system

Abstract

The system according to this embodiment aims to analyze people's behavioral data and provide an optimal pedestrian environment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects people's behavior data. The analysis unit analyzes the data collected by the collection unit and analyzes people's motivation for passing in real time. The generation unit generates advice and instructions to provide an optimal passing environment based on the analysis results obtained by the analysis unit. The provision unit provides the advice and instructions generated by the generation 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 character of the chatbot, 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 conventional technology, real-time analysis of passing motivation based on people's behavior data and provision of an optimal passing environment have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze people's behavior data and provide an optimal passing environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on people's behavior. The analysis unit analyzes the data collected by the collection unit and analyzes people's motivation for passing through in real time. The generation unit generates advice and instructions to provide an optimal passage environment based on the analysis results obtained by the analysis unit. The provision unit provides the advice and instructions generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze people's behavior data and provide an optimal pedestrian environment. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pedestrian flow control system according to an embodiment of the present invention is a system that utilizes the generation capabilities of AI based on pedestrian flow data analysis to create a modern version of a "checkpoint." This system can effectively manage people's movements and achieve appropriate pedestrian flow control. For example, the AI ​​analyzes people's motivations for passing through in real time and generates advice and instructions to provide an optimal passage environment. Next, the advice changes according to the patterns and fluctuation speeds of pedestrian flow and the degree of congestion at a specific point in time, enabling the most comfortable and efficient passage for people. This creates a safe and smooth flow of people, minimizing congestion and stress levels in areas with large pedestrian traffic. Thus, the pedestrian flow control system can collect and analyze people's behavior data and generate and provide advice and instructions to provide an optimal passage environment.

[0029] The pedestrian flow control system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects people's behavior data. For example, the collection unit monitors people's movements at event venues or tourist destinations and collects data. The collection unit can monitor people's movements in real time and collect data using, for example, cameras or sensors. The collection unit can also collect data from smartphones or wearable devices. The analysis unit analyzes the data collected by the collection unit and analyzes people's motivation to pass through in real time. For example, the analysis unit can analyze the collected data using AI to grasp the degree of congestion and the rate of change. For example, the analysis unit can analyze people's behavior patterns using data mining technology and estimate their motivation to pass through. The generation unit generates advice and instructions to provide an optimal passage environment based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to advise on the optimal passage route and time based on the analysis results. For example, the generation unit can generate advice and instructions using natural language generation technology. The providing unit provides advice and instructions generated by the generating unit. The providing unit can provide advice and instructions to users using, for example, smartphones or digital signage. The providing unit can also provide advice and instructions to users using, for example, a voice assistant. As a result, the pedestrian flow control system according to the embodiment can provide an optimal traffic environment and realize safe and smooth pedestrian flow control by collecting, analyzing, generating, and providing people's behavior data.

[0030] The data collection unit collects data on people's behavior. For example, it monitors and collects data on people's movements at event venues and tourist destinations. Specifically, the data collection unit uses high-resolution cameras and infrared sensors to monitor people's movements in real time. This allows for accurate understanding of the density and speed of people in a specific area. The data collection unit can also collect data from smartphones and wearable devices. It uses the GPS function of smartphones to obtain the user's location information and collects biometric data such as heart rate and steps from wearable devices. This data is transmitted to a cloud server and updated in real time. Furthermore, the data collection unit can use Wi-Fi and Bluetooth® beacons to communicate with the user's device and obtain location information and movement paths. This allows the data collection unit to collect a wide range of data from various devices and understand the situation in real time. The collected data is stored in a database and managed so that the analysis and generation units can access it. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. For example, by increasing the frequency of data collection during times when there is heavy human movement, such as at the start or end of an event, more detailed information can be obtained. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the collection unit to analyze people's motivations for passing through areas in real time. For example, the analysis unit can use AI to analyze collected data and understand congestion levels and fluctuation rates. Specifically, the AI ​​uses image recognition technology to analyze camera footage and identify the density and direction of movement of people in a particular area. It can also use data mining technology to analyze people's behavior patterns from past data and estimate their motivations for passing through areas. For example, it can predict congestion trends during specific time periods or the flow of people when a particular event is held. Furthermore, the analysis unit can analyze collected location information and biometric data to understand users' movement speed and dwell time. This allows the analysis unit to understand people's behavior in real time and predict the occurrence of congestion. The analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. For example, if an abnormally high density of people exceeding normal congestion levels is detected, the analysis unit can immediately issue a warning and take appropriate measures. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit generates advice and instructions to provide an optimal traffic environment based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to advise on the optimal traffic routes and times based on the analysis results. Specifically, the AI ​​uses the analysis results to recommend the best routes to avoid congestion and traffic during specific times. For example, if a particular area is congested, the generation unit will suggest an alternative route to avoid that area. It can also use natural language generation technology to generate easy-to-understand advice and instructions for users. For example, it can generate specific instructions such as, "The north entrance is currently very congested. We recommend entering from the south entrance." Furthermore, the generation unit can also provide advice to event organizers and managers based on the analysis results. For example, if a particular area is congested, it can suggest deploying additional guidance staff. In this way, the generation unit can provide appropriate advice not only to users but also to organizers and managers, optimizing the overall traffic environment. The generation unit can continuously generate advice and instructions based on analysis results that are updated in real time, and respond to the latest situation. In this way, the generation unit can always provide an optimal traffic environment and achieve safe and smooth pedestrian flow control.

