system

Abstract

We provide the system. [Solution] A means for inputting characteristic information of a group, A means for estimating an individual's psychological state based on the aforementioned characteristic information, A means for generating group psychology by aggregating the aforementioned individual psychological states, A means for simulating crowd behavior based on the aforementioned group psychology, A display means using augmented reality technology to overlay the aforementioned simulation results onto real space, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional disaster prevention measures, there has been a lack of simulations considering group psychology, making it difficult to accurately predict the movement of the crowd during a disaster and formulate an effective evacuation plan. For this reason, there is a problem that evacuation training and countermeasures under actual situations are insufficient, and as a result, there is a possibility of an increase in damage.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for inputting characteristic information of a group, means for estimating the psychological state of individuals based on the characteristic information, means for aggregating the estimated individual psychological states to generate group psychology, means for simulating crowd movements based on the generated group psychology, and display means using augmented reality technology to overlay the simulation results onto real space. This makes it possible to more accurately predict crowd evacuation behavior and improve the accuracy of actual disaster prevention drills and measures.

[0006] "Group characteristic information" refers to data about the individuals who make up a group, including information such as age group, gender composition, and areas of interest.

[0007] "Individual psychological state" refers to the psychological reactions and emotional states that an individual exhibits under specific conditions, and is expressed as a score such as anxiety level or calmness level.

[0008] "Group psychology" refers to the psychological characteristics and behavioral tendencies of a group as a whole, obtained by aggregating the psychological states of multiple individuals.

[0009] "Crowd movement" refers to the patterns of behavior and movement exhibited by a group under certain circumstances, particularly including choices and routes taken during evacuation.

[0010] "Simulation" refers to a technology or method that models real-world situations and virtually predicts and reproduces them on a computer.

[0011] Augmented reality technology is a technology that overlays digital information onto the real world environment, and typically uses devices such as AR glasses. [Brief explanation of the drawing]

[0012] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (for example, hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] In embodiments of this invention, a system is constructed in which the user, terminal, and server components function in coordination. This invention envisions a specific implementation of a group psychology simulation system for disaster prevention purposes.

[0034] Users input characteristic information about a group using their devices. This collected data is then sent to a server. Based on the received data, the server utilizes a multimodal AI model to estimate each individual's psychological state. This psychological state is then scored using specific indicators such as anxiety, calmness, and panic.

[0035] Next, the server aggregates the individual psychological states to form a collective psychology that represents the overall psychology of the group. Using this formed collective psychology, the server simulates crowd movement. This simulation is conducted to predict what behavioral patterns will emerge during a disaster, analyzing evacuation route selection and crowd flow.

[0036] The simulation results are transmitted to the terminal and presented to the user through AR glasses using augmented reality technology. This allows the user to experience the movement of crowds in the real world in a real-time, visualized form. For example, if a facility conducts an earthquake drill, this system can be used to simulate visitor evacuation routes and areas where congestion may occur, enabling the development of an appropriate evacuation plan. In this way, this system contributes to improving the accuracy of actual disaster prevention drills and policy considerations.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users use a terminal to input characteristic information about a group. This information includes age group, gender composition, and areas of interest. The terminal organizes this information into a database format and prepares it for transmission to the server.

[0040] Step 2:

[0041] The terminal sends characteristic information about the group to the server. The server verifies the integrity of the received data and stores it in a database. This allows the data to be quickly accessed in subsequent processing steps.

[0042] Step 3:

[0043] The server activates a multimodal AI model and estimates each individual's psychological state based on stored feature information. The estimation results are generated as numerical values ​​such as anxiety level, panic level, and calmness level.

[0044] Step 4:

[0045] The server aggregates individual psychological states to form a collective psychology. This collective psychology is then integrated into a score that represents overall anxiety and calmness.

[0046] Step 5:

[0047] The server uses a generated AI model to simulate crowd movements during disasters based on collective psychology. The simulation evaluates evacuation routes, congestion predictions, and the likelihood of panic.

[0048] Step 6:

[0049] The server formats the data to visualize the simulation results and prepares it for transmission to the terminal.

[0050] Step 7:

[0051] The device receives visualization data from the server and sends it to the AR glasses. Through the AR glasses, the user can visualize the simulation results overlaid on the real world and observe the movement of the crowd.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] It is difficult to grasp changes in the psychological state and behavioral patterns of a group in real time and to formulate an effective evacuation plan during a disaster. In particular, there are technical challenges in accurately assessing the psychological state of the entire group and presenting simulation results based on that assessment in a realistic manner in order to achieve safe and efficient evacuation.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for inputting characteristic information, means for estimating psychological states, and means for generating psychological states. This makes it possible to formulate more accurate evacuation plans through the estimation of the psychological state of a group and the simulation of crowd movements.

[0057] "Characteristic information" refers to information about the attributes and characteristics of individuals who make up a group, including age, gender, and areas of interest.

[0058] "Psychological state" refers to the mental and emotional state of an individual or group, and indicators such as anxiety level, calmness level, and panic level are used.

[0059] "Psychology" refers to the overall mental state aggregated from the individual psychological states, and is useful for predicting the average emotions and behaviors of a group.

[0060] "Movement simulation" is a process that predicts what behavioral patterns and movement paths will be taken under specific conditions, based on group psychology.

[0061] "Display technology" refers to technologies that use methods such as augmented reality to overlay digital information onto the real world.

[0062] Users input characteristic information about a specific group through a terminal. This terminal has a dedicated application installed, allowing users to easily register data such as age group, gender, and areas of interest.

