system

Abstract

The system according to this embodiment aims to efficiently evaluate the safety level of a region and take appropriate measures. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. The provision unit provides the evaluation results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently evaluate the safety level of a region and take appropriate measures.

[0005] The system according to the embodiment aims to efficiently evaluate the safety level of a region and take appropriate measures.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires information such as the flow of people, face recognition, and time zone. The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. The provision unit provides the evaluation result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently assess the safety level of a region and take appropriate measures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The safety evaluation system according to an embodiment of the present invention is a system that evaluates the safety level of an area by mounting sensors and cameras on utility poles installed in parks, continuously acquiring information such as pedestrian flow, facial recognition, and time of day, and analyzing the data using AI technology. In this safety evaluation system, sensors and cameras installed on utility poles acquire information such as pedestrian flow, facial recognition, and time of day, and the acquired information is analyzed by AI to analyze the safety level of the area. The analysis results are provided to organizations such as police and schools and are used to help in park development and crime prevention plan formulation. It also contributes to the early detection of suspicious persons. For example, in the safety evaluation system, sensors and cameras installed on utility poles acquire information such as pedestrian flow, facial recognition, and time of day. In this process, sensors detect human movement, and cameras use facial recognition technology to identify individual persons. For example, the movements of people walking in a park can be monitored in real time, and suspicious persons can be identified using facial recognition technology. Next, the safety evaluation system uses AI to analyze the acquired information. Based on the acquired data, the AI ​​evaluates the safety level of the area. For example, if there is a high volume of people at a particular time of day, or if a particular person is frequently entering or leaving an area, AI can analyze this information and assess the safety level. Furthermore, the safety assessment system provides the analysis results to organizations such as police and schools. This can be used to develop parks and crime prevention plans. For example, if there is a high volume of people at a particular time of day, measures such as deploying security guards during that time can be taken. The safety assessment system also contributes to the early detection of suspicious individuals. By using facial recognition technology to identify suspicious individuals and reporting them to the police, a quick response can be achieved. For example, if a person with a criminal record is in the park, the AI ​​can identify that person and report them to the police, allowing for a swift response. In this way, the safety assessment system can evaluate the safety level of an area by equipping utility poles in parks with sensors and cameras and analyzing the data using AI technology, which can be used to develop parks and crime prevention plans. It also contributes to the early detection of suspicious individuals. Thus, the safety assessment system can evaluate the safety level of an area and contribute to the development of parks and crime prevention plans. It also contributes to the early detection of suspicious individuals.

[0029] The safety evaluation system according to the embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. The acquisition unit acquires information using, for example, sensors or cameras installed on utility poles. Sensors detect human movement, and cameras identify individual people using facial recognition technology. For example, the acquisition unit can monitor the movements of people walking in a park in real time and identify suspicious individuals using facial recognition technology. The acquisition unit can also acquire information using, for example, temperature sensors or motion detection sensors. The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. The analysis unit analyzes pedestrian flow during a specific time period based on the acquired data. For example, the analysis unit can analyze information such as when there is a large pedestrian flow during a specific time period or when a particular person frequently enters and exits, and evaluate the safety level. The analysis unit can also identify suspicious individuals using, for example, facial recognition technology. The analysis unit can also evaluate the safety level by referring to, for example, past crime data. The provision unit provides the evaluation results obtained by the analysis unit. The providing unit can, for example, provide the analysis results to organizations such as police departments and schools. The providing unit can, for example, use the analysis results to help develop parks and crime prevention plans. The providing unit can, for example, take measures such as deploying security guards during specific time periods based on the analysis results. Thus, the safety evaluation system according to the embodiment can acquire and analyze information such as pedestrian flow, facial recognition, and time period, evaluate the safety level, and provide the results.

[0030] The data acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. For example, the data acquisition unit acquires information using sensors and cameras installed on utility poles. Sensors detect human movement, and cameras use facial recognition technology to identify individual people. Specifically, sensors installed on utility poles use infrared or ultrasonic waves to detect human movement and collect the data in real time. Cameras capture high-resolution video and use facial recognition algorithms to identify people in the video. For example, the data acquisition unit can monitor the movements of people walking in a park in real time and identify suspicious individuals using facial recognition technology. Facial recognition technology compares the faces with a pre-registered facial database to identify matching individuals. This allows the data acquisition unit to confirm whether a specific person is in the park. Furthermore, the data acquisition unit can also acquire information using temperature sensors and motion detection sensors. Temperature sensors detect changes in ambient temperature and sense when a person approaches. Motion detection sensors detect moving objects and track their movement. This allows the data acquisition unit to combine various sensors to collect more accurate information. The data acquisition unit centrally manages the data obtained from these sensors and cameras and transmits it to a central database in real time. This allows the data acquisition unit to efficiently collect information over a wide area and improve the overall performance of the system.

[0031] The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. For example, the analysis unit analyzes pedestrian traffic during specific time periods based on the acquired data. Specifically, the analysis unit uses AI to analyze the collected data in real time and understand patterns and trends in pedestrian traffic. The AI ​​uses machine learning algorithms to learn from past data and predict the current situation. For example, it can analyze information such as high pedestrian traffic during specific time periods or frequent entry and exit of specific individuals to evaluate the safety level. The analysis unit can also identify suspicious individuals using facial recognition technology. The facial recognition technology analyzes video data sent from the acquisition unit and compares it with a pre-registered list of suspicious individuals. This allows the analysis unit to quickly determine whether a suspicious person is in a particular area. Furthermore, the analysis unit can also evaluate the safety level by referring to past crime data. Based on past crime data, it can evaluate the crime risk in specific areas and time periods and take preventive measures. The analysis unit integrates this data to conduct a comprehensive safety assessment. This allows the analysis unit to not only grasp the situation in real time but also to conduct long-term risk assessments and trend analyses. The analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to improve the overall reliability and safety of the system.