[0033] The service provider provides advice and instructions generated by the generation unit. For example, the service provider can provide advice and instructions to users using smartphones or digital signage. Specifically, it can notify users in real time with advice and instructions via a smartphone app. For instance, it can use the app's push notification function to inform users of congestion levels and optimal routes. It can also use digital signage to display congestion levels and routes in specific areas. This allows users to obtain the latest information in real time. Furthermore, the service provider can also provide advice and instructions using a voice assistant. For example, it can provide users with voice guidance on congestion levels and optimal routes through a voice assistant. This provides not only visual but also auditory information, improving user convenience. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of its advice and instructions. For example, it can collect feedback on the results of users following the provided advice and provide this feedback to the analysis and generation units, thereby improving the overall system accuracy. This enables the service provider to provide advice and instructions to users quickly and reliably, achieving safe and smooth pedestrian flow control.

[0034] The data collection unit can monitor people's movements at event venues, tourist destinations, etc., and collect data. For example, the data collection unit can monitor people's movements at event venues, tourist destinations, etc., using cameras and sensors and collect data. For example, the data collection unit can monitor people's movements in real time using cameras and collect data. The data collection unit can also monitor people's movements using sensors and collect data. For example, the data collection unit can collect data from smartphones and wearable devices. This allows for more accurate pedestrian flow data to be obtained by monitoring people's movements at event venues, tourist destinations, etc., and collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data acquired by a camera into a generating AI and analyze people's movements from the image data.

[0035] The analysis unit can analyze the collected data to understand congestion levels and fluctuation rates. For example, the analysis unit can analyze the collected data using AI to understand congestion levels and fluctuation rates. For example, the analysis unit can analyze the collected data using data mining techniques to understand congestion levels and fluctuation rates. The analysis unit can also analyze the collected data using machine learning algorithms to understand congestion levels and fluctuation rates. For example, the analysis unit can analyze the collected data using clustering techniques to understand congestion levels and fluctuation rates. By analyzing the collected data and understanding congestion levels and fluctuation rates, more appropriate traffic advice can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of congestion levels and fluctuation rates.

[0036] The generation unit can advise on the optimal travel route and time based on the analysis results. For example, the generation unit can advise on the optimal travel route and time based on the analysis results using AI. For example, the generation unit can advise on the optimal travel route and time based on the analysis results using natural language generation technology. The generation unit can also advise on the optimal travel route and time based on the analysis results using machine learning algorithms. For example, the generation unit can advise on the optimal travel route and time based on the analysis results using clustering technology. By advising on the optimal travel route and time based on the analysis results, congestion can be avoided and efficient travel can be achieved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI provide advice on the optimal travel route and time.

[0037] The service provider can provide advice and instructions to users and support optimal traffic flow. For example, the service provider can provide advice and instructions to users using smartphones or digital signage. The service provider can provide advice and instructions to users using smartphones, for example. The service provider can also provide advice and instructions to users using digital signage. The service provider can also provide advice and instructions to users using voice assistants, for example. This enables safe and smooth pedestrian flow by providing advice and instructions to users and supporting optimal traffic flow. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated advice and instructions into a generating AI and have the generating AI execute the optimal method for providing them to the user.

[0038] The generation unit can issue instructions to avoid a particular area if that area is congested. For example, the generation unit can use AI to issue instructions to avoid a particular area if that area is congested. For example, the generation unit can use natural language generation technology to issue instructions to avoid a particular area if that area is congested. The generation unit can also use machine learning algorithms to issue instructions to avoid a particular area if that area is congested. For example, the generation unit can use clustering technology to issue instructions to avoid a particular area if that area is congested. In this way, by issuing instructions to avoid a particular area if that area is congested, congestion can be alleviated and pedestrian flow can be made smoother. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about congested areas into a generation AI and have the generation AI execute instructions to avoid those areas.

[0039] The generation unit can provide advice to avoid certain time periods when congestion is expected. For example, the generation unit can use AI to provide advice to avoid certain time periods when congestion is expected. For example, the generation unit can use natural language generation technology to provide advice to avoid certain time periods when congestion is expected. The generation unit can also use machine learning algorithms to provide advice to avoid certain time periods when congestion is expected. For example, the generation unit can use clustering technology to provide advice to avoid certain time periods when congestion is expected. By providing advice to avoid certain time periods when congestion is expected, congestion can be avoided and efficient traffic flow can be achieved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about time periods when congestion is expected to occur into a generation AI and have the generation AI provide advice to avoid those time periods.

