are involved in planning and running local events. Furthermore, the AI ​​quantifies the range of influence of an action and builds a system that highly evaluates actions that have a wide-ranging impact. For example, it evaluates actions that contribute to solving problems in the local community. In this way, by highly evaluating actions that have a wide-ranging impact, it is possible to promote contributions to society as a whole.

[0042] When analyzing behavioral data, the ranking unit can evaluate consistency and continuity, placing emphasis on long-term social contribution. For example, the ranking unit uses AI to collect behavioral data of citizens over a long period of time and evaluate the consistency of behavior. For example, it may highly evaluate people who regularly participate in volunteer activities. Furthermore, the AI ​​may analyze temporal patterns in the behavioral data to evaluate the continuity of behavior. For example, it may highly evaluate people who continuously engage in social contribution activities over a long period of time. Furthermore, the AI ​​may develop an algorithm that comprehensively evaluates the consistency and continuity of behavior. For example, it may set evaluation criteria that place emphasis on long-term social contribution over short-term actions. This may promote sustainable social contribution activities by emphasizing long-term social contribution.

[0043] The ranking unit can integrate different data sources and perform a comprehensive evaluation. For example, the ranking unit uses AI to collect social media posting data and evaluate citizens' online activities. For example, it may highly evaluate positive posts and posts related to social contributions. The AI ​​may also integrate different data sources and build a system to comprehensively evaluate citizens' behavior. For example, it may combine online activities with actual behavioral data to perform the evaluation. The AI ​​may also analyze social media data and evaluate citizens' social influence. For example, it may evaluate based on the number of followers and engagement rate. This allows for more accurate evaluations by integrating different data sources.

[0044] The ranking unit can refer to the evaluation standards of other countries and regions and conduct evaluations from an international perspective. For example, the ranking unit uses AI to collect evaluation standards of other countries and regions and set ranks for citizens based on them. For example, it refers to international volunteer activity standards. The AI ​​also builds a system that analyzes data from other countries to conduct evaluations from an international perspective. For example, it incorporates evaluation standards for social contribution activities in other countries. The AI ​​also develops algorithms that evaluate citizens' behavior based on international evaluation standards. For example, it gives high marks to behavior that has received international awards or certifications. In this way, evaluations from an international perspective make it possible to evaluate citizens' behavior based on a broader range of standards.

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

[0046] The national ranking system may further include a health data collection unit. The health data collection unit may collect, for example, heart rate, step count, sleep data, etc. from wearable devices. This may enable evaluation of the health status of citizens and highly evaluate people with healthy lifestyles. The health data collection unit may also collect the results of regular health checkups and highly evaluate people who make efforts to maintain their health. Furthermore, the health data collection unit may collect diet and exercise records and highly evaluate people who lead balanced lives.

[0047] The national ranking system may further include an education data collection unit. The education data collection unit may collect, for example, learning history and grade data from an online learning platform. This may enable people who make an effort to improve themselves and their skills to be highly evaluated. The education data collection unit may also collect data on qualification acquisition and training participation, allowing people who continue to study to be evaluated. Furthermore, the education data collection unit may collect records of reading and cultural activities, allowing people who deepen their knowledge and culture to be evaluated.

[0048] The citizen ranking system may further include an environmental data collection unit. The environmental data collection unit may collect, for example, electricity consumption data from smart meters and highly evaluate people who live energy-efficient lives. The environmental data collection unit may also collect data on recycling and eco-friendly activities and highly evaluate people who contribute to environmental protection. Furthermore, the environmental data collection unit may collect data on public transportation usage and highly evaluate people who practice low-carbon lifestyles.

[0049] The citizen ranking system may further include a social relationship data collection unit. The social relationship data collection unit may, for example, collect social media data and evaluate the frequency of interactions and communication with others. The social relationship data collection unit may also collect participation data in local communities and evaluate people who contribute to the local community. The social relationship data collection unit may also evaluate relationships with family and friends and evaluate people who build good relationships.

[0050] The national ranking system can further include a cultural activity data collection unit. The cultural activity data collection unit can collect, for example, visit histories of art galleries and museums, and highly evaluate people who are active in cultural activities. The cultural activity data collection unit can also collect music and theater appreciation histories, and evaluate people who are interested in the arts. Furthermore, the cultural activity data collection unit can collect participation data on local traditional events and festivals, and evaluate people who contribute to the preservation of culture.