[0032] The Service Provider provides the evaluation results obtained by the Analysis Provider. For example, the Service Provider provides analysis results to organizations such as police departments and schools. Specifically, the Service Provider notifies relevant organizations of the analysis results in real time to support a rapid response. For example, the analysis results can be used to develop parks or crime prevention plans. Based on the analysis results, the Service Provider can take measures such as deploying security guards during specific time periods. The Service Provider provides a dashboard to visually display the analysis results in an easy-to-understand manner, allowing stakeholders to easily grasp the information. The dashboard displays real-time data on graphs and maps, allowing for an intuitive understanding of changes in the situation. Furthermore, the Service Provider regularly creates reports based on the analysis results and provides them to relevant organizations. The reports include historical data, trend analysis, and risk assessment results, which can be used to inform future countermeasures. The Service Provider provides a platform to strengthen collaboration with relevant organizations and facilitate information sharing. This allows the Service Provider to provide analysis results quickly and effectively, and to support relevant organizations in taking appropriate measures. The Service Provider can also collect feedback from users and use it to improve the system. For example, based on feedback from the police and schools, the analysis algorithms and delivery methods are reviewed to improve the overall system performance. This allows the service provider to always provide highly accurate evaluation results based on the latest information, supporting quick and appropriate responses.

[0033] The acquisition unit acquires information using sensors and cameras installed on utility poles. For example, the acquisition unit can detect human movement using sensors installed on utility poles. For example, the acquisition unit can identify individual people using facial recognition technology with cameras installed on utility poles. For example, the acquisition unit can acquire temperature information using temperature sensors installed on utility poles. Thus, the acquisition unit can acquire information using sensors and cameras installed on utility poles. Sensors include, but are not limited to, temperature sensors and motion detection sensors. Cameras include, but are not limited to, fixed cameras and PTZ cameras. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input data acquired from sensors and cameras installed on utility poles into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze pedestrian traffic during specific time periods based on the acquired information. For example, the analysis unit analyzes pedestrian traffic during specific time periods based on the acquired data. The analysis unit can analyze this information, for example, when there is a high volume of pedestrian traffic during a specific time period, or when a particular person is frequently entering or leaving an area, and evaluate the safety level. The analysis unit can also analyze fluctuations in pedestrian traffic during specific time periods. This allows the analysis unit to analyze pedestrian traffic during specific time periods. Specific time periods include, but are not limited to, peak hours and off-peak hours. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input acquired data into a generating AI and have the generating AI perform an analysis of pedestrian traffic during specific time periods.

[0035] The analysis unit can identify suspicious individuals using facial recognition technology. For example, the analysis unit can identify suspicious individuals using facial recognition technology. For example, the analysis unit can identify a specific person using facial recognition technology and determine whether that person is suspicious. The analysis unit can also identify individuals with a criminal record using facial recognition technology. This allows the analysis unit to identify suspicious individuals using facial recognition technology. Suspicious individuals include, but are not limited to, behavioral patterns and physical characteristics. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data acquired using facial recognition technology into a generating AI and have the generating AI perform the identification of suspicious individuals.

[0036] The service provider can provide the analysis results to organizations such as police and schools. For example, the service provider can provide the analysis results to organizations such as police and schools. For example, the service provider can take measures based on the analysis results, such as deploying security guards during specific time periods. For example, the service provider can use the analysis results to help in park development and crime prevention plan formulation. In this way, the service provider can provide the analysis results to organizations such as police and schools. Organizations include, but are not limited to, police, schools, and local governments. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate the information to be provided.

[0037] The service provider can use the analysis results to help develop parks and crime prevention plans. For example, the service provider can use the analysis results to help develop parks and crime prevention plans. For example, the service provider can take measures such as deploying security guards during specific time periods based on the analysis results. For example, the service provider can revise park design standards and safety standards based on the analysis results. This allows the service provider to use the analysis results to help develop parks and crime prevention plans. Park development includes, for example, design standards and safety standards, but is not limited to such examples. Crime prevention plans include, for example, the locations of security cameras and patrol routes, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate information useful for developing parks and crime prevention plans.

[0038] The acquisition unit can analyze past data and determine the optimal sensor placement. For example, the acquisition unit can place sensors in the most crowded locations based on past pedestrian flow data. For example, the acquisition unit can install cameras in areas with a high crime rate based on past crime data. For example, the acquisition unit can place sensors in locations where many people gather during specific time periods based on past time-of-day data. This allows the acquisition unit to analyze past data and determine the optimal sensor placement. The optimal sensor placement includes, but is not limited to, coverage area and distance between sensors. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past data into a generating AI and have the generating AI determine the optimal sensor placement.

[0039] The data acquisition unit can change its acquisition method according to specific events or seasons when acquiring information. For example, during an event, the acquisition unit can increase the frequency of acquiring information around the event venue. For example, the acquisition unit can change the sensor settings to take into account different pedestrian flow patterns depending on the season. For example, during a specific festival or event, the acquisition unit can increase the camera resolution to acquire more detailed information. This allows the acquisition unit to change its acquisition method according to specific events or seasons. Specific events include, but are not limited to, festivals and sporting events. Seasons include, but are not limited to, spring, summer, autumn, and winter. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input data corresponding to a specific event or season into a generating AI and have the generating AI execute the change in the acquisition method.

[0040] The acquisition unit can adjust its acquisition method when acquiring information, taking weather information into consideration. For example, in rainy weather, the acquisition unit can increase the sensitivity of the outdoor sensor to acquire information. For example, in sunny weather, the acquisition unit can acquire information with normal sensitivity. For example, on snowy days, the acquisition unit can increase the resolution of the camera to ensure visibility. In this way, the acquisition unit can adjust its acquisition method considering weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can input weather information into a generating AI and have the generating AI perform the adjustment of the acquisition method.