[0040] The data collection unit can measure the length of time people spend in specific areas of event venues or tourist destinations, thereby improving the accuracy of data collection. For example, the data collection unit can measure the length of time people spend in specific areas to understand the degree of congestion. For example, the data collection unit can identify areas where people spend a long time and focus data collection on those areas. The data collection unit can also identify areas where people spend a short time and adjust the frequency of data collection. This improves the accuracy of data collection by measuring the length of time people spend in specific areas. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the length of time people spend in specific areas into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0041] The data collection unit can select the optimal data collection method by referring to the user's past movement history when collecting data. For example, the data collection unit can refer to the user's past movement history and select the optimal data collection point. For example, the data collection unit can select a data collection method to avoid congestion based on past movement history. The data collection unit can also analyze the user's movement patterns and select an efficient data collection method. In this way, by referring to the user's past movement history, the optimal data collection method can be selected and efficient data collection can be achieved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past movement history data into a generating AI and have the generating AI select the optimal data collection method.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of highly relevant data by considering the user's travel route. Furthermore, the data collection unit can also prioritize the collection of highly relevant data based on the user's destination. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media posts and collect relevant data. For example, the data collection unit can analyze the user's social media friendships and collect relevant data. The data collection unit can also analyze the user's social media location information and collect relevant data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the collected data during the analysis. For example, the analysis unit can analyze the interrelationships of the collected data to improve the accuracy of the analysis. For example, the analysis unit can adjust the analysis algorithm by considering the interrelationships of the data. The analysis unit can also correct the analysis results based on the interrelationships of the data. In this way, the accuracy of the analysis can be improved by considering the interrelationships of the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the interrelationships of the collected data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The analysis unit can apply different analysis algorithms depending on specific events or seasons during analysis. For example, the analysis unit can adjust the analysis algorithm according to a specific event. For example, the analysis unit can change the analysis algorithm according to the season. Furthermore, the analysis unit can consider the characteristics of events and seasons and apply the optimal analysis algorithm. This allows for improved accuracy of the analysis by applying different analysis algorithms according to specific events and seasons. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data corresponding to specific events or seasons into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0046] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze the geographical distribution of the data to improve the accuracy of the analysis. For example, the analysis unit can adjust the analysis algorithm while considering the geographical distribution. The analysis unit can also correct the analysis results based on the geographical distribution. In this way, the accuracy of the analysis can be improved by considering the geographical distribution of the data. 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 the geographical distribution of the data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0047] The analysis unit can improve the accuracy of the analysis by referring to relevant historical data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to historical data. For example, the analysis unit can adjust the analysis algorithm based on relevant historical data. The analysis unit can also correct the analysis results based on historical data. In this way, the accuracy of the analysis can be improved by referring to historical data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input historical data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0048] The generation unit can adjust the level of detail in advice and instructions based on the importance of the analysis results during generation. For example, if the analysis results are important, the generation unit can provide detailed advice and instructions. For example, if the analysis results are general, the generation unit can provide concise advice and instructions. Furthermore, if the analysis results are urgent, the generation unit can provide quick and concise advice and instructions. This allows for the provision of appropriate information by adjusting the level of detail in advice and instructions based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI adjust the level of detail in the advice and instructions.

[0049] The generation unit can apply different generation algorithms depending on the specific area or time of day during generation. For example, the generation unit can adjust the generation algorithm depending on the specific area. For example, the generation unit can change the generation algorithm depending on the specific time of day. Furthermore, the generation unit can consider the characteristics of the area and time of day and apply the optimal generation algorithm. This allows for the provision of optimal advice and instructions by applying different generation algorithms depending on the specific area and time of day. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data corresponding to a specific area and time of day into a generation AI and have the generation AI execute the application of the optimal generation algorithm.

[0050] The generation unit can generate optimal advice and instructions while considering the user's geographical location information. For example, the generation unit can provide optimal advice and instructions based on the user's current location. For example, the generation unit can provide optimal advice and instructions while considering the user's travel route. Furthermore, the generation unit can also provide optimal advice and instructions based on the user's destination. In this way, by considering the user's geographical location information, optimal advice and instructions can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of optimal advice and instructions.

[0051] The generation unit can analyze the user's social media activity during generation and generate relevant advice and instructions. For example, the generation unit can analyze the user's social media posts and provide relevant advice and instructions. For example, the generation unit can analyze the user's social media friendships and provide relevant advice and instructions. The generation unit can also analyze the user's social media location information and provide relevant advice and instructions. In this way, relevant advice and instructions can be provided by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant advice and instructions.

[0052] The service provider can select the optimal service delivery method by referring to the user's past behavior history at the time of delivery. For example, the service provider can refer to the user's past behavior history and select the optimal service delivery method. For example, the service provider can select a service delivery method preferred by the user based on their past behavior history. The service provider can also analyze the user's behavior patterns and select an efficient service delivery method. This makes it possible to select the optimal service delivery method by referring to the user's past behavior history and to provide information efficiently. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past behavior history data into a generating AI and have the generating AI select the optimal service delivery method.

[0053] The delivery unit can apply different delivery methods depending on the specific device or platform at the time of delivery. For example, if a smartphone is being used, the delivery unit can provide a delivery method adapted to the screen size. If a tablet is being used, the delivery unit can provide a delivery method optimized for a larger screen. Furthermore, if a smartwatch is being used, the delivery unit can provide a concise and highly visible delivery method. This enables optimal information delivery by applying different delivery methods depending on the specific device or platform. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data specific to the device or platform into a generating AI and have the generating AI execute the application of the optimal delivery method.

[0054] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's current location. For example, the service provider can select the optimal service delivery method by considering the user's travel route. Furthermore, the service provider can also select the optimal service delivery method based on the user's destination. This makes it possible to select the optimal service delivery method by considering the user's geographical location information, enabling efficient information delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0055] The service provider can analyze the user's social media activity and provide relevant advice and instructions at the time of delivery. For example, the service provider can analyze the user's social media posts and provide relevant advice and instructions. For example, the service provider can analyze the user's social media friendships and provide relevant advice and instructions. The service provider can also analyze the user's social media location information and provide relevant advice and instructions. In this way, by analyzing the user's social media activity, relevant advice and instructions can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant advice and instructions.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method during data collection. For example, it can analyze the user's past behavior patterns and select a data collection method that avoids congestion. It can also select an efficient data collection method based on the user's behavior patterns. Furthermore, it can refer to the user's past behavior patterns to select the optimal data collection points. In this way, by analyzing the user's past behavior patterns, the optimal data collection method can be selected, and efficient data collection can be achieved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior pattern data into a generating AI and have the generating AI select the optimal collection method.