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

[0052] Step 1: Surveillance cameras monitor the behavior of citizens. For example, surveillance cameras throughout the city capture people's behavior and collect video data. Surveillance cameras are also installed in public places and major facilities, allowing them to record citizens' behavior in real time. Step 2: The behavioral data collection unit collects video data collected by surveillance cameras. For example, the video data from the surveillance cameras is sent to a server and stored in a database. The behavioral data collection unit can also analyze the video data and extract specific behavioral patterns. Step 3: The rank setting unit sets ranks based on the behavioral data collected by the behavioral data collection unit. For example, the AI ​​analyzes the behavioral data and assigns a higher rank to people who actively participate in social contribution activities and volunteer activities. The AI ​​can also assign a lower rank to people who have engaged in illegal activities or socially condemned behavior.

[0053] (Example 2) The citizen ranking system according to an embodiment of the present invention is a system that monitors the behavior of citizens and sets ranks based on that data, thereby correcting unfairness among citizens and maintaining order and fairness in society as a whole.

[0054] A citizen ranking system according to an embodiment includes a surveillance camera, a behavioral data collection unit, and a rank setting unit. The surveillance camera monitors citizens' behavior. For example, surveillance cameras throughout the city capture images of people's behavior and collect the video data. Surveillance cameras are also installed in public places and major facilities, allowing for real-time recording of citizens' behavior. The behavioral data collection unit collects the video data collected by the surveillance camera. For example, the surveillance camera video data is transmitted to a server and stored in a database. The behavioral data collection unit can also analyze the video data and extract specific behavioral patterns. The rank setting unit sets ranks based on the behavioral data collected by the behavioral data collection unit. For example, AI can analyze the behavioral data and assign higher ranks to individuals who actively participate in social contribution activities and volunteer activities. AI can also assign lower ranks to individuals who have engaged in illegal activities or socially condemned behavior. In this way, the citizen ranking system can correct inequality among citizens by monitoring citizens' behavior and assigning ranks based on that data.

[0055] The rank setting unit can assign a higher rank to people who actively participate in social contribution activities or volunteer activities. For example, the rank setting unit uses AI to analyze video data from surveillance cameras and assign a higher rank to people who actively participate in social contribution activities or volunteer activities. For example, AI evaluates actions such as picking up trash or following traffic rules and assigns a higher rank to them. AI can also perform evaluations based on the frequency of participation in social contribution activities or volunteer activities and the content of the activities. For example, AI can assign a higher rank to people who regularly participate in volunteer activities. This makes it possible to highly evaluate people who actively participate in social contribution activities or volunteer activities.

[0056] The rank setting unit can assign a lower rank to people who have committed illegal acts or socially reprehensible acts. For example, the rank setting unit uses AI to analyze video data from surveillance cameras and assign a lower rank to people who have committed illegal acts or socially reprehensible acts. For example, AI detects acts such as traffic violations or theft and assigns a lower rank to them. AI can also perform evaluations based on acts that are contrary to public order and morals or nuisance behavior. For example, AI detects nuisance behavior in public places and assigns a lower rank to them. This makes it possible to assign a lower rank to people who have committed illegal acts or socially reprehensible acts.

[0057] The behavioral data collection unit can collect video data from surveillance cameras. For example, the behavioral data collection unit collects video data from surveillance cameras in real time. For example, surveillance cameras around town capture people's behavior and send the video data to a server. The behavioral data collection unit can also analyze the recorded video data and extract specific behavioral patterns. For example, AI can analyze the video data and detect behaviors such as picking up trash and following traffic rules. In this way, by collecting video data from surveillance cameras, it is possible to monitor the behavior of citizens.

[0058] The ranking unit uses an emotion estimation function to evaluate the emotions behind actions and can highly evaluate emotionally positive actions. For example, the ranking unit uses AI to analyze surveillance camera video data and estimate citizens' emotions using facial expression recognition technology. For example, it detects smiling or happy expressions and evaluates such actions as positive. The AI ​​also uses voice analysis technology to estimate emotions from the content and tone of citizens' conversations. For example, it evaluates kind words and encouraging words as positive actions. The AI ​​also integrates behavioral data and emotional data to develop an algorithm that highly evaluates emotionally positive actions. For example, it evaluates helping and cooperative actions together with emotional data. This allows for a more accurate evaluation of citizens' behavior by highly evaluating emotionally positive actions.