[0041] The acquisition unit can analyze the surrounding traffic conditions and acquire relevant information when acquiring information. For example, during traffic congestion, the acquisition unit can prioritize acquiring pedestrian flow information in the congested area. For example, when a traffic accident occurs, the acquisition unit can prioritize acquiring information around the accident site. For example, during times of heavy traffic, the acquisition unit can acquire information corresponding to the traffic volume. In this way, the acquisition unit can analyze the surrounding traffic conditions and acquire relevant information. Traffic conditions include, but are not limited to, traffic volume and congestion information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can input traffic condition data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0042] The analysis unit can evaluate the safety level by referring to past crime data during analysis. For example, the analysis unit can evaluate the safety level of a specific area based on past crime data. For example, the analysis unit can evaluate the safety level by considering the time periods in which past crimes occurred. For example, the analysis unit can analyze the types of past crimes and evaluate the safety level. This allows the analysis unit to evaluate the safety level by referring to past crime data. Past crime data includes, but is not limited to, the number of crimes and the types of crimes. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past crime data into a generating AI and have the generating AI perform the safety level evaluation.

[0043] The analysis unit can apply different analysis algorithms depending on the time of day or day of the week during analysis. For example, the analysis unit can apply different analysis algorithms during the daytime and nighttime on weekdays. For example, the analysis unit can apply analysis algorithms corresponding to specific events on weekends and holidays. For example, the analysis unit can apply different analysis algorithms depending on the season. In this way, the analysis unit can apply different analysis algorithms depending on the time of day or day of the week. Different analysis algorithms include, but are not limited to, time series analysis and clustering. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data corresponding to a specific time of day or day of the week into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can evaluate the safety level by considering information about surrounding commercial facilities and residential areas during the analysis. For example, the analysis unit can evaluate the safety level by considering the operating hours of surrounding commercial facilities. For example, the analysis unit can evaluate the safety level by considering the population density of surrounding residential areas. For example, the analysis unit can evaluate the safety level by considering the crime rate of surrounding commercial facilities and residential areas. This allows the analysis unit to evaluate the safety level by considering information about surrounding commercial facilities and residential areas. Commercial facilities include, but are not limited to, shopping malls and supermarkets. Residential areas include, but are not limited to, detached houses and apartment buildings. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input information about surrounding commercial facilities and residential areas into a generating AI and have the generating AI perform the safety level evaluation.

[0045] The analysis unit can improve the accuracy of its analysis by referring to local event information during the analysis process. For example, when a local event is being held, the analysis unit prioritizes analyzing information around the event venue. For example, the analysis unit can adjust its analysis algorithm according to the type of local event. For example, the analysis unit can improve the accuracy of its analysis by considering the number of participants in a local event. This allows the analysis unit to improve the accuracy of its analysis by referring to local event information. Local event information includes, but is not limited to, event calendars and local news. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input local event information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0046] The delivery unit can select the optimal delivery method by referring to past delivery history at the time of delivery. For example, if the delivery unit has previously provided information in a detailed report format, it can provide the information in a similar format. For example, if the delivery unit has previously provided information in a concise summary format, it can provide the information in a similar format. For example, the delivery unit can select the most effective delivery method from past delivery history. This allows the delivery unit to select the optimal delivery method by referring to past delivery history. Delivery history includes, but is not limited to, past delivery dates and times, and delivery content. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input past delivery history into a generating AI and have the generating AI select the optimal delivery method.

[0047] The information provider can provide customized information to specific organizations or individuals at the time of provision. For example, the information provider can provide police with detailed information on crime rates and suspicious persons. For example, the information provider can provide schools with information on student safety. For example, the information provider can provide local residents with information on the safety level of their area. This allows the information provider to provide customized information to specific organizations or individuals. Customized information includes, but is not limited to, providing information tailored to individual needs. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the needs of a specific organization or individual into a generating AI and have the generating AI perform the generation of customized information.

[0048] The information provider can adjust its delivery method when providing information, taking into account the attribute information of the information recipient. For example, the provider can provide information to the police in the form of a detailed report. For example, the provider can provide information to schools in the form of a concise summary. For example, the provider can provide information to local residents in the form of a concise bulleted list. This allows the provider to adjust its delivery method, taking into account the attribute information of the information recipient. Attribute information includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the provider can input the attribute information of the information recipient into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0049] The information provider can optimize the content of its deliverables by referring to past feedback from the recipient of the information. For example, the provider can provide information in a similar format based on feedback received when information was previously provided in a detailed report format. For example, the provider can provide information in a similar format based on feedback received when information was previously provided in a concise summary format. For example, the provider can select the most effective content from past feedback. This allows the provider to optimize the content of its deliverables by referring to past feedback from the recipient of the information. Past feedback includes, but is not limited to, survey results and user reviews. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the provider can input past feedback into a generating AI and have the generating AI perform the optimization of the content of its deliverables.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data acquisition unit can analyze past data and determine the optimal sensor placement. For example, based on past pedestrian flow data, sensors can be placed in the areas where the most people gather. Also, based on past crime data, cameras can be installed in areas where crime frequently occurs. Furthermore, based on past time-of-day data, sensors can be placed in areas where many people gather during specific time periods. In this way, the data acquisition unit can analyze past data and determine the optimal sensor placement. The optimal sensor placement includes, but is not limited to, coverage area and distance between sensors. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input past data into a generating AI and have the generating AI determine the optimal sensor placement.

[0052] The data acquisition unit can change its acquisition method according to specific events or seasons when acquiring information. For example, during an event, it can increase the frequency of acquiring information around the event venue. It can also change the sensor settings to take into account different pedestrian flow patterns depending on the season. Furthermore, during specific festivals or events, it can increase the camera resolution to acquire more detailed information. In this way, the data acquisition unit can change its acquisition method according to specific events or seasons. Specific events include, but are not limited to, festivals and sporting events. Seasons include, but are not limited to, spring, summer, autumn, and winter. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data corresponding to specific events or seasons into a generating AI and have the generating AI execute the change in the acquisition method.

[0053] The acquisition unit can adjust its acquisition method when acquiring information, taking weather information into consideration. For example, in rainy weather, it can increase the sensitivity of the outdoor sensor to acquire information. In sunny weather, it can acquire information with normal sensitivity. Furthermore, on snowy days, it can increase the camera resolution to ensure visibility. In this way, the acquisition unit can adjust its acquisition method considering weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input weather information into a generating AI and have the generating AI perform the adjustment of the acquisition method.