[0058] The analysis unit can perform analysis while considering the temporal variation of the data. For example, it can analyze the temporal variation of the data to improve the accuracy of the analysis. It can adjust the analysis algorithm while considering the temporal variation of the data. It can also correct the analysis results based on the temporal variation of the data. In this way, the accuracy of the analysis can be improved by considering the temporal variation of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the temporal variation of the data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0059] The generation unit can generate optimal advice and instructions by referring to the user's past behavior history during the generation process. For example, it can provide optimal advice and instructions by referring to the user's past behavior history. It can also provide advice and instructions that the user prefers based on past behavior history. Furthermore, it can analyze the user's behavior patterns and provide efficient advice and instructions. This makes it possible to provide optimal advice and instructions and deliver information efficiently by referring to the user's past behavior history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the user's past behavior history data into a generation AI and have the generation AI execute the generation of optimal advice and instructions.

[0060] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, it can select the optimal service delivery method based on the user's current location. It can also select the optimal service delivery method by considering the user's travel route. Furthermore, it can select the optimal service delivery method based on the user's destination. This allows for efficient information delivery by selecting the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0061] The service provider can analyze the user's social media activity and provide relevant advice and instructions at the time of service delivery. For example, it can analyze the user's social media posts and provide relevant advice and instructions. It can also analyze the user's social media friendships and provide relevant advice and instructions. Furthermore, it can analyze the user's social media location information and provide relevant advice and instructions. In this way, by analyzing the user's social media activity, it is possible to provide relevant advice and instructions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant advice and instructions.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data on people's behavior. The data collection unit monitors people's movements in places like event venues and tourist destinations and collects data. The data collection unit can monitor people's movements in real time using cameras and sensors and collect data. The data collection unit can also collect data from smartphones and wearable devices. Step 2: The analysis unit analyzes the data collected by the collection unit to analyze people's motivation for passing through in real time. The analysis unit can use AI to analyze the collected data and understand the degree of congestion and the rate of change. The analysis unit can use data mining technology to analyze people's behavior patterns and estimate their motivation for passing through. Step 3: The generation unit generates advice and instructions to provide the optimal traffic environment based on the analysis results obtained by the analysis unit. The generation unit can use AI to advise on the optimal traffic route and time based on the analysis results. The generation unit can use natural language generation technology to generate advice and instructions. Step 4: The providing unit provides the advice and instructions generated by the generating unit. The providing unit can provide advice and instructions to the user using a smartphone or digital signage. The providing unit can also provide advice and instructions to the user using a voice assistant.

[0064] (Example of form 2) The pedestrian flow control system according to an embodiment of the present invention is a system that utilizes the generation capabilities of AI based on pedestrian flow data analysis to create a modern version of a "checkpoint." This system can effectively manage people's movements and achieve appropriate pedestrian flow control. For example, the AI ​​analyzes people's motivations for passing through in real time and generates advice and instructions to provide an optimal passage environment. Next, the advice changes according to the patterns and fluctuation speeds of pedestrian flow and the degree of congestion at a specific point in time, enabling the most comfortable and efficient passage for people. This creates a safe and smooth flow of people, minimizing congestion and stress levels in areas with large pedestrian traffic. Thus, the pedestrian flow control system can collect and analyze people's behavior data and generate and provide advice and instructions to provide an optimal passage environment.

[0065] The pedestrian flow control system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects people's behavior data. For example, the collection unit monitors people's movements at event venues or tourist destinations and collects data. The collection unit can monitor people's movements in real time and collect data using, for example, cameras or sensors. The collection unit can also collect data from smartphones or wearable devices. The analysis unit analyzes the data collected by the collection unit and analyzes people's motivation to pass through in real time. For example, the analysis unit can analyze the collected data using AI to grasp the degree of congestion and the rate of change. For example, the analysis unit can analyze people's behavior patterns using data mining technology and estimate their motivation to pass through. The generation unit generates advice and instructions to provide an optimal passage environment based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to advise on the optimal passage route and time based on the analysis results. For example, the generation unit can generate advice and instructions using natural language generation technology. The providing unit provides advice and instructions generated by the generating unit. The providing unit can provide advice and instructions to users using, for example, smartphones or digital signage. The providing unit can also provide advice and instructions to users using, for example, a voice assistant. As a result, the pedestrian flow control system according to the embodiment can provide an optimal traffic environment and realize safe and smooth pedestrian flow control by collecting, analyzing, generating, and providing people's behavior data.