[0059] The ranking unit can evaluate the consistency or continuity of behavior and place emphasis on long-term social contribution. For example, the ranking unit uses AI to collect behavioral data of citizens over a long period of time and evaluate the consistency of behavior. For example, it highly rates people who regularly participate in volunteer activities. Furthermore, the AI ​​analyzes temporal patterns of the behavioral data to evaluate the continuity of behavior. For example, it highly rates people who continuously engage in social contribution activities over a long period of time. Furthermore, the AI ​​develops an algorithm that comprehensively evaluates the consistency and continuity of behavior. For example, it sets evaluation criteria that place emphasis on long-term social contribution over short-term actions. This makes it possible to promote sustainable social contribution activities by emphasizing long-term social contribution.

[0060] The ranking unit can highly evaluate actions that have a wide-ranging impact based on the impact range of the action. For example, the ranking unit uses AI to analyze action data and develop an algorithm to evaluate the impact range of an action. For example, it highly evaluates actions that have a positive impact on the entire local community. Furthermore, the AI ​​integrates action data with local data to evaluate the impact range of an action. For example, it highly evaluates actions that are involved in planning and running local events. Furthermore, the AI ​​quantifies the impact range of an action and builds a system that highly evaluates actions that have a wide-ranging impact. For example, it evaluates actions that contribute to solving problems in the local community. In this way, by highly evaluating actions that have a wide-ranging impact, it is possible to promote contributions to society as a whole.

[0061] The ranking unit can integrate different data sources and perform a comprehensive evaluation. For example, the ranking unit uses AI to collect social media posting data and evaluate citizens' online activities. For example, it may highly evaluate positive posts and posts related to social contributions. The AI ​​may also integrate different data sources and build a system to comprehensively evaluate citizens' behavior. For example, it may combine online activities with actual behavioral data to perform the evaluation. The AI ​​may also analyze social media data and evaluate citizens' social influence. For example, it may evaluate based on the number of followers and engagement rate. This allows for more accurate evaluations by integrating different data sources.

[0062] The ranking unit can perform evaluations from an international perspective based on the evaluation criteria of other countries or regions. For example, the ranking unit uses AI to collect evaluation criteria from other countries or regions and set ranks for citizens based on them. For example, it uses international volunteer activity standards as a reference. The AI ​​also builds a system that analyzes data from other countries to perform evaluations from an international perspective. For example, it incorporates evaluation criteria for social contribution activities from other countries. The AI ​​also develops algorithms that evaluate citizens' behavior based on international evaluation standards. For example, it gives high marks to behavior that has received international awards or certifications. In this way, evaluations from an international perspective make it possible to evaluate citizens' behavior based on a broader range of standards.

[0063] The ranking unit uses the emotion estimation function to evaluate the emotions behind actions and can highly evaluate emotionally positive actions. For example, the ranking unit uses the emotion estimation function to analyze citizens' behavioral data and highly evaluate emotionally positive actions. For example, it detects smiling and happy facial expressions. The AI ​​also uses voice analysis technology to estimate emotions from the content and tone of citizens' conversations and evaluates positive actions. For example, it evaluates kind language and encouraging words. The AI ​​also integrates behavioral data and emotion data to develop an algorithm that highly evaluates emotionally positive actions. For example, it evaluates helping and cooperative actions. This makes it possible to more accurately evaluate citizens' behavior by highly evaluating emotionally positive actions.

[0064] The behavioral data collection unit can analyze surveillance camera video data in real time and perform emotion estimation. For example, the behavioral data collection unit analyzes surveillance camera video data in real time and estimates emotions using facial expression recognition technology. For example, it detects smiling and happy expressions. The AI ​​also estimates emotions from surveillance camera video data using voice analysis technology. For example, it detects kind words and encouraging words. The AI ​​also integrates the video data and emotion data to develop an algorithm that identifies emotionally positive behavior. For example, it identifies helping and cooperative behavior. This makes it possible to instantly evaluate citizens' behavior by performing emotion estimation in real time.