[0063] The terminal sends the input information to the server. The server has high parallel processing capabilities and, after receiving the data, uses a multimodal AI model to estimate the psychological state. In this process, AI software such as TENSORFLOW® and PyTorch operates, scoring each individual's level of anxiety, calmness, and panic. This allows for a quantitative evaluation of each individual's psychological state.

[0064] The server aggregates the psychological states of each individual to form the overall psychology of the group. Then, based on this collective psychology, it simulates crowd behavior. The simulation is performed to predict crowd behavior during disasters and runs in a real-world environment using Unity or other simulation engines.

[0065] Finally, the device receives the simulation results and presents them to the user using augmented reality technology. By wearing AR glasses, the user can visually experience the simulated crowd movements and evacuation routes overlaid on the real world.

[0066] For example, if a facility conducts a disaster prevention drill simulating an earthquake, this system can be used to simulate evacuation routes for visitors and areas where people tend to gather, allowing for the development of an appropriate evacuation plan in advance. Another possible prompt for the generating AI model is, "Simulate the crowd psychology of 100 students in their 20s during an earthquake and provide recommended evacuation routes."

[0067] In this way, this system contributes to ensuring safety and facilitating efficient evacuation during disasters by understanding and applying group psychology.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] Users use their devices to input characteristic information about a group. This information includes age group and gender. The application on the device collects this data and sends it to the server for the next processing step.

[0071] Step 2:

[0072] The device transmits the collected characteristic information to the server. The data is securely transmitted to the server via the internet, and this input data becomes the basic data used by the server for estimating the user's psychological state.

[0073] Step 3:

[0074] The server receives feature information and uses a multimodal AI model to estimate each individual's psychological state. Based on the input feature information, the AI ​​model calculates psychological indicators such as anxiety, calmness, and panic. The AI ​​model is executed using TensorFlow or PyTorch, and as a result, each individual's psychological state score is output.

[0075] Step 4:

[0076] The server aggregates the calculated individual psychological state scores to generate the overall group psychology. By summing and averaging each psychological state, the group's psychological state is formed. This output provides the foundation for analyzing the group's behavioral tendencies.

[0077] Step 5:

[0078] The server simulates crowd movement based on collective psychology. Using simulation engines such as Unity, it realistically reproduces evacuation routes and crowd flow. This simulation allows for the acquisition of specific behavioral prediction patterns.

[0079] Step 6:

[0080] The server sends the simulation results to the terminal. This output is the data needed to be presented to the user.

[0081] Step 7:

[0082] The device displays the received simulation results to the user via AR glasses. The user can visually confirm the movement of the crowd superimposed on the real world and optimize their evacuation actions. In this process, the user can edit and modify their actions in real time based on the predicted behavioral patterns.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In places where crowds gather, predicting emergency evacuation procedures and congestion levels is crucial. However, conventional methods have made it difficult to grasp changes in group psychology in real time and take immediate action, thus hindering effective crowd management and disaster prevention measures.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating individual psychological states, a device for generating group psychology, a device for simulating crowd movement, a visualization device utilizing augmented reality technology, and a device for presenting real-time crowd management support information. This enables real-time prediction and visualization of crowd evacuation behavior and congestion in emergencies, allowing for a rapid and appropriate response.

[0088] A "device for inputting characteristic information of a group" is a device that allows the constituent elements and characteristics of a group to be input as data.

[0089] A "device for estimating individual psychological states" is a device that analyzes each individual's psychological response based on collected characteristic information.

[0090] A "device for generating group psychology" is a device that aggregates estimated individual psychological states to form an overall psychological trend.

[0091] A "device that simulates crowd movement" is a device that virtually reproduces crowd behavior patterns based on group psychology.

[0092] A "visualization device utilizing augmented reality technology" is a device that uses technology to overlay virtual information onto the real world and present it visually.

[0093] A "device that provides real-time crowd management support information" is a device that provides information necessary for group management immediately based on simulation results.

[0094] In the system that implements this application, each component works in a coordinated manner to predict and visualize crowd movement. The server first receives group characteristic information input from the user and then performs calculations to estimate the psychological state of each individual based on that information. A multimodal AI model is used for estimation to generate psychological state data for each individual.

[0095] Next, the server aggregates this individual psychological data to form data representing the overall psychology of the group. Based on this group psychology data, it simulates how the crowd's behavior will change. Simulation software and algorithms are used for the simulation.

[0096] The results of this simulation are provided to the user through visualization devices utilizing augmented reality technology. For example, smart glasses or head-mounted displays can serve this purpose, displaying crowd movement and congestion levels in real time, superimposed onto the real space. This makes it easier for disaster management and security personnel to appropriately control crowd flow.

[0097] A concrete example of its use is in large-scale music events. By collecting participant characteristic information, analyzing group psychology, and visualizing areas where congestion is expected, safe operation can be supported. This system functions effectively when prompted with the string, "Based on participant characteristic data, simulate safe evacuation routes in the event of an earthquake."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] Users use a device to input characteristic information about a group. This input information includes age group, gender composition, and areas of interest. The device collects this information as digital data and sends it to the server.

[0101] Step 2:

[0102] The server estimates an individual's psychological state based on the received characteristic information. Using a multimodal AI model, it calculates individual psychological indicators such as anxiety, calmness, and panic. By analyzing the input data and quantifying the psychological state, it generates psychological data for each individual.

[0103] Step 3:

[0104] The server aggregates the generated individual psychological state data to form a collective psychology. It statistically processes each individual's psychological indicators as a set and outputs data that shows the overall psychological trend of the group.

[0105] Step 4:

[0106] The server simulates crowd movement based on collective psychology data. Using simulation software, it visually reproduces how a crowd moves based on the input psychology data.