[0054] The acquisition unit can analyze the surrounding traffic conditions and obtain relevant information when acquiring data. For example, during traffic congestion, it can prioritize the acquisition of pedestrian flow information in the congested area. Also, when a traffic accident occurs, it can prioritize the acquisition of information around the accident site. Furthermore, during times of heavy traffic, it can acquire information corresponding to the traffic volume. In this way, the acquisition unit can analyze the surrounding traffic conditions and acquire relevant information. Traffic conditions include, but are not limited to, traffic volume and congestion information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input traffic condition data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0055] The analysis unit can evaluate safety levels by referring to past crime data during analysis. For example, it can evaluate the safety level of a specific area based on past crime data. It can also evaluate safety levels by considering the time periods in which past crimes occurred. Furthermore, it can analyze the types of past crimes and evaluate safety levels. In this way, the analysis unit can evaluate safety levels by referring to past crime data. Past crime data includes, but is not limited to, the number of crimes and the types of crimes. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past crime data into a generating AI and have the generating AI perform the safety level evaluation.

[0056] The analysis unit can apply different analysis algorithms depending on the time of day or day of the week during analysis. For example, different analysis algorithms can be applied during the daytime and at night on weekdays. Furthermore, analysis algorithms corresponding to specific events can be applied on weekends and holidays. In addition, different analysis algorithms can be applied depending on the season. This allows the analysis unit to apply different analysis algorithms depending on the time of day or day of the week. These different analysis algorithms include, but are not limited to, time series analysis and clustering. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data corresponding to specific time of day or day of the week into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. The acquisition unit acquires information using, for example, sensors or cameras installed on utility poles. Sensors detect human movement, and cameras use facial recognition technology to identify individual people. For example, the acquisition unit can monitor the movements of people walking in a park in real time and identify suspicious individuals using facial recognition technology. The acquisition unit can also acquire information using, for example, temperature sensors or motion detection sensors. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. For example, the analysis unit analyzes pedestrian traffic during specific time periods based on the acquired data. The analysis unit can analyze this information, for example, if there is a high volume of pedestrian traffic during a particular time period, or if a particular person is frequently coming and going, and evaluate the safety level. The analysis unit can also identify suspicious individuals using facial recognition technology, for example. The analysis unit can also evaluate the safety level by referring to past crime data, for example. Step 3: The provisioning department provides the evaluation results obtained by the analysis department. The provisioning department provides the analysis results to organizations such as police departments and schools. The provisioning department can use the analysis results to help develop parks and crime prevention plans, for example. The provisioning department can take measures such as deploying security guards during specific time periods based on the analysis results.

[0059] (Example of form 2) The safety evaluation system according to an embodiment of the present invention is a system that evaluates the safety level of an area by mounting sensors and cameras on utility poles installed in parks, continuously acquiring information such as pedestrian flow, facial recognition, and time of day, and analyzing the data using AI technology. In this safety evaluation system, sensors and cameras installed on utility poles acquire information such as pedestrian flow, facial recognition, and time of day, and the acquired information is analyzed by AI to analyze the safety level of the area. The analysis results are provided to organizations such as police and schools and are used to help in park development and crime prevention plan formulation. It also contributes to the early detection of suspicious persons. For example, in the safety evaluation system, sensors and cameras installed on utility poles acquire information such as pedestrian flow, facial recognition, and time of day. In this process, sensors detect human movement, and cameras use facial recognition technology to identify individual persons. For example, the movements of people walking in a park can be monitored in real time, and suspicious persons can be identified using facial recognition technology. Next, the safety evaluation system uses AI to analyze the acquired information. Based on the acquired data, the AI ​​evaluates the safety level of the area. For example, if there is a high volume of people at a particular time of day, or if a particular person is frequently entering or leaving an area, AI can analyze this information and assess the safety level. Furthermore, the safety assessment system provides the analysis results to organizations such as police and schools. This can be used to develop parks and crime prevention plans. For example, if there is a high volume of people at a particular time of day, measures such as deploying security guards during that time can be taken. The safety assessment system also contributes to the early detection of suspicious individuals. By using facial recognition technology to identify suspicious individuals and reporting them to the police, a quick response can be achieved. For example, if a person with a criminal record is in the park, the AI ​​can identify that person and report them to the police, allowing for a swift response. In this way, the safety assessment system can evaluate the safety level of an area by equipping utility poles in parks with sensors and cameras and analyzing the data using AI technology, which can be used to develop parks and crime prevention plans. It also contributes to the early detection of suspicious individuals. Thus, the safety assessment system can evaluate the safety level of an area and contribute to the development of parks and crime prevention plans. It also contributes to the early detection of suspicious individuals.

[0060] The safety evaluation system according to the embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. The acquisition unit acquires information using, for example, sensors or cameras installed on utility poles. Sensors detect human movement, and cameras identify individual people using facial recognition technology. For example, the acquisition unit can monitor the movements of people walking in a park in real time and identify suspicious individuals using facial recognition technology. The acquisition unit can also acquire information using, for example, temperature sensors or motion detection sensors. The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. The analysis unit analyzes pedestrian flow during a specific time period based on the acquired data. For example, the analysis unit can analyze information such as when there is a large pedestrian flow during a specific time period or when a particular person frequently enters and exits, and evaluate the safety level. The analysis unit can also identify suspicious individuals using, for example, facial recognition technology. The analysis unit can also evaluate the safety level by referring to, for example, past crime data. The provision unit provides the evaluation results obtained by the analysis unit. The providing unit can, for example, provide the analysis results to organizations such as police departments and schools. The providing unit can, for example, use the analysis results to help develop parks and crime prevention plans. The providing unit can, for example, take measures such as deploying security guards during specific time periods based on the analysis results. Thus, the safety evaluation system according to the embodiment can acquire and analyze information such as pedestrian flow, facial recognition, and time period, evaluate the safety level, and provide the results.