[0065] The behavioral data collection unit can analyze video data from surveillance cameras and evaluate the frequency or patterns of behavior. For example, the behavioral data collection unit uses AI to analyze video data from surveillance cameras and detect the frequency and patterns of behavior. For example, regularly picking up trash may be highly rated. The AI ​​also analyzes temporal patterns in the behavioral data and develops an algorithm that highly rates specific behaviors when they are repeated. For example, participating in volunteer activities every week may be highly rated. The AI ​​also builds a system that comprehensively evaluates the frequency and patterns of behavior. For example, it highly rates the behavior of continuously contributing to society over a long period of time. This makes it possible to evaluate the frequency and patterns of behavior and evaluate citizens' behavior in more detail.

[0066] The behavioral data collection unit can analyze video data from surveillance cameras and evaluate behavior based on the context of the behavior. For example, the behavioral data collection unit uses AI to analyze video data from surveillance cameras and develop algorithms that take the context of the behavior into account. For example, it evaluates behavior in specific locations and time periods. The AI ​​also integrates behavioral data and contextual data to build a system that makes appropriate evaluations. For example, it highly evaluates social contribution activities in public places. The AI ​​also quantifies the context of the behavior and develops algorithms that make appropriate evaluations. For example, it evaluates behavior in specific events or situations. This allows for a more accurate evaluation of citizens' behavior by taking the context of the behavior into account.

[0067] The behavioral data collection unit can integrate video data from different perspectives and perform comprehensive behavioral evaluations. For example, the behavioral data collection unit will build a system in which AI integrates video data from multiple surveillance cameras and performs comprehensive behavioral evaluations. For example, behavior will be evaluated by combining video from different angles. The AI ​​will also develop algorithms that analyze video data from different perspectives and grasp the overall picture of behavior. For example, it will synchronize and evaluate video from multiple cameras. The AI ​​will also integrate and analyze video data and develop a system that performs comprehensive behavioral evaluations. For example, it will evaluate behavior in different locations using a single evaluation standard. This will allow for a more detailed evaluation of citizens' behavior by integrating video data from different perspectives.

[0068] The behavioral data collection unit can integrate other sensor data and evaluate behavior. For example, the behavioral data collection unit builds a system in which AI integrates video data and audio data from surveillance cameras to evaluate behavior. For example, it combines video and audio to evaluate behavior. In addition, AI develops an algorithm that integrates video data and temperature data to evaluate behavior. For example, it evaluates behavior under specific temperature conditions. In addition, AI develops a system that integrates other sensor data and evaluates behavior. For example, it combines video data and environmental data to evaluate behavior. In this way, by integrating other sensor data, it is possible to more accurately evaluate citizens' behavior.

[0069] The behavioral data collection unit can use the emotion estimation function to analyze surveillance camera video data in real time and identify emotionally positive behavior. For example, the behavioral data collection unit uses AI to analyze surveillance camera video data in real time and estimate emotions using facial expression recognition technology. For example, it detects smiling and happy expressions. The AI ​​also uses voice analysis technology to estimate emotions from surveillance camera video data. For example, it detects kind words and words of encouragement. The AI ​​also integrates the video data and emotion data to develop an algorithm that identifies emotionally positive behavior. For example, it identifies helping and cooperative behavior. This makes it possible to more accurately evaluate public behavior by identifying emotionally positive behavior.

[0070] When analyzing behavioral data, the ranking unit uses an emotion estimation function to evaluate the emotions behind the behavior and can highly evaluate emotionally positive behavior. For example, the ranking unit uses AI to analyze video data from surveillance cameras and estimate citizens' emotions using facial expression recognition technology. For example, it detects smiling or happy expressions and evaluates the behavior as positive. The AI ​​also uses voice analysis technology to estimate emotions from the content and tone of citizens' conversations. For example, it evaluates kind words and encouraging words as positive behavior. The AI ​​also integrates behavioral data and emotion data to develop an algorithm that highly evaluates emotionally positive behavior. For example, it evaluates helping and cooperative behavior together with emotion data. This allows for a more accurate evaluation of citizens' behavior by highly evaluating emotionally positive behavior.

[0071] When analyzing behavioral data, the ranking unit takes into account the range of influence and can highly evaluate behaviors that have a wide-ranging impact. For example, the ranking unit develops an algorithm in which AI analyzes behavioral data and evaluates the range of influence of an action. For example, it highly evaluates actions that have a positive impact on the entire local community. Furthermore, the AI ​​integrates behavioral data with local data to evaluate the range of influence of an action. For example, it highly evaluates actions that are involved in planning and running local events. Furthermore, the AI ​​quantifies the range of influence of an action and builds a system that highly evaluates actions that have a wide-ranging impact. For example, it evaluates actions that contribute to solving problems in the local community. In this way, by highly evaluating actions that have a wide-ranging impact, it is possible to promote contributions to society as a whole.