[0107] Step 5:

[0108] The simulation results are transmitted to a visualization device using augmented reality technology. For example, smart glasses act as a receiver, overlaying the results onto the real world. This allows users to see crowd movement patterns and changes in movement in real time.

[0109] Step 6:

[0110] The server provides crowd management support information using simulation results that are updated in real time. This allows users to obtain the information they need at the right time and take appropriate countermeasures.

[0111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0112] The present invention will now describe an embodiment of the system. This system consists of a user, a terminal, and a server unit that work together, and also incorporates an emotion engine that recognizes the user's emotions.

[0113] Users provide group characteristic information to the system via their devices. This characteristic information includes age group, gender composition, and areas of interest. The devices send this data to a server. The server uses a multimodal AI model to analyze the input characteristic information and estimate the individual's psychological state.

[0114] In this estimation process, the emotion engine plays a crucial role. The emotion engine utilizes speech recognition and facial expression analysis technologies to grasp the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of the individual's psychological state.

[0115] The server aggregates the psychological states of all individuals and generates collective psychology. Based on this generated collective psychology, the server simulates crowd movements during a disaster. This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0116] The simulation results are visualized to the user using augmented reality technology on the terminal. As a concrete example, consider an earthquake drill in a shopping mall. Using this system, facility managers can visualize the emotional state and psychological tendencies of visitors, enabling them to develop effective evacuation protocols in the event of an actual disaster. Thus, this system plays a crucial role in the formulation and implementation of disaster prevention plans.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] Users input group characteristics information using a terminal. This characteristics include age group, gender composition, and areas of interest. The terminal formats the input data, creating a format that the server can receive.

[0120] Step 2:

[0121] The terminal sends the formatted characteristic information of the group to the server. The server verifies the consistency of the received data and stores it in the appropriate database.

[0122] Step 3:

[0123] The server uses an emotion engine to analyze the user's emotions from their voice and video feeds. This analysis is achieved using speech recognition and facial expression analysis technologies.

[0124] Step 4:

[0125] The server integrates emotional information acquired by the emotion engine into a multimodal AI model to estimate individual psychological states. It then generates numerical scores representing each user's level of anxiety and calmness.

[0126] Step 5:

[0127] The server aggregates individual psychological states to form a collective psychology. This collective psychology is considered to reflect the behavioral tendencies of the entire group.

[0128] Step 6:

[0129] The server uses a generated AI model to simulate crowd movement based on collective psychology. The results are obtained by considering factors such as evacuation routes, congestion predictions, and crowd flow patterns.

[0130] Step 7:

[0131] The server formats the simulation results into a visually usable format and sends them to the terminal.

[0132] Step 8:

[0133] The device displays the simulation results received from the server on the AR glasses. Users can then see the crowd movements visualized overlaid on the real world, gaining a more immersive experience.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0136] There is a challenge in accurately understanding crowd movements and emotional states, and in more accurately predicting evacuation behavior during emergencies. In particular, formulating evacuation plans that take emotions into account is not easy, so realistic and practical simulations are needed.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes means for inputting characteristic information of a group, means for estimating psychological states using emotion analysis technology, and means for simulating crowd movements based on group psychology. This makes it possible to analyze the emotional state of users in real time, practically predict evacuation behavior, and present the optimal evacuation route.

[0139] "Group characteristic information" refers to data including each individual's age group, gender composition, and areas of interest, and represents information that shows the overall trend of the group.

[0140] "Individual psychological state" refers to the state of an individual user that indicates their emotions and mental tendencies, and specifically includes emotional information obtained through voice recognition and facial expression analysis.

[0141] "Group psychology" refers to the overall psychological characteristics and tendencies of a crowd, obtained by aggregating the individual psychological states of the group members.

[0142] "Methods for simulating crowd movement" refer to technologies that virtually reproduce the behavior and movement patterns of crowds in specific situations based on generated group psychology.

[0143] Augmented reality technology is a technology that overlays virtual information onto the real world, enabling users to visually perceive digital information within the real world.

[0144] This system functions through the coordinated efforts of user, terminal, and server units. Users provide group characteristic information to the system using terminals. This characteristic information includes age group, gender composition, and areas of interest. Terminals are responsible for collecting this data and transmitting it to the server.

[0145] The server analyzes the data using a multimodal AI model based on the received feature information. The emotion engine plays a crucial role in this analysis process. The emotion engine utilizes speech recognition technology (e.g., APIs that convert voice input to text) and facial expression analysis technology (e.g., software that detects emotions from camera footage) to understand the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of each individual's psychological state.

[0146] The aggregated individual psychological states are then transformed into collective psychology using an algorithm. Based on this collective psychology, the server utilizes a generative AI model to simulate crowd behavior during a disaster. An example of a specific prompt is, "Analyze the anxiety levels of a group of men aged 25 to 35." This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0147] Finally, the simulation results are visualized to the user using augmented reality technology on the device. Augmented reality technology (e.g., through the use of AR software and display technology) overlays virtual evacuation routes onto the user's real space, enabling intuitive understanding. A system constructed in this way is extremely useful in the development and implementation of disaster prevention plans.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] Users input group characteristics information, including age group, gender composition, and areas of interest, using their devices. This input data is filled into the application form on the device, and the data is collected when the submit button is pressed.

[0151] Step 2:

[0152] The terminal sends the characteristic information entered by the user to the server. This transmission process uses encryption technology to ensure the data is securely transferred. The input data is sent here and passed to the server as output.