[0066] The data collection unit collects data on people's behavior. For example, it monitors and collects data on people's movements at event venues and tourist destinations. Specifically, the data collection unit uses high-resolution cameras and infrared sensors to monitor people's movements in real time. This allows for accurate understanding of the density and speed of people in a specific area. The data collection unit can also collect data from smartphones and wearable devices. It uses the GPS function of smartphones to obtain the user's location information and collects biometric data such as heart rate and steps from wearable devices. This data is transmitted to a cloud server and updated in real time. Furthermore, the data collection unit can use Wi-Fi and Bluetooth beacons to communicate with the user's device and obtain location information and movement paths. This allows the data collection unit to collect a wide range of data from various devices and understand the situation in real time. The collected data is stored in a database and managed so that the analysis and generation units can access it. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. For example, by increasing the frequency of data collection during times when there is heavy human movement, such as at the start or end of an event, more detailed information can be obtained. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis unit analyzes data collected by the collection unit to analyze people's motivations for passing through areas in real time. For example, the analysis unit can use AI to analyze collected data and understand congestion levels and fluctuation rates. Specifically, the AI ​​uses image recognition technology to analyze camera footage and identify the density and direction of movement of people in a particular area. It can also use data mining technology to analyze people's behavior patterns from past data and estimate their motivations for passing through areas. For example, it can predict congestion trends during specific time periods or the flow of people when a particular event is held. Furthermore, the analysis unit can analyze collected location information and biometric data to understand users' movement speed and dwell time. This allows the analysis unit to understand people's behavior in real time and predict the occurrence of congestion. The analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. For example, if an abnormally high density of people exceeding normal congestion levels is detected, the analysis unit can immediately issue a warning and take appropriate measures. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0068] The generation unit generates advice and instructions to provide an optimal traffic environment based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to advise on the optimal traffic routes and times based on the analysis results. Specifically, the AI ​​uses the analysis results to recommend the best routes to avoid congestion and traffic during specific times. For example, if a particular area is congested, the generation unit will suggest an alternative route to avoid that area. It can also use natural language generation technology to generate easy-to-understand advice and instructions for users. For example, it can generate specific instructions such as, "The north entrance is currently very congested. We recommend entering from the south entrance." Furthermore, the generation unit can also provide advice to event organizers and managers based on the analysis results. For example, if a particular area is congested, it can suggest deploying additional guidance staff. In this way, the generation unit can provide appropriate advice not only to users but also to organizers and managers, optimizing the overall traffic environment. The generation unit can continuously generate advice and instructions based on analysis results that are updated in real time, and respond to the latest situation. In this way, the generation unit can always provide an optimal traffic environment and achieve safe and smooth pedestrian flow control.

[0069] The service provider provides advice and instructions generated by the generation unit. For example, the service provider can provide advice and instructions to users using smartphones or digital signage. Specifically, it can notify users in real time with advice and instructions via a smartphone app. For instance, it can use the app's push notification function to inform users of congestion levels and optimal routes. It can also use digital signage to display congestion levels and routes in specific areas. This allows users to obtain the latest information in real time. Furthermore, the service provider can also provide advice and instructions using a voice assistant. For example, it can provide users with voice guidance on congestion levels and optimal routes through a voice assistant. This provides not only visual but also auditory information, improving user convenience. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of its advice and instructions. For example, it can collect feedback on the results of users following the provided advice and provide this feedback to the analysis and generation units, thereby improving the overall system accuracy. This enables the service provider to provide advice and instructions to users quickly and reliably, achieving safe and smooth pedestrian flow control.

[0070] The data collection unit can monitor people's movements at event venues, tourist destinations, etc., and collect data. For example, the data collection unit can monitor people's movements at event venues, tourist destinations, etc., using cameras and sensors and collect data. For example, the data collection unit can monitor people's movements in real time using cameras and collect data. The data collection unit can also monitor people's movements using sensors and collect data. For example, the data collection unit can collect data from smartphones and wearable devices. This allows for more accurate pedestrian flow data to be obtained by monitoring people's movements at event venues, tourist destinations, etc., and collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data acquired by a camera into a generating AI and analyze people's movements from the image data.

[0071] The analysis unit can analyze the collected data to understand congestion levels and fluctuation rates. For example, the analysis unit can analyze the collected data using AI to understand congestion levels and fluctuation rates. For example, the analysis unit can analyze the collected data using data mining techniques to understand congestion levels and fluctuation rates. The analysis unit can also analyze the collected data using machine learning algorithms to understand congestion levels and fluctuation rates. For example, the analysis unit can analyze the collected data using clustering techniques to understand congestion levels and fluctuation rates. By analyzing the collected data and understanding congestion levels and fluctuation rates, more appropriate traffic advice can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of congestion levels and fluctuation rates.

[0072] The generation unit can advise on the optimal travel route and time based on the analysis results. For example, the generation unit can advise on the optimal travel route and time based on the analysis results using AI. For example, the generation unit can advise on the optimal travel route and time based on the analysis results using natural language generation technology. The generation unit can also advise on the optimal travel route and time based on the analysis results using machine learning algorithms. For example, the generation unit can advise on the optimal travel route and time based on the analysis results using clustering technology. By advising on the optimal travel route and time based on the analysis results, congestion can be avoided and efficient travel can be achieved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI provide advice on the optimal travel route and time.

[0073] The service provider can provide advice and instructions to users and support optimal traffic flow. For example, the service provider can provide advice and instructions to users using smartphones or digital signage. The service provider can provide advice and instructions to users using smartphones, for example. The service provider can also provide advice and instructions to users using digital signage. The service provider can also provide advice and instructions to users using voice assistants, for example. This enables safe and smooth pedestrian flow by providing advice and instructions to users and supporting optimal traffic flow. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated advice and instructions into a generating AI and have the generating AI execute the optimal method for providing them to the user.

[0074] The generation unit can issue instructions to avoid a particular area if that area is congested. For example, the generation unit can use AI to issue instructions to avoid a particular area if that area is congested. For example, the generation unit can use natural language generation technology to issue instructions to avoid a particular area if that area is congested. The generation unit can also use machine learning algorithms to issue instructions to avoid a particular area if that area is congested. For example, the generation unit can use clustering technology to issue instructions to avoid a particular area if that area is congested. In this way, by issuing instructions to avoid a particular area if that area is congested, congestion can be alleviated and pedestrian flow can be made smoother. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about congested areas into a generation AI and have the generation AI execute instructions to avoid those areas.