[0072] When analyzing behavioral data, the ranking unit can evaluate consistency and continuity, placing emphasis on long-term social contribution. For example, the ranking unit uses AI to collect behavioral data of citizens over a long period of time and evaluate the consistency of behavior. For example, it may highly evaluate people who regularly participate in volunteer activities. Furthermore, the AI ​​may analyze temporal patterns in the behavioral data to evaluate the continuity of behavior. For example, it may highly evaluate people who continuously engage in social contribution activities over a long period of time. Furthermore, the AI ​​may develop an algorithm that comprehensively evaluates the consistency and continuity of behavior. For example, it may set evaluation criteria that place emphasis on long-term social contribution over short-term actions. This may promote sustainable social contribution activities by emphasizing long-term social contribution.

[0073] The ranking unit can integrate different data sources and perform a comprehensive evaluation. For example, the ranking unit uses AI to collect social media posting data and evaluate citizens' online activities. For example, it may highly evaluate positive posts and posts related to social contributions. The AI ​​may also integrate different data sources and build a system to comprehensively evaluate citizens' behavior. For example, it may combine online activities with actual behavioral data to perform the evaluation. The AI ​​may also analyze social media data and evaluate citizens' social influence. For example, it may evaluate based on the number of followers and engagement rate. This allows for more accurate evaluations by integrating different data sources.

[0074] The ranking unit can refer to the evaluation standards of other countries and regions and conduct evaluations from an international perspective. For example, the ranking unit uses AI to collect evaluation standards of other countries and regions and set ranks for citizens based on them. For example, it refers to international volunteer activity standards. The AI ​​also builds a system that analyzes data from other countries to conduct evaluations from an international perspective. For example, it incorporates evaluation standards for social contribution activities in other countries. The AI ​​also develops algorithms that evaluate citizens' behavior based on international evaluation standards. For example, it gives high marks to behavior that has received international awards or certifications. In this way, evaluations from an international perspective make it possible to evaluate citizens' behavior based on a broader range of standards.

[0075] The ranking unit uses the emotion estimation function to evaluate the emotions behind actions and can highly evaluate emotionally positive actions. For example, the ranking unit uses the emotion estimation function to analyze citizens' behavioral data and highly evaluate emotionally positive actions. For example, it detects smiling and happy facial expressions. The AI ​​also uses voice analysis technology to estimate emotions from the content and tone of citizens' conversations and evaluates positive actions. For example, it evaluates kind language and encouraging words. The AI ​​also integrates behavioral data and emotion data to develop an algorithm that highly evaluates emotionally positive actions. For example, it evaluates helping and cooperative actions. This makes it possible to more accurately evaluate citizens' behavior by highly evaluating emotionally positive actions.

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

[0077] The national ranking system may further include a health data collection unit. The health data collection unit may collect, for example, heart rate, step count, sleep data, etc. from wearable devices. This may enable evaluation of the health status of citizens and highly evaluate people with healthy lifestyles. The health data collection unit may also collect the results of regular health checkups and highly evaluate people who make efforts to maintain their health. Furthermore, the health data collection unit may collect diet and exercise records and highly evaluate people who lead balanced lives.

[0078] The national ranking system may further include an education data collection unit. The education data collection unit may collect, for example, learning history and grade data from an online learning platform. This may enable people who make an effort to improve themselves and their skills to be highly evaluated. The education data collection unit may also collect data on qualification acquisition and training participation, allowing people who continue to study to be evaluated. Furthermore, the education data collection unit may collect records of reading and cultural activities, allowing people who deepen their knowledge and culture to be evaluated.

[0079] The citizen ranking system may further include an environmental data collection unit. The environmental data collection unit may collect, for example, electricity consumption data from smart meters and highly evaluate people who live energy-efficient lives. The environmental data collection unit may also collect data on recycling and eco-friendly activities and highly evaluate people who contribute to environmental protection. Furthermore, the environmental data collection unit may collect data on public transportation usage and highly evaluate people who practice low-carbon lifestyles.