[0153] Step 3:

[0154] The server operates a multimodal AI model based on the received feature information. The input data is feature information, and the output is an estimated individual psychological state. This process employs data analysis techniques, and the AI ​​model identifies each user's psychological pattern by comparing it to the dataset.

[0155] Step 4:

[0156] The server uses an emotion engine to perform real-time speech recognition and facial expression analysis to understand the user's emotions. Input is speech and facial expression data, and output is emotional information. This utilizes speech recognition APIs and facial recognition software.

[0157] Step 5:

[0158] The server aggregates estimated individual psychological states and emotional information to generate group psychology. The input data consists of individual psychological states and emotional information, while the output is group psychology. An algorithm is used in this aggregation process to reveal the overall psychological tendencies of the group.

[0159] Step 6:

[0160] The server uses a generative AI model to simulate crowd behavior based on generated group psychology. The input is group psychology, and the output is the simulation result. Specifically, prompts are input into the model to obtain detailed insights into evacuation behavior and evacuation routes.

[0161] Step 7:

[0162] The device visualizes simulation results sent from the server to the user using augmented reality technology. The input is the simulation result, and the output is visual information superimposed on the real world. Specifically, virtual information is displayed in the user's real environment using AR technology.

[0163] (Application Example 2)

[0164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0165] The objective of this invention is to provide safe and optimal evacuation routes that take into account the individual psychological and emotional states of users in spaces where diverse users gather. In particular, in emergency situations, rapid and accurate evacuation is necessary, and a system that reflects the psychology and behavior of the group in real time is required.

[0166] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0167] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating the psychological state of individuals, and a device for generating group psychology. This makes it possible to analyze the emotional state of users in real time and provide customized evacuation routes based on that analysis.

[0168] "Group characteristic information" refers to basic information about a group, such as age group, gender composition, and areas of interest, which is necessary to estimate the psychological state of individuals within that group.

[0169] "Individual psychological state" refers to information that describes the unique psychological state of each individual within a group, and usually includes tendencies in emotions and thoughts.

[0170] "Group psychology" refers to information that represents the overall psychological tendencies and behavioral patterns of a group, obtained by aggregating the psychological states of individuals.

[0171] "Crowd movement simulation" is a process that virtually reproduces how crowds move or behave based on group psychology.

[0172] Augmented reality technology is a technology that overlays digital information onto real-world space, and typically uses smartphones or dedicated devices.

[0173] An "emotion recognition device" is a device that analyzes voice and facial expression data to identify an individual's emotional state in real time.

[0174] A "customized evacuation route" is a safe and effective evacuation route optimized based on group characteristics and individual psychological states.

[0175] The system for implementing this invention primarily aims to estimate the individual psychological states based on group characteristic information and aggregate them to generate group psychology. The system uses a smartphone or dedicated device as hardware, and utilizes TensorFlow, OpenCV, and Unity as software necessary for emotion recognition and data analysis.

[0176] Users input group characteristics such as age group, gender composition, and areas of interest through their devices. The emotion recognition device within the device collects voice and facial expression data in real time and analyzes individual emotional states. TensorFlow and OpenCV are used for this analysis, and the resulting emotional data is sent to a server.

[0177] The server generates collective psychology based on aggregated individual psychological states and uses that data to simulate crowd movement. The simulated evacuation routes are visualized in AR format on the user's device using Unity, thereby providing a customized evacuation route for each individual user.

[0178] For example, in a shopping mall, if an emergency suddenly occurs, the system can instantly analyze the emotional state of the users and visually guide them to a safe evacuation route. This allows users to avoid confusion and evacuate safely.

[0179] An example of a prompt for a generative AI model is: "Analyze the psychological state of groups, including families, in a shopping mall in real time and customize safe evacuation routes."

[0180] Thus, the present invention supports a rapid and safe evacuation process by providing an evacuation plan that reflects emotional states.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] Users input characteristic information about a group, such as age group, gender composition, and areas of interest, using their device.

[0184] The entered information is formatted for transmission to the server after initial verification and data format conversion are performed on the terminal.

[0185] Step 2:

[0186] The device uses its camera and microphone to collect user voice and facial expression data.

[0187] The collected data is analyzed using OpenCV and TensorFlow to identify individual emotional states.

[0188] The results of the emotion recognition are output as a numerical emotion score and sent to the server.

[0189] Step 3:

[0190] The server receives group characteristic information and sentiment scores sent from the terminal.

[0191] Based on this information, a multimodal AI model is used to estimate the individual's psychological state.

[0192] The estimation results are stored as intermediate data.

[0193] Step 4:

[0194] The server aggregates intermediate data and performs a process to generate collective psychology.

[0195] Here, common psychological tendencies and patterns within a group are extracted and output as group psychology.

[0196] Step 5:

[0197] Based on the generated group psychology, the server performs a simulation of crowd movement.

[0198] The simulation results include recommended evacuation routes, and these scenarios are developed by a generative AI model.

[0199] Step 6:

[0200] The server converts the simulation results into data for visualization using augmented reality technology and sends it to the terminal.

[0201] On the device, Unity is used to display the evacuation route on the user's screen, overlaid on the real world.

[0202] This series of processes enables users to receive safe and effective evacuation instructions tailored to their emotional state in real time.

[0203] 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.

[0204] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0210] 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.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0212] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] 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.

[0214] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The 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.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] In embodiments of this invention, a system is constructed in which the user, terminal, and server components function in coordination. This invention envisions a specific implementation of a group psychology simulation system for disaster prevention purposes.

[0220] Users input characteristic information about a group using their devices. This collected data is then sent to a server. Based on the received data, the server utilizes a multimodal AI model to estimate each individual's psychological state. This psychological state is then scored using specific indicators such as anxiety, calmness, and panic.