[0075] The generation unit can provide advice to avoid certain time periods when congestion is expected. For example, the generation unit can use AI to provide advice to avoid certain time periods when congestion is expected. For example, the generation unit can use natural language generation technology to provide advice to avoid certain time periods when congestion is expected. The generation unit can also use machine learning algorithms to provide advice to avoid certain time periods when congestion is expected. For example, the generation unit can use clustering technology to provide advice to avoid certain time periods when congestion is expected. By providing advice to avoid certain time periods when congestion is expected, congestion can be avoided and efficient traffic flow can be achieved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about time periods when congestion is expected to occur into a generation AI and have the generation AI provide advice to avoid those time periods.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also adjust the timing of data collection to quickly collect the necessary data if the user is in a hurry. In this way, by adjusting the timing of data collection based on the user's emotions, the user's burden can be reduced and efficient data collection can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0077] The data collection unit can measure the length of time people spend in specific areas of event venues or tourist destinations, thereby improving the accuracy of data collection. For example, the data collection unit can measure the length of time people spend in specific areas to understand the degree of congestion. For example, the data collection unit can identify areas where people spend a long time and focus data collection on those areas. The data collection unit can also identify areas where people spend a short time and adjust the frequency of data collection. This improves the accuracy of data collection by measuring the length of time people spend in specific areas. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the length of time people spend in specific areas into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0078] The data collection unit can select the optimal data collection method by referring to the user's past movement history when collecting data. For example, the data collection unit can refer to the user's past movement history and select the optimal data collection point. For example, the data collection unit can select a data collection method to avoid congestion based on past movement history. The data collection unit can also analyze the user's movement patterns and select an efficient data collection method. In this way, by referring to the user's past movement history, the optimal data collection method can be selected and efficient data collection can be achieved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past movement history data into a generating AI and have the generating AI select the optimal data collection method.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Also, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data prioritization.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of highly relevant data by considering the user's travel route. Furthermore, the data collection unit can also prioritize the collection of highly relevant data based on the user's destination. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media posts and collect relevant data. For example, the data collection unit can analyze the user's social media friendships and collect relevant data. The data collection unit can also analyze the user's social media location information and collect relevant data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can relax the analysis criteria to reduce the user's burden. For example, if the user is relaxed, the analysis unit can tighten the analysis criteria and perform a more detailed analysis. The analysis unit can also adjust the analysis criteria to perform a rapid analysis if the user is in a hurry. By adjusting the analysis criteria based on the user's emotions, the user's burden can be reduced and efficient analysis can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis criteria.

[0083] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the collected data during the analysis. For example, the analysis unit can analyze the interrelationships of the collected data to improve the accuracy of the analysis. For example, the analysis unit can adjust the analysis algorithm by considering the interrelationships of the data. The analysis unit can also correct the analysis results based on the interrelationships of the data. In this way, the accuracy of the analysis can be improved by considering the interrelationships of the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the interrelationships of the collected data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0084] The analysis unit can apply different analysis algorithms depending on specific events or seasons during analysis. For example, the analysis unit can adjust the analysis algorithm according to a specific event. For example, the analysis unit can change the analysis algorithm according to the season. Furthermore, the analysis unit can consider the characteristics of events and seasons and apply the optimal analysis algorithm. This allows for improved accuracy of the analysis by applying different analysis algorithms according to specific events and seasons. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data corresponding to specific events or seasons into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0086] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze the geographical distribution of the data to improve the accuracy of the analysis. For example, the analysis unit can adjust the analysis algorithm while considering the geographical distribution. The analysis unit can also correct the analysis results based on the geographical distribution. In this way, the accuracy of the analysis can be improved by considering the geographical distribution of the data. 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 the geographical distribution of the data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0087] The analysis unit can improve the accuracy of the analysis by referring to relevant historical data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to historical data. For example, the analysis unit can adjust the analysis algorithm based on relevant historical data. The analysis unit can also correct the analysis results based on historical data. In this way, the accuracy of the analysis can be improved by referring to historical data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input historical data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0088] The generation unit can estimate the user's emotions and adjust the expression of advice and instructions based on the estimated emotions. For example, if the user is stressed, the generation unit can provide a simple and easy-to-understand expression. For example, if the user is relaxed, the generation unit can provide an expression that includes detailed information. Also, if the user is in a hurry, the generation unit can provide a quick and concise expression. By adjusting the expression of advice and instructions based on the user's emotions, it becomes possible to provide expressions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the expression of advice and instructions.

[0089] The generation unit can adjust the level of detail in advice and instructions based on the importance of the analysis results during generation. For example, if the analysis results are important, the generation unit can provide detailed advice and instructions. For example, if the analysis results are general, the generation unit can provide concise advice and instructions. Furthermore, if the analysis results are urgent, the generation unit can provide quick and concise advice and instructions. This allows for the provision of appropriate information by adjusting the level of detail in advice and instructions based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the generation AI and have the generation AI adjust the level of detail in the advice and instructions.

[0090] The generation unit can apply different generation algorithms depending on the specific area or time of day during generation. For example, the generation unit can adjust the generation algorithm depending on the specific area. For example, the generation unit can change the generation algorithm depending on the specific time of day. Furthermore, the generation unit can consider the characteristics of the area and time of day and apply the optimal generation algorithm. This allows for the provision of optimal advice and instructions by applying different generation algorithms depending on the specific area and time of day. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data corresponding to a specific area and time of day into a generation AI and have the generation AI execute the application of the optimal generation algorithm.

[0091] The generation unit can estimate the user's emotions and determine the priority of advice and instructions to generate based on the estimated emotions. For example, if the user is stressed, the generation unit can prioritize important advice and instructions. If the user is relaxed, the generation unit can prioritize detailed advice and instructions. Also, if the user is in a hurry, the generation unit can prioritize advice and instructions that can be delivered quickly. In this way, by prioritizing advice and instructions based on the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of advice and instructions.