[0061] The data acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. For example, the data acquisition unit acquires information using sensors and cameras installed on utility poles. Sensors detect human movement, and cameras use facial recognition technology to identify individual people. Specifically, sensors installed on utility poles use infrared or ultrasonic waves to detect human movement and collect the data in real time. Cameras capture high-resolution video and use facial recognition algorithms to identify people in the video. For example, the data acquisition unit can monitor the movements of people walking in a park in real time and identify suspicious individuals using facial recognition technology. Facial recognition technology compares the faces with a pre-registered facial database to identify matching individuals. This allows the data acquisition unit to confirm whether a specific person is in the park. Furthermore, the data acquisition unit can also acquire information using temperature sensors and motion detection sensors. Temperature sensors detect changes in ambient temperature and sense when a person approaches. Motion detection sensors detect moving objects and track their movement. This allows the data acquisition unit to combine various sensors to collect more accurate information. The data acquisition unit centrally manages the data obtained from these sensors and cameras and transmits it to a central database in real time. This allows the data acquisition unit to efficiently collect information over a wide area and improve the overall performance of the system.

[0062] The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. For example, the analysis unit analyzes pedestrian traffic during specific time periods based on the acquired data. Specifically, the analysis unit uses AI to analyze the collected data in real time and understand patterns and trends in pedestrian traffic. The AI ​​uses machine learning algorithms to learn from past data and predict the current situation. For example, it can analyze information such as high pedestrian traffic during specific time periods or frequent entry and exit of specific individuals to evaluate the safety level. The analysis unit can also identify suspicious individuals using facial recognition technology. The facial recognition technology analyzes video data sent from the acquisition unit and compares it with a pre-registered list of suspicious individuals. This allows the analysis unit to quickly determine whether a suspicious person is in a particular area. Furthermore, the analysis unit can also evaluate the safety level by referring to past crime data. Based on past crime data, it can evaluate the crime risk in specific areas and time periods and take preventive measures. The analysis unit integrates this data to conduct a comprehensive safety assessment. This allows the analysis unit to not only grasp the situation in real time but also to conduct long-term risk assessments and trend analyses. The analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to improve the overall reliability and safety of the system.

[0063] The Service Provider provides the evaluation results obtained by the Analysis Provider. For example, the Service Provider provides analysis results to organizations such as police departments and schools. Specifically, the Service Provider notifies relevant organizations of the analysis results in real time to support a rapid response. For example, the analysis results can be used to develop parks or crime prevention plans. Based on the analysis results, the Service Provider can take measures such as deploying security guards during specific time periods. The Service Provider provides a dashboard to visually display the analysis results in an easy-to-understand manner, allowing stakeholders to easily grasp the information. The dashboard displays real-time data on graphs and maps, allowing for an intuitive understanding of changes in the situation. Furthermore, the Service Provider regularly creates reports based on the analysis results and provides them to relevant organizations. The reports include historical data, trend analysis, and risk assessment results, which can be used to inform future countermeasures. The Service Provider provides a platform to strengthen collaboration with relevant organizations and facilitate information sharing. This allows the Service Provider to provide analysis results quickly and effectively, and to support relevant organizations in taking appropriate measures. The Service Provider can also collect feedback from users and use it to improve the system. For example, based on feedback from the police and schools, the analysis algorithms and delivery methods are reviewed to improve the overall system performance. This allows the service provider to always provide highly accurate evaluation results based on the latest information, supporting quick and appropriate responses.

[0064] The acquisition unit acquires information using sensors and cameras installed on utility poles. For example, the acquisition unit can detect human movement using sensors installed on utility poles. For example, the acquisition unit can identify individual people using facial recognition technology with cameras installed on utility poles. For example, the acquisition unit can acquire temperature information using temperature sensors installed on utility poles. Thus, the acquisition unit can acquire information using sensors and cameras installed on utility poles. Sensors include, but are not limited to, temperature sensors and motion detection sensors. Cameras include, but are not limited to, fixed cameras and PTZ cameras. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input data acquired from sensors and cameras installed on utility poles into a generating AI and have the generating AI perform data analysis.

[0065] The analysis unit can analyze pedestrian traffic during specific time periods based on the acquired information. For example, the analysis unit analyzes pedestrian traffic during specific time periods based on the acquired data. The analysis unit can analyze this information, for example, when there is a high volume of pedestrian traffic during a specific time period, or when a particular person is frequently entering or leaving an area, and evaluate the safety level. The analysis unit can also analyze fluctuations in pedestrian traffic during specific time periods. This allows the analysis unit to analyze pedestrian traffic during specific time periods. Specific time periods include, but are not limited to, peak hours and off-peak hours. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input acquired data into a generating AI and have the generating AI perform an analysis of pedestrian traffic during specific time periods.

[0066] The analysis unit can identify suspicious individuals using facial recognition technology. For example, the analysis unit can identify suspicious individuals using facial recognition technology. For example, the analysis unit can identify a specific person using facial recognition technology and determine whether that person is suspicious. The analysis unit can also identify individuals with a criminal record using facial recognition technology. This allows the analysis unit to identify suspicious individuals using facial recognition technology. Suspicious individuals include, but are not limited to, behavioral patterns and physical characteristics. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data acquired using facial recognition technology into a generating AI and have the generating AI perform the identification of suspicious individuals.

[0067] The service provider can provide the analysis results to organizations such as police and schools. For example, the service provider can provide the analysis results to organizations such as police and schools. For example, the service provider can take measures based on the analysis results, such as deploying security guards during specific time periods. For example, the service provider can use the analysis results to help in park development and crime prevention plan formulation. In this way, the service provider can provide the analysis results to organizations such as police and schools. Organizations include, but are not limited to, police, schools, and local governments. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate the information to be provided.