[0221] Next, the server aggregates the individual psychological states to form a collective psychology that represents the overall psychology of the group. Using this formed collective psychology, the server simulates crowd movement. This simulation is conducted to predict what behavioral patterns will emerge during a disaster, analyzing evacuation route selection and crowd flow.

[0222] The simulation results are transmitted to the terminal and presented to the user through AR glasses using augmented reality technology. This allows the user to experience the movement of crowds in the real world in a real-time, visualized form. For example, if a facility conducts an earthquake drill, this system can be used to simulate visitor evacuation routes and areas where congestion may occur, enabling the development of an appropriate evacuation plan. In this way, this system contributes to improving the accuracy of actual disaster prevention drills and policy considerations.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] Users use a terminal to input characteristic information about a group. This information includes age group, gender composition, and areas of interest. The terminal organizes this information into a database format and prepares it for transmission to the server.

[0226] Step 2:

[0227] The terminal sends characteristic information about the group to the server. The server verifies the integrity of the received data and stores it in a database. This allows the data to be quickly accessed in subsequent processing steps.

[0228] Step 3:

[0229] The server activates a multimodal AI model and estimates each individual's psychological state based on stored feature information. The estimation results are generated as numerical values ​​such as anxiety level, panic level, and calmness level.

[0230] Step 4:

[0231] The server aggregates individual psychological states to form a collective psychology. This collective psychology is then integrated into a score that represents overall anxiety and calmness.

[0232] Step 5:

[0233] The server uses a generated AI model to simulate crowd movements during disasters based on collective psychology. The simulation evaluates evacuation routes, congestion predictions, and the likelihood of panic.

[0234] Step 6:

[0235] The server formats the data to visualize the simulation results and prepares it for transmission to the terminal.

[0236] Step 7:

[0237] The device receives visualization data from the server and sends it to the AR glasses. Through the AR glasses, the user can visualize the simulation results overlaid on the real world and observe the movement of the crowd.

[0238] (Example 1)

[0239] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0240] It is difficult to grasp changes in the psychological state and behavioral patterns of a group in real time and to formulate an effective evacuation plan during a disaster. In particular, there are technical challenges in accurately assessing the psychological state of the entire group and presenting simulation results based on that assessment in a realistic manner in order to achieve safe and efficient evacuation.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes means for inputting characteristic information, means for estimating psychological states, and means for generating psychological states. This makes it possible to formulate more accurate evacuation plans through the estimation of the psychological state of a group and the simulation of crowd movements.

[0243] "Characteristic information" refers to information about the attributes and characteristics of individuals who make up a group, including age, gender, and areas of interest.

[0244] "Psychological state" refers to the mental and emotional state of an individual or group, and indicators such as anxiety level, calmness level, and panic level are used.

[0245] "Psychology" refers to the overall mental state aggregated from the individual psychological states, and is useful for predicting the average emotions and behaviors of a group.

[0246] "Movement simulation" is a process that predicts what behavioral patterns and movement paths will be taken under specific conditions, based on group psychology.

[0247] "Display technology" refers to technologies that use methods such as augmented reality to overlay digital information onto the real world.

[0248] Users input characteristic information about a specific group through a terminal. This terminal has a dedicated application installed, allowing users to easily register data such as age group, gender, and areas of interest.

[0249] The terminal sends the input information to the server. The server has high parallel processing capabilities and, after receiving the data, uses a multimodal AI model to estimate the psychological state. In this process, AI software such as TensorFlow and PyTorch operates, scoring each individual's level of anxiety, calmness, and panic. This allows for a quantitative evaluation of each individual's psychological state.

[0250] The server aggregates the psychological states of each individual to form the overall psychology of the group. Then, based on this collective psychology, it simulates crowd behavior. The simulation is performed to predict crowd behavior during disasters and runs in a real-world environment using Unity or other simulation engines.

[0251] Finally, the device receives the simulation results and presents them to the user using augmented reality technology. By wearing AR glasses, the user can visually experience the simulated crowd movements and evacuation routes overlaid on the real world.

[0252] For example, if a facility conducts a disaster prevention drill simulating an earthquake, this system can be used to simulate evacuation routes for visitors and areas where people tend to gather, allowing for the development of an appropriate evacuation plan in advance. Another possible prompt for the generating AI model is, "Simulate the crowd psychology of 100 students in their 20s during an earthquake and provide recommended evacuation routes."

[0253] In this way, this system contributes to ensuring safety and facilitating efficient evacuation during disasters by understanding and applying group psychology.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users use their devices to input characteristic information about a group. This information includes age group and gender. The application on the device collects this data and sends it to the server for the next processing step.

[0257] Step 2:

[0258] The device transmits the collected characteristic information to the server. The data is securely transmitted to the server via the internet, and this input data becomes the basic data used by the server for estimating the user's psychological state.

[0259] Step 3:

[0260] The server receives feature information and uses a multimodal AI model to estimate each individual's psychological state. Based on the input feature information, the AI ​​model calculates psychological indicators such as anxiety, calmness, and panic. The AI ​​model is executed using TensorFlow or PyTorch, and as a result, each individual's psychological state score is output.

[0261] Step 4:

[0262] The server aggregates the calculated individual psychological state scores to generate the overall group psychology. By summing and averaging each psychological state, the group's psychological state is formed. This output provides the foundation for analyzing the group's behavioral tendencies.

[0263] Step 5:

[0264] The server simulates crowd movement based on collective psychology. Using simulation engines such as Unity, it realistically reproduces evacuation routes and crowd flow. This simulation allows for the acquisition of specific behavioral prediction patterns.