[0092] The generation unit can generate optimal advice and instructions while considering the user's geographical location information. For example, the generation unit can provide optimal advice and instructions based on the user's current location. For example, the generation unit can provide optimal advice and instructions while considering the user's travel route. Furthermore, the generation unit can also provide optimal advice and instructions based on the user's destination. In this way, by considering the user's geographical location information, optimal advice and instructions can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of optimal advice and instructions.

[0093] The generation unit can analyze the user's social media activity during generation and generate relevant advice and instructions. For example, the generation unit can analyze the user's social media posts and provide relevant advice and instructions. For example, the generation unit can analyze the user's social media friendships and provide relevant advice and instructions. The generation unit can also analyze the user's social media location information and provide relevant advice and instructions. In this way, relevant advice and instructions can be provided by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant advice and instructions.

[0094] The service provider can estimate the user's emotions and adjust the way advice and instructions are delivered based on those estimated emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-understand method of delivery. If the user is relaxed, the service provider can provide a method of delivery that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a quick and concise method of delivery. This allows for user-friendly delivery by adjusting the delivery method of advice and instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the delivery method of advice and instructions.

[0095] The service provider can select the optimal service delivery method by referring to the user's past behavior history at the time of delivery. For example, the service provider can refer to the user's past behavior history and select the optimal service delivery method. For example, the service provider can select a service delivery method preferred by the user based on their past behavior history. The service provider can also analyze the user's behavior patterns and select an efficient service delivery method. This makes it possible to select the optimal service delivery method by referring to the user's past behavior history and to provide information efficiently. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past behavior history data into a generating AI and have the generating AI select the optimal service delivery method.

[0096] The delivery unit can apply different delivery methods depending on the specific device or platform at the time of delivery. For example, if a smartphone is being used, the delivery unit can provide a delivery method adapted to the screen size. If a tablet is being used, the delivery unit can provide a delivery method optimized for a larger screen. Furthermore, if a smartwatch is being used, the delivery unit can provide a concise and highly visible delivery method. This enables optimal information delivery by applying different delivery methods depending on the specific device or platform. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data specific to the device or platform into a generating AI and have the generating AI execute the application of the optimal delivery method.

[0097] The service provider can estimate the user's emotions and adjust the frequency of advice and instructions based on the estimated emotions. For example, if the user is stressed, the service provider can reduce the frequency of advice and instructions to lessen the user's burden. For example, if the user is relaxed, the service provider can increase the frequency of advice and instructions to provide more detailed information. The service provider can also adjust the frequency of advice and instructions to provide them quickly if the user is in a hurry. By adjusting the frequency of advice and instructions based on the user's emotions, the service provider can reduce the user's burden and provide information efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the frequency of advice and instructions.

[0098] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's current location. For example, the service provider can select the optimal service delivery method by considering the user's travel route. Furthermore, the service provider can also select the optimal service delivery method based on the user's destination. This makes it possible to select the optimal service delivery method by considering the user's geographical location information, enabling efficient information delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0099] The service provider can analyze the user's social media activity and provide relevant advice and instructions at the time of delivery. For example, the service provider can analyze the user's social media posts and provide relevant advice and instructions. For example, the service provider can analyze the user's social media friendships and provide relevant advice and instructions. The service provider can also analyze the user's social media location information and provide relevant advice and instructions. In this way, by analyzing the user's social media activity, relevant advice and instructions can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant advice and instructions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and adjust the confidence level of the analysis results based on the estimated emotions. For example, if the user is stressed, the confidence level of the analysis results can be set low for careful decision-making. If the user is relaxed, the confidence level can be set high for quick decision-making. If the user is in a hurry, the confidence level can be set to a moderate level for balanced decision-making. By adjusting the confidence level of the analysis results based on the user's emotions, more appropriate decisions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the confidence level of the analysis results.

[0102] The data collection unit can estimate the user's emotions and adjust the scope of data collection based on the estimated emotions. For example, if the user is stressed, the scope of data collection can be narrowed to reduce the user's burden. If the user is relaxed, the scope of data collection can be broadened to collect more detailed data. If the user is in a hurry, the scope of data collection can be set to a moderate level for efficient data collection. In this way, by adjusting the scope of data collection based on the user's emotions, the user's burden can be reduced and efficient data collection can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the scope of data collection.

[0103] The service provider can estimate the user's emotions and adjust the timing of advice and instructions based on the estimated emotions. For example, if the user is stressed, the service provider can delay the delivery to reduce the user's burden. If the user is relaxed, the service provider can speed up the delivery to provide information quickly. If the user is in a hurry, the service provider can also adjust the delivery timing appropriately to provide information efficiently. By adjusting the timing of advice and instructions based on the user's emotions, the service provider can reduce the user's burden and provide information efficiently. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the timing of advice and instructions.

[0104] The generation unit can estimate the user's emotions and adjust the content of the advice and instructions it generates based on the estimated emotions. For example, if the user is stressed, it can provide simple and easy-to-understand advice and instructions. If the user is relaxed, it can provide advice and instructions that include detailed information. If the user is in a hurry, it can provide quick and concise advice and instructions. In this way, by adjusting the content of the advice and instructions based on the user's emotions, it becomes possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the advice and instructions.

[0105] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, important data can be prioritized for analysis. If the user is relaxed, detailed data can be prioritized for analysis. Also, if the user is in a hurry, data that can be analyzed quickly can be prioritized for analysis. In this way, by determining the priority of analysis based on the user's emotions, important data can be prioritized for analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0106] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method during data collection. For example, it can analyze the user's past behavior patterns and select a data collection method that avoids congestion. It can also select an efficient data collection method based on the user's behavior patterns. Furthermore, it can refer to the user's past behavior patterns to select the optimal data collection points. In this way, by analyzing the user's past behavior patterns, the optimal data collection method can be selected, and efficient data collection can be achieved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior pattern data into a generating AI and have the generating AI select the optimal collection method.