[0068] The service provider can use the analysis results to help develop parks and crime prevention plans. For example, the service provider can use the analysis results to help develop parks and crime prevention plans. For example, the service provider can take measures such as deploying security guards during specific time periods based on the analysis results. For example, the service provider can revise park design standards and safety standards based on the analysis results. This allows the service provider to use the analysis results to help develop parks and crime prevention plans. Park development includes, for example, design standards and safety standards, but is not limited to such examples. Crime prevention plans include, for example, the locations of security cameras and patrol routes, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate information useful for developing parks and crime prevention plans.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated user emotions. For example, if the user is feeling anxious, the acquisition unit can increase the frequency of information acquisition from sensors and cameras. For example, if the user is relaxed, the acquisition unit can return the information acquisition frequency to normal. For example, if the user is in a hurry, the acquisition unit can prioritize acquiring only important information. In this way, the acquisition unit can adjust the timing of information acquisition based on the user's emotions. User emotions include, but are not limited to, facial expression analysis and voice analysis. Some or all of the above processing in the acquisition unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the acquisition unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The acquisition unit can analyze past data and determine the optimal sensor placement. For example, the acquisition unit can place sensors in the most crowded locations based on past pedestrian flow data. For example, the acquisition unit can install cameras in areas with a high crime rate based on past crime data. For example, the acquisition unit can place sensors in locations where many people gather during specific time periods based on past time-of-day data. This allows the acquisition unit to analyze past data and determine the optimal sensor placement. The optimal sensor placement includes, but is not limited to, coverage area and distance between sensors. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input past data into a generating AI and have the generating AI determine the optimal sensor placement.

[0071] The data acquisition unit can change its acquisition method according to specific events or seasons when acquiring information. For example, during an event, the acquisition unit can increase the frequency of acquiring information around the event venue. For example, the acquisition unit can change the sensor settings to take into account different pedestrian flow patterns depending on the season. For example, during a specific festival or event, the acquisition unit can increase the camera resolution to acquire more detailed information. This allows the acquisition unit to change its acquisition method according to specific events or seasons. Specific events include, but are not limited to, festivals and sporting events. Seasons include, but are not limited to, spring, summer, autumn, and winter. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input data corresponding to a specific event or season into a generating AI and have the generating AI execute the change in the acquisition method.

[0072] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated user emotions. For example, if the user is feeling anxious, the data acquisition unit may prioritize acquiring facial recognition information. For example, if the user is relaxed, the data acquisition unit may prioritize acquiring pedestrian flow information. For example, if the user is in a hurry, the data acquisition unit may prioritize acquiring time-of-day information. In this way, the data acquisition unit can determine the priority of information to acquire based on the user's emotions. The priority of information includes, but is not limited to, importance and urgency. Some or all of the above processing in the data acquisition unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the data acquisition unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of information to acquire.

[0073] The acquisition unit can adjust its acquisition method when acquiring information, taking weather information into consideration. For example, in rainy weather, the acquisition unit can increase the sensitivity of the outdoor sensor to acquire information. For example, in sunny weather, the acquisition unit can acquire information with normal sensitivity. For example, on snowy days, the acquisition unit can increase the resolution of the camera to ensure visibility. In this way, the acquisition unit can adjust its acquisition method considering weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can input weather information into a generating AI and have the generating AI perform the adjustment of the acquisition method.

[0074] The acquisition unit can analyze the surrounding traffic conditions and acquire relevant information when acquiring information. For example, during traffic congestion, the acquisition unit can prioritize acquiring pedestrian flow information in the congested area. For example, when a traffic accident occurs, the acquisition unit can prioritize acquiring information around the accident site. For example, during times of heavy traffic, the acquisition unit can acquire information corresponding to the traffic volume. In this way, the acquisition unit can analyze the surrounding traffic conditions and acquire relevant information. Traffic conditions include, but are not limited to, traffic volume and congestion information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can input traffic condition data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can increase the accuracy of the analysis and provide more detailed information. For example, if the user is relaxed, the analysis unit can provide information with normal accuracy. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise information. In this way, the analysis unit can adjust the accuracy of the analysis based on the user's emotions. Accuracy of the analysis includes, but is not limited to, adjusting the parameters of the analysis algorithm. Some or all of the above-described processes in the analysis unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of the analysis.

[0076] The analysis unit can evaluate the safety level by referring to past crime data during analysis. For example, the analysis unit can evaluate the safety level of a specific area based on past crime data. For example, the analysis unit can evaluate the safety level by considering the time periods in which past crimes occurred. For example, the analysis unit can analyze the types of past crimes and evaluate the safety level. This allows the analysis unit to evaluate the safety level by referring to past crime data. Past crime data includes, but is not limited to, the number of crimes and the types of crimes. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input past crime data into a generating AI and have the generating AI perform the safety level evaluation.

[0077] The analysis unit can apply different analysis algorithms depending on the time of day or day of the week during analysis. For example, the analysis unit can apply different analysis algorithms during the daytime and nighttime on weekdays. For example, the analysis unit can apply analysis algorithms corresponding to specific events on weekends and holidays. For example, the analysis unit can apply different analysis algorithms depending on the season. In this way, the analysis unit can apply different analysis algorithms depending on the time of day or day of the week. Different analysis algorithms include, but are not limited to, time series analysis and clustering. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data corresponding to a specific time of day or day of the week into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0078] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can display detailed analysis results. For example, if the user is relaxed, the analysis unit can display concise analysis results. For example, if the user is in a hurry, the analysis unit can display concise analysis results. In this way, the analysis unit can adjust how the analysis results are displayed based on the user's emotions. The methods for displaying the analysis results include, but are not limited to, graph displays and text displays. Some or all of the above-described processes in the analysis unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0079] The analysis unit can evaluate the safety level by considering information about surrounding commercial facilities and residential areas during the analysis. For example, the analysis unit can evaluate the safety level by considering the operating hours of surrounding commercial facilities. For example, the analysis unit can evaluate the safety level by considering the population density of surrounding residential areas. For example, the analysis unit can evaluate the safety level by considering the crime rate of surrounding commercial facilities and residential areas. This allows the analysis unit to evaluate the safety level by considering information about surrounding commercial facilities and residential areas. Commercial facilities include, but are not limited to, shopping malls and supermarkets. Residential areas include, but are not limited to, detached houses and apartment buildings. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input information about surrounding commercial facilities and residential areas into a generating AI and have the generating AI perform the safety level evaluation.