[0265] Step 6:

[0266] The server sends the simulation results to the terminal. This output is the data needed to be presented to the user.

[0267] Step 7:

[0268] The device displays the received simulation results to the user via AR glasses. The user can visually confirm the movement of the crowd superimposed on the real world and optimize their evacuation actions. In this process, the user can edit and modify their actions in real time based on the predicted behavioral patterns.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In places where crowds gather, predicting emergency evacuation procedures and congestion levels is crucial. However, conventional methods have made it difficult to grasp changes in group psychology in real time and take immediate action, thus hindering effective crowd management and disaster prevention measures.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating individual psychological states, a device for generating group psychology, a device for simulating crowd movement, a visualization device utilizing augmented reality technology, and a device for presenting real-time crowd management support information. This enables real-time prediction and visualization of crowd evacuation behavior and congestion in emergencies, allowing for a rapid and appropriate response.

[0274] A "device for inputting characteristic information of a group" is a device that allows the constituent elements and characteristics of a group to be input as data.

[0275] A "device for estimating individual psychological states" is a device that analyzes each individual's psychological response based on collected characteristic information.

[0276] A "device for generating group psychology" is a device that aggregates estimated individual psychological states to form an overall psychological trend.

[0277] A "device that simulates crowd movement" is a device that virtually reproduces crowd behavior patterns based on group psychology.

[0278] A "visualization device utilizing augmented reality technology" is a device that uses technology to overlay virtual information onto the real world and present it visually.

[0279] A "device that provides real-time crowd management support information" is a device that provides information necessary for group management immediately based on simulation results.

[0280] In the system that implements this application, each component works in a coordinated manner to predict and visualize crowd movement. The server first receives group characteristic information input from the user and then performs calculations to estimate the psychological state of each individual based on that information. A multimodal AI model is used for estimation to generate psychological state data for each individual.

[0281] Next, the server aggregates this individual psychological data to form data representing the overall psychology of the group. Based on this group psychology data, it simulates how the crowd's behavior will change. Simulation software and algorithms are used for the simulation.

[0282] The results of this simulation are provided to the user through visualization devices utilizing augmented reality technology. For example, smart glasses or head-mounted displays can serve this purpose, displaying crowd movement and congestion levels in real time, superimposed onto the real space. This makes it easier for disaster management and security personnel to appropriately control crowd flow.

[0283] As a specific example, it can be considered for use in large-scale music events. By collecting the characteristic information of participants, analyzing the group psychology, and visualizing the places where congestion is expected, safe operation is supported. This system functions effectively by inputting the string "Please simulate a safe evacuation route in case of an earthquake based on the characteristic data of the participants." into the prompt.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The user uses the terminal to input the characteristic information of the group. This input information includes the age group, gender composition, area of interest, etc. The terminal collects this information as digital data and transmits it to the server.

[0287] Step 2:

[0288] Based on the received characteristic information, the server estimates the individual psychological states. Using a multimodal AI model, individual psychological indicators such as the degree of anxiety, calmness, and panic are calculated. By analyzing the input data and quantifying the psychological states, the psychological data of each individual is generated.

[0289] Step 3:

[0290] The server aggregates the generated individual psychological state data to form the group psychology. The psychological indicators of each individual are statistically processed as a collective, and data indicating the psychological tendency of the entire group is output.

[0291] Step 4:

[0292] Based on the group psychology data, the server simulates the movement of the crowd. Utilizing simulation software, it visually reproduces how the crowd moves based on the input psychological data.

[0293] Step 5:

[0294] The simulation results are transmitted to a visualization device using augmented reality technology. For example, smart glasses act as a receiver, overlaying the results onto the real world. This allows users to see crowd movement patterns and changes in movement in real time.

[0295] Step 6:

[0296] The server provides crowd management support information using simulation results that are updated in real time. This allows users to obtain the information they need at the right time and take appropriate countermeasures.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] The present invention will now describe an embodiment of the system. This system consists of a user, a terminal, and a server unit that work together, and also incorporates an emotion engine that recognizes the user's emotions.

[0299] Users provide group characteristic information to the system via their devices. This characteristic information includes age group, gender composition, and areas of interest. The devices send this data to a server. The server uses a multimodal AI model to analyze the input characteristic information and estimate the individual's psychological state.

[0300] In this estimation process, the emotion engine plays a crucial role. The emotion engine utilizes speech recognition and facial expression analysis technologies to grasp the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of the individual's psychological state.

[0301] The server aggregates the psychological states of all individuals to generate group psychology. Based on the generated group psychology, the server simulates the movements of the crowd during a disaster. This simulation provides detailed insights regarding evacuation behaviors and the selection of evacuation routes.

[0302] The results of the simulation are visualized for the user by the terminal using augmented reality technology. As a specific example, consider earthquake training in a shopping mall. By using this system, facility managers can visualize the emotional states and psychological tendencies of visitors and construct effective evacuation protocols in the event of an actual disaster. Thus, this system plays a very important role in the formulation and implementation of disaster prevention plans.

[0303] The following describes the processing flow.

[0304] Step 1: <00009​​​​​​​​​​​​​​​​​​​​​​​​​The server integrates emotional information acquired by the emotion engine into a multimodal AI model to estimate individual psychological states. It then generates numerical scores representing each user's level of anxiety and calmness.

[0312] Step 5:

[0313] The server aggregates individual psychological states to form a collective psychology. This collective psychology is considered to reflect the behavioral tendencies of the entire group.

[0314] Step 6:

[0315] The server uses a generated AI model to simulate crowd movement based on collective psychology. The results are obtained by considering factors such as evacuation routes, congestion predictions, and crowd flow patterns.