[0080] The citizen ranking system may further include a social relationship data collection unit. The social relationship data collection unit may, for example, collect social media data and evaluate the frequency of interactions and communication with others. The social relationship data collection unit may also collect participation data in local communities and evaluate people who contribute to the local community. The social relationship data collection unit may also evaluate relationships with family and friends and evaluate people who build good relationships.

[0081] The national ranking system can further include a cultural activity data collection unit. The cultural activity data collection unit can collect, for example, visit histories of art galleries and museums, and highly evaluate people who are active in cultural activities. The cultural activity data collection unit can also collect music and theater appreciation histories, and evaluate people who are interested in the arts. Furthermore, the cultural activity data collection unit can collect participation data on local traditional events and festivals, and evaluate people who contribute to the preservation of culture.

[0082] The ranking unit can use emotion estimation to evaluate stress levels and give higher marks to people who are good at managing their stress. For example, AI can use facial expression recognition technology to detect signs of stress. It can also use voice analysis technology to estimate stress levels from the tone and content of conversations. Furthermore, it can integrate health data and emotion data to develop an algorithm to evaluate people's ability to manage stress. This can promote the mental health of the nation by giving higher marks to people who are good at managing their stress.

[0083] The ranking unit can use the emotion estimation function to evaluate empathy and highly evaluate people who behave empathetically toward others. For example, AI can use facial expression recognition technology to detect empathetic facial expressions. It can also use voice analysis technology to evaluate empathetic language from the content and tone of conversations. Furthermore, it can develop an algorithm that integrates behavioral data and emotional data to evaluate empathetic behavior. This can improve human relationships throughout society by highly evaluating people with empathy.

[0084] The ranking unit can use the emotion estimation function to evaluate emotional stability and give a higher rating to people who can control their emotions. For example, AI can use facial expression recognition technology to detect emotional fluctuations. It can also use voice analysis technology to evaluate emotional stability from the tone and content of conversations. Furthermore, it can integrate behavioral data and emotional data to develop an algorithm that evaluates people who can control their emotions. This can improve the stability of society as a whole by giving a higher rating to people who can control their emotions.

[0085] The ranking unit can use the emotion estimation function to evaluate behaviors that provide emotional support and highly evaluate people who support others. For example, AI can use facial expression recognition technology to detect supportive facial expressions. It can also use voice analysis technology to evaluate supportive language from the content and tone of conversations. Furthermore, it can develop an algorithm that integrates behavioral data and emotional data to evaluate behaviors that provide emotional support. This can strengthen support networks throughout society by highly evaluating behaviors that support others.

[0086] The ranking unit can use the emotion estimation function to evaluate emotional leadership and highly evaluate people who demonstrate emotional leadership. For example, AI can use facial expression recognition technology to detect leadership expressions. It can also use voice analysis technology to evaluate leadership based on the content and tone of conversation. Furthermore, it can integrate behavioral data and emotional data to develop an algorithm for evaluating emotional leadership. This can improve leadership throughout society by highly evaluating people who demonstrate emotional leadership.

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

[0088] Step 1: Surveillance cameras monitor the behavior of citizens. For example, surveillance cameras throughout the city capture people's behavior and collect video data. Surveillance cameras are also installed in public places and major facilities, allowing them to record citizens' behavior in real time. Step 2: The behavioral data collection unit collects video data collected by surveillance cameras. For example, the video data from the surveillance cameras is sent to a server and stored in a database. The behavioral data collection unit can also analyze the video data and extract specific behavioral patterns. Step 3: The rank setting unit sets ranks based on the behavioral data collected by the behavioral data collection unit. For example, the AI ​​analyzes the behavioral data and assigns a higher rank to people who actively participate in social contribution activities and volunteer activities. The AI ​​can also assign a lower rank to people who have engaged in illegal activities or socially condemned behavior.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] 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.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Surveillance cameras and a behavioral data collection department that collects behavioral data of citizens; a rank setting unit that sets a rank based on the behavioral data collected by the behavioral data collecting unit. A system characterized by:

2. The rank setting unit Higher rankings are given to people who actively participate in social contribution or volunteer activities. The system of claim 1 .

3. The rank setting unit People who have committed illegal or socially reprehensible acts are given a lower rank. The system of claim 1 .

4. The behavioral data collection unit Collecting video data from the surveillance cameras The system of claim 1 .

5. The rank setting unit Evaluate the emotions behind behaviors and reward emotionally positive behaviors The system of claim 1 .

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