[0080] The analysis unit can improve the accuracy of its analysis by referring to local event information during the analysis process. For example, when a local event is being held, the analysis unit prioritizes analyzing information around the event venue. For example, the analysis unit can adjust its analysis algorithm according to the type of local event. For example, the analysis unit can improve the accuracy of its analysis by considering the number of participants in a local event. This allows the analysis unit to improve the accuracy of its analysis by referring to local event information. Local event information includes, but is not limited to, event calendars and local news. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input local event information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0081] The information provider can estimate the user's emotions and adjust the format of the information provided based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide information in a detailed report format. For example, if the user is relaxed, the information provider can provide information in a concise summary format. For example, if the user is in a hurry, the information provider can provide information in a concise bulleted list format. In this way, the information provider can adjust the format of the information provided based on the user's emotions. The format of the information includes, but is not limited to, text format, image format, video format, etc. Some or all of the processing described above in the information provider may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the format of the information provided.

[0082] The delivery unit can select the optimal delivery method by referring to past delivery history at the time of delivery. For example, if the delivery unit has previously provided information in a detailed report format, it can provide the information in a similar format. For example, if the delivery unit has previously provided information in a concise summary format, it can provide the information in a similar format. For example, the delivery unit can select the most effective delivery method from past delivery history. This allows the delivery unit to select the optimal delivery method by referring to past delivery history. Delivery history includes, but is not limited to, past delivery dates and times, and delivery content. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input past delivery history into a generating AI and have the generating AI select the optimal delivery method.

[0083] The information provider can provide customized information to specific organizations or individuals at the time of provision. For example, the information provider can provide police with detailed information on crime rates and suspicious persons. For example, the information provider can provide schools with information on student safety. For example, the information provider can provide local residents with information on the safety level of their area. This allows the information provider to provide customized information to specific organizations or individuals. Customized information includes, but is not limited to, providing information tailored to individual needs. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input the needs of a specific organization or individual into a generating AI and have the generating AI perform the generation of customized information.

[0084] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the information provider may prioritize providing safety-related information. For example, if the user is relaxed, the information provider may prioritize providing general information. For example, if the user is in a hurry, the information provider may prioritize providing important information. In this way, the information provider can determine the priority of the information to be provided based on the user's emotions. Prioritization of information includes, but is not limited to, importance and urgency. Some or all of the processing described above in the information provider may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of the information to be provided.

[0085] The information provider can adjust its delivery method when providing information, taking into account the attribute information of the information recipient. For example, the provider can provide information to the police in the form of a detailed report. For example, the provider can provide information to schools in the form of a concise summary. For example, the provider can provide information to local residents in the form of a concise bulleted list. This allows the provider to adjust its delivery method, taking into account the attribute information of the information recipient. Attribute information includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the provider can input the attribute information of the information recipient into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0086] The information provider can optimize the content of its deliverables by referring to past feedback from the recipient of the information. For example, the provider can provide information in a similar format based on feedback received when information was previously provided in a detailed report format. For example, the provider can provide information in a similar format based on feedback received when information was previously provided in a concise summary format. For example, the provider can select the most effective content from past feedback. This allows the provider to optimize the content of its deliverables by referring to past feedback from the recipient of the information. Past feedback includes, but is not limited to, survey results and user reviews. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the provider can input past feedback into a generating AI and have the generating AI perform the optimization of the content of its deliverables.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated user emotions. For example, if the user is feeling anxious, the frequency of information acquisition from sensors and cameras can be increased. If the user is relaxed, the frequency of information acquisition can be returned to normal. Furthermore, if the user is in a hurry, only important information can be prioritized for acquisition. In this way, the acquisition unit can adjust the timing of information acquisition based on the user's emotions. User emotions include, but are not limited to, facial expression analysis and voice analysis. Some or all of the above processing in the acquisition unit may be performed using an emotion engine or generative AI, or it may be performed without using an emotion engine or generative AI. For example, the acquisition unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data acquisition unit can analyze past data and determine the optimal sensor placement. For example, based on past pedestrian flow data, sensors can be placed in the areas where the most people gather. Also, based on past crime data, cameras can be installed in areas where crime frequently occurs. Furthermore, based on past time-of-day data, sensors can be placed in areas where many people gather during specific time periods. In this way, the data acquisition unit can analyze past data and determine the optimal sensor placement. The optimal sensor placement includes, but is not limited to, coverage area and distance between sensors. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input past data into a generating AI and have the generating AI determine the optimal sensor placement.

[0090] The data acquisition unit can change its acquisition method according to specific events or seasons when acquiring information. For example, during an event, it can increase the frequency of acquiring information around the event venue. It can also change the sensor settings to take into account different pedestrian flow patterns depending on the season. Furthermore, during specific festivals or events, it can increase the camera resolution to acquire more detailed information. In this way, the data acquisition unit can change its acquisition method according to specific events or seasons. Specific events include, but are not limited to, festivals and sporting events. Seasons include, but are not limited to, spring, summer, autumn, and winter. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data corresponding to specific events or seasons into a generating AI and have the generating AI execute the change in the acquisition method.

[0091] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated user emotions. For example, if the user is feeling anxious, facial recognition information can be prioritized. If the user is relaxed, pedestrian flow information can be prioritized. Furthermore, if the user is in a hurry, time-of-day information can be prioritized. In this way, the data acquisition unit can determine the priority of information to acquire based on the user's emotions. The priority of information includes, but is not limited to, importance and urgency. Some or all of the above processing in the data acquisition unit may be performed using an emotion engine or generative AI, or it may be performed without using an emotion engine or generative AI. For example, the data acquisition unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of information to acquire.

[0092] The acquisition unit can adjust its acquisition method when acquiring information, taking weather information into consideration. For example, in rainy weather, it can increase the sensitivity of the outdoor sensor to acquire information. In sunny weather, it can acquire information with normal sensitivity. Furthermore, on snowy days, it can increase the camera resolution to ensure visibility. In this way, the acquisition unit can adjust its acquisition method considering weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input weather information into a generating AI and have the generating AI perform the adjustment of the acquisition method.