[0316] Step 7:

[0317] The server formats the simulation results into a visually usable format and sends them to the terminal.

[0318] Step 8:

[0319] The device displays the simulation results received from the server on the AR glasses. Users can then see the crowd movements visualized overlaid on the real world, gaining a more immersive experience.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0322] There is a challenge in accurately understanding crowd movements and emotional states, and in more accurately predicting evacuation behavior during emergencies. In particular, formulating evacuation plans that take emotions into account is not easy, so realistic and practical simulations are needed.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for inputting characteristic information of a group, means for estimating psychological states using emotion analysis technology, and means for simulating crowd movements based on group psychology. This makes it possible to analyze the emotional state of users in real time, practically predict evacuation behavior, and present the optimal evacuation route.

[0325] "Group characteristic information" refers to data including each individual's age group, gender composition, and areas of interest, and represents information that shows the overall trend of the group.

[0326] "Individual psychological state" refers to the state of an individual user that indicates their emotions and mental tendencies, and specifically includes emotional information obtained through voice recognition and facial expression analysis.

[0327] "Group psychology" refers to the overall psychological characteristics and tendencies of a crowd, obtained by aggregating the individual psychological states of the group members.

[0328] "Methods for simulating crowd movement" refer to technologies that virtually reproduce the behavior and movement patterns of crowds in specific situations based on generated group psychology.

[0329] Augmented reality technology is a technology that overlays virtual information onto the real world, enabling users to visually perceive digital information within the real world.

[0330] This system functions through the coordinated efforts of user, terminal, and server units. Users provide group characteristic information to the system using terminals. This characteristic information includes age group, gender composition, and areas of interest. Terminals are responsible for collecting this data and transmitting it to the server.

[0331] The server analyzes the data using a multimodal AI model based on the received feature information. The emotion engine plays a crucial role in this analysis process. The emotion engine utilizes speech recognition technology (e.g., APIs that convert voice input to text) and facial expression analysis technology (e.g., software that detects emotions from camera footage) to understand the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of each individual's psychological state.

[0332] The aggregated individual psychological states are then transformed into collective psychology using an algorithm. Based on this collective psychology, the server utilizes a generative AI model to simulate crowd behavior during a disaster. An example of a specific prompt is, "Analyze the anxiety levels of a group of men aged 25 to 35." This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0333] Finally, the simulation results are visualized to the user using augmented reality technology on the device. Augmented reality technology (e.g., through the use of AR software and display technology) overlays virtual evacuation routes onto the user's real space, enabling intuitive understanding. A system constructed in this way is extremely useful in the development and implementation of disaster prevention plans.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] Users input group characteristics information, including age group, gender composition, and areas of interest, using their devices. This input data is filled into the application form on the device, and the data is collected when the submit button is pressed.

[0337] Step 2:

[0338] The terminal sends the characteristic information entered by the user to the server. This transmission process uses encryption technology to ensure the data is securely transferred. The input data is sent here and passed to the server as output.

[0339] Step 3:

[0340] The server operates a multimodal AI model based on the received feature information. The input data is feature information, and the output is an estimated individual psychological state. This process employs data analysis techniques, and the AI ​​model identifies each user's psychological pattern by comparing it to the dataset.

[0341] Step 4:

[0342] The server uses an emotion engine to perform real-time speech recognition and facial expression analysis to understand the user's emotions. Input is speech and facial expression data, and output is emotional information. This utilizes speech recognition APIs and facial recognition software.

[0343] Step 5:

[0344] The server aggregates estimated individual psychological states and emotional information to generate group psychology. The input data consists of individual psychological states and emotional information, while the output is group psychology. An algorithm is used in this aggregation process to reveal the overall psychological tendencies of the group.

[0345] Step 6:

[0346] The server uses a generative AI model to simulate crowd behavior based on generated group psychology. The input is group psychology, and the output is the simulation result. Specifically, prompts are input into the model to obtain detailed insights into evacuation behavior and evacuation routes.

[0347] Step 7:

[0348] The device visualizes simulation results sent from the server to the user using augmented reality technology. The input is the simulation result, and the output is visual information superimposed on the real world. Specifically, virtual information is displayed in the user's real environment using AR technology.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0351] The objective of this invention is to provide safe and optimal evacuation routes that take into account the individual psychological and emotional states of users in spaces where diverse users gather. In particular, in emergency situations, rapid and accurate evacuation is necessary, and a system that reflects the psychology and behavior of the group in real time is required.

[0352] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0353] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating the psychological state of individuals, and a device for generating group psychology. This makes it possible to analyze the emotional state of users in real time and provide customized evacuation routes based on that analysis.

[0354] "Group characteristic information" refers to basic information about a group, such as age group, gender composition, and areas of interest, which is necessary to estimate the psychological state of individuals within that group.

[0355] "Individual psychological state" refers to information that describes the unique psychological state of each individual within a group, and usually includes tendencies in emotions and thoughts.

[0356] "Group psychology" refers to information that represents the overall psychological tendencies and behavioral patterns of a group, obtained by aggregating the psychological states of individuals.

[0357] "Crowd movement simulation" is a process that virtually reproduces how crowds move or behave based on group psychology.

[0358] Augmented reality technology is a technology that overlays digital information onto real-world space, and typically uses smartphones or dedicated devices.

[0359] An "emotion recognition device" is a device that analyzes voice and facial expression data to identify an individual's emotional state in real time.

[0360] A "customized evacuation route" is a safe and effective evacuation route optimized based on group characteristics and individual psychological states.