[0107] The analysis unit can perform analysis while considering the temporal variation of the data. For example, it can analyze the temporal variation of the data to improve the accuracy of the analysis. It can adjust the analysis algorithm while considering the temporal variation of the data. It can also correct the analysis results based on the temporal variation of the data. In this way, the accuracy of the analysis can be improved by considering the temporal variation of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the temporal variation of the data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0108] The generation unit can generate optimal advice and instructions by referring to the user's past behavior history during the generation process. For example, it can provide optimal advice and instructions by referring to the user's past behavior history. It can also provide advice and instructions that the user prefers based on past behavior history. Furthermore, it can analyze the user's behavior patterns and provide efficient advice and instructions. This makes it possible to provide optimal advice and instructions and deliver information efficiently by referring to the user's past behavior history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the user's past behavior history data into a generation AI and have the generation AI execute the generation of optimal advice and instructions.

[0109] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, it can select the optimal service delivery method based on the user's current location. It can also select the optimal service delivery method by considering the user's travel route. Furthermore, it can select the optimal service delivery method based on the user's destination. This allows for efficient information delivery by selecting the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0110] The service provider can analyze the user's social media activity and provide relevant advice and instructions at the time of service delivery. For example, it can analyze the user's social media posts and provide relevant advice and instructions. It can also analyze the user's social media friendships and provide relevant advice and instructions. Furthermore, it can analyze the user's social media location information and provide relevant advice and instructions. In this way, by analyzing the user's social media activity, it is possible to provide relevant advice and instructions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant advice and instructions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects data on people's behavior. The data collection unit monitors people's movements in places like event venues and tourist destinations and collects data. The data collection unit can monitor people's movements in real time using cameras and sensors and collect data. The data collection unit can also collect data from smartphones and wearable devices. Step 2: The analysis unit analyzes the data collected by the collection unit to analyze people's motivation for passing through in real time. The analysis unit can use AI to analyze the collected data and understand the degree of congestion and the rate of change. The analysis unit can use data mining technology to analyze people's behavior patterns and estimate their motivation for passing through. Step 3: The generation unit generates advice and instructions to provide the optimal traffic environment based on the analysis results obtained by the analysis unit. The generation unit can use AI to advise on the optimal traffic route and time based on the analysis results. The generation unit can use natural language generation technology to generate advice and instructions. Step 4: The providing unit provides the advice and instructions generated by the generating unit. The providing unit can provide advice and instructions to the user using a smartphone or digital signage. The providing unit can also provide advice and instructions to the user using a voice assistant.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit monitors people's movements in real time using the camera 42 and sensors of the smart device 14 and collects data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze people's motivation to pass in real time. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and instructions to provide an optimal passage environment based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated advice and instructions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation 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 collection unit monitors people's movements in real time using the camera 42 and sensors of the smart glasses 214 and collects data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze people's motivation to pass in real time. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and instructions to provide an optimal passage environment based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated advice and instructions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit monitors people's movements in real time using the camera 42 and sensors of the headset terminal 314 and collects data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze people's motivation to pass in real time. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice and instructions to provide an optimal passage environment based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated advice and instructions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation 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 collection unit monitors people's movements in real time using the camera 42 and sensors of the robot 414 and collects data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze people's motivation to pass in real time. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates advice and instructions to provide an optimal passage environment based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the generated advice and instructions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that collects people's behavioral data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes people's motivation to pass through in real time, Based on the analysis results obtained by the aforementioned analysis unit, a generation unit generates advice and instructions to provide an optimal traffic environment. The system includes a providing unit that provides advice and instructions generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Monitor people's movements at event venues, tourist destinations, and other locations, and collect data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to understand the degree of congestion and the rate of change. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, we will advise you on the optimal route and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide users with advice and instructions to support them in finding the best route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is If a particular area is crowded, instruct people to avoid that area. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is If congestion is expected during a specific time period, advise customers to avoid that time. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Measure the amount of time people spend in specific areas of event venues and tourist destinations to improve the accuracy of data collection. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system selects the optimal collection method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the interrelationships between collected data are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant historical data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the way advice and instructions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the level of detail in advice and instructions is adjusted based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the specific area or time of day. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and determines the priority of advice and instructions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system takes the user's geographical location into consideration to generate optimal advice and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant advice and instructions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how advice and instructions are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, At the time of delivery, different delivery methods will be applied depending on the specific device or platform. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts the frequency of advice and instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, it analyzes the user's social media activity and provides relevant advice and guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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. A data collection unit that collects people's behavioral data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes people's motivation to pass through in real time, Based on the analysis results obtained by the aforementioned analysis unit, a generation unit generates advice and instructions to provide an optimal traffic environment. The system includes a providing unit that provides advice and instructions generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Monitor people's movements at event venues, tourist destinations, and other locations, and collect data. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to understand the degree of congestion and the rate of change. The system according to feature 1.

4. The generating unit is Based on the analysis results, we will advise you on the optimal route and time of day. The system according to feature 1.

5. The aforementioned supply unit is, We provide users with advice and instructions to support them in finding the best route. The system according to feature 1.

6. The generating unit is If a particular area is crowded, instruct people to avoid that area. The system according to feature 1.

7. The generating unit is If congestion is expected during a specific time period, advise customers to avoid that time. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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