[0093] The acquisition unit can analyze the surrounding traffic conditions and obtain relevant information when acquiring data. For example, during traffic congestion, it can prioritize the acquisition of pedestrian flow information in the congested area. Also, when a traffic accident occurs, it can prioritize the acquisition of information around the accident site. Furthermore, during times of heavy traffic, it can acquire information corresponding to the traffic volume. In this way, the acquisition unit can analyze the surrounding traffic conditions and acquire relevant information. Traffic conditions include, but are not limited to, traffic volume and congestion information. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input traffic condition data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0094] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis accuracy can be increased to provide more detailed information. If the user is relaxed, information can be provided with normal analysis accuracy. Furthermore, if the user is in a hurry, the analysis can be performed quickly to provide concise information. In this way, the analysis unit can adjust the accuracy of the analysis based on the user's emotions. The accuracy of the analysis includes, but is not limited to, adjusting the parameters of the analysis algorithm. Some or all of the above-described processes in the analysis unit may be performed using an emotion engine or generative AI, or they may be performed without using an emotion engine or generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0095] The analysis unit can evaluate safety levels by referring to past crime data during analysis. For example, it can evaluate the safety level of a specific area based on past crime data. It can also evaluate safety levels by considering the time periods in which past crimes occurred. Furthermore, it can analyze the types of past crimes and evaluate safety levels. In this way, the analysis unit can evaluate safety levels by referring to past crime data. Past crime data includes, but is not limited to, the number of crimes and the types of crimes. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past crime data into a generating AI and have the generating AI perform the safety level evaluation.

[0096] The analysis unit can apply different analysis algorithms depending on the time of day or day of the week during analysis. For example, different analysis algorithms can be applied during the daytime and at night on weekdays. Furthermore, analysis algorithms corresponding to specific events can be applied on weekends and holidays. In addition, different analysis algorithms can be applied depending on the season. This allows the analysis unit to apply different analysis algorithms depending on the time of day or day of the week. These different analysis algorithms include, but are not limited to, time series analysis and clustering. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data corresponding to specific time of day or day of the week into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0097] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is feeling anxious, detailed analysis results can be displayed. If the user is relaxed, concise analysis results can be displayed. Furthermore, if the user is in a hurry, a summary analysis result can be displayed. In this way, the analysis unit can adjust how the analysis results are displayed based on the user's emotions. The methods for displaying analysis results include, but are not limited to, graph displays and text displays. Some or all of the above-described processes in the analysis unit may be performed using an emotion engine or a generative AI, or they may be performed without using an emotion engine or a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The acquisition unit acquires information such as pedestrian flow, facial recognition, and time of day. The acquisition unit acquires information using, for example, sensors or cameras installed on utility poles. Sensors detect human movement, and cameras use facial recognition technology to identify individual people. For example, the acquisition unit can monitor the movements of people walking in a park in real time and identify suspicious individuals using facial recognition technology. The acquisition unit can also acquire information using, for example, temperature sensors or motion detection sensors. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level. For example, the analysis unit analyzes pedestrian traffic during specific time periods based on the acquired data. The analysis unit can analyze this information, for example, if there is a high volume of pedestrian traffic during a particular time period, or if a particular person is frequently coming and going, and evaluate the safety level. The analysis unit can also identify suspicious individuals using facial recognition technology, for example. The analysis unit can also evaluate the safety level by referring to past crime data, for example. Step 3: The provisioning department provides the evaluation results obtained by the analysis department. The provisioning department provides the analysis results to organizations such as police departments and schools. The provisioning department can use the analysis results to help develop parks and crime prevention plans, for example. The provisioning department can take measures such as deploying security guards during specific time periods based on the analysis results.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires information using the sensors and cameras of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the safety level based on the acquired data. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the analysis results to organizations such as police or schools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires information using the sensors and cameras of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and evaluates the safety level based on the acquired data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the analysis results to organizations such as police or schools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires information using the sensors and camera of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the safety level based on the acquired data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the analysis results to organizations such as police or schools. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires information using the sensors and cameras of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the safety level based on the acquired data. The provision unit is implemented in, for example, the control unit 46A of the robot 414 and provides the analysis results to organizations such as police or schools. The correspondence between each unit and the devices or control units is not limited to the example described above and can be modified in various ways.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) An acquisition unit that acquires information such as pedestrian flow, facial recognition, and time of day, An analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level, The system includes a providing unit that provides evaluation results obtained by the analysis unit. A system characterized by the following features. (Note 2) The acquisition unit is, Information is acquired using sensors and cameras installed on utility poles. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the acquired information, we analyze pedestrian traffic during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Using facial recognition technology to identify suspicious individuals The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The analysis results will be provided to organizations such as the police and schools. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The analysis results will be used to inform the development of parks and crime prevention plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze past data to determine the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving information, change the retrieval method depending on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring information, the acquisition method is adjusted to take weather information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring information, the system analyzes the surrounding traffic conditions and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, past crime data is referenced to assess the level of safety. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the specific time of day or day of the week. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the safety level is evaluated by taking into account information about surrounding commercial facilities and residential areas. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to local event information to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the format of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected by referring to past delivery history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, customized information is provided to specific organizations or individuals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the method of delivery will be adjusted considering the attributes of the information recipient. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we optimize the content by referring to past feedback from recipients. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires information such as pedestrian flow, facial recognition, and time of day, An analysis unit analyzes the information acquired by the acquisition unit and evaluates the safety level, The system includes a providing unit that provides evaluation results obtained by the analysis unit. A system characterized by the following features.

2. The acquisition unit is, Information is acquired using sensors and cameras installed on utility poles. The system according to feature 1.

3. The aforementioned analysis unit, Based on the acquired information, we analyze pedestrian traffic during specific time periods. The system according to feature 1.

4. The aforementioned analysis unit, Using facial recognition technology to identify suspicious individuals The system according to feature 1.

5. The aforementioned supply unit is, The analysis results will be provided to organizations such as police and schools. The system according to feature 1.

6. The aforementioned supply unit is, The analysis results will be used to inform the development of parks and crime prevention plans. The system according to feature 1.

7. The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, Analyze past data to determine the optimal sensor placement. The system according to feature 1.

9. The acquisition unit is, When retrieving information, change the retrieval method depending on specific events or seasons. The system according to feature 1.

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