[0361] The system for implementing this invention primarily aims to estimate the individual psychological states based on group characteristic information and aggregate them to generate group psychology. The system uses a smartphone or dedicated device as hardware, and utilizes TensorFlow, OpenCV, and Unity as software necessary for emotion recognition and data analysis.

[0362] Users input group characteristics such as age group, gender composition, and areas of interest through their devices. The emotion recognition device within the device collects voice and facial expression data in real time and analyzes individual emotional states. TensorFlow and OpenCV are used for this analysis, and the resulting emotional data is sent to a server.

[0363] The server generates collective psychology based on aggregated individual psychological states and uses that data to simulate crowd movement. The simulated evacuation routes are visualized in AR format on the user's device using Unity, thereby providing a customized evacuation route for each individual user.

[0364] For example, in a shopping mall, if an emergency suddenly occurs, the system can instantly analyze the emotional state of the users and visually guide them to a safe evacuation route. This allows users to avoid confusion and evacuate safely.

[0365] An example of a prompt for a generative AI model is: "Analyze the psychological state of groups, including families, in a shopping mall in real time and customize safe evacuation routes."

[0366] Thus, the present invention supports a rapid and safe evacuation process by providing an evacuation plan that reflects emotional states.

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] Users input characteristic information about a group, such as age group, gender composition, and areas of interest, using their device.

[0370] The entered information is formatted for transmission to the server after initial verification and data format conversion are performed on the terminal.

[0371] Step 2:

[0372] The device uses its camera and microphone to collect user voice and facial expression data.

[0373] The collected data is analyzed using OpenCV and TensorFlow to identify individual emotional states.

[0374] The results of the emotion recognition are output as a numerical emotion score and sent to the server.

[0375] Step 3:

[0376] The server receives group characteristic information and sentiment scores sent from the terminal.

[0377] Based on this information, a multimodal AI model is used to estimate the individual's psychological state.

[0378] The estimation results are stored as intermediate data.

[0379] Step 4:

[0380] The server aggregates intermediate data and performs a process to generate collective psychology.

[0381] Here, common psychological tendencies and patterns within a group are extracted and output as group psychology.

[0382] Step 5:

[0383] Based on the generated group psychology, the server performs a simulation of crowd movement.

[0384] The simulation results include recommended evacuation routes, and these scenarios are developed by a generative AI model.

[0385] Step 6:

[0386] The server converts the simulation results into data for visualization using augmented reality technology and sends it to the terminal.

[0387] On the device, Unity is used to display the evacuation route on the user's screen, overlaid on the real world.

[0388] This series of processes enables users to receive safe and effective evacuation instructions tailored to their emotional state in real time.

[0389] 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.

[0390] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0396] 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.

[0397] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0398] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0399] 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.

[0400] 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.

[0401] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0402] The 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.

[0403] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0404] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0405] In embodiments of this invention, a system is constructed in which the user, terminal, and server components function in coordination. This invention envisions a specific implementation of a group psychology simulation system for disaster prevention purposes.

[0406] Users input characteristic information about a group using their devices. This collected data is then sent to a server. Based on the received data, the server utilizes a multimodal AI model to estimate each individual's psychological state. This psychological state is then scored using specific indicators such as anxiety, calmness, and panic.

[0407] Next, the server aggregates the individual psychological states to form a collective psychology that represents the overall psychology of the group. Using this formed collective psychology, the server simulates crowd movement. This simulation is conducted to predict what behavioral patterns will emerge during a disaster, analyzing evacuation route selection and crowd flow.

[0408] The simulation results are transmitted to the terminal and presented to the user through AR glasses using augmented reality technology. This allows the user to experience the movement of crowds in the real world in a real-time, visualized form. For example, if a facility conducts an earthquake drill, this system can be used to simulate visitor evacuation routes and areas where congestion may occur, enabling the development of an appropriate evacuation plan. In this way, this system contributes to improving the accuracy of actual disaster prevention drills and policy considerations.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] Users use a terminal to input characteristic information about a group. This information includes age group, gender composition, and areas of interest. The terminal organizes this information into a database format and prepares it for transmission to the server.

[0412] Step 2:

[0413] The terminal sends characteristic information about the group to the server. The server verifies the integrity of the received data and stores it in a database. This allows the data to be quickly accessed in subsequent processing steps.

[0414] Step 3:

[0415] The server activates a multimodal AI model and estimates each individual's psychological state based on stored feature information. The estimation results are generated as numerical values ​​such as anxiety level, panic level, and calmness level.

[0416] Step 4:

[0417] The server aggregates individual psychological states to form a collective psychology. This collective psychology is then integrated into a score that represents overall anxiety and calmness.

[0418] Step 5:

[0419] The server uses a generated AI model to simulate crowd movements during disasters based on collective psychology. The simulation evaluates evacuation routes, congestion predictions, and the likelihood of panic.

[0420] Step 6:

[0421] The server formats the data to visualize the simulation results and prepares it for transmission to the terminal.

[0422] Step 7:

[0423] The device receives visualization data from the server and sends it to the AR glasses. Through the AR glasses, the user can visualize the simulation results overlaid on the real world and observe the movement of the crowd.

[0424] (Example 1)

[0425] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0426] It is difficult to grasp changes in the psychological state and behavioral patterns of a group in real time and to formulate an effective evacuation plan during a disaster. In particular, there are technical challenges in accurately assessing the psychological state of the entire group and presenting simulation results based on that assessment in a realistic manner in order to achieve safe and efficient evacuation.

[0427] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0428] In this invention, the server includes means for inputting characteristic information, means for estimating psychological states, and means for generating psychological states. This makes it possible to formulate more accurate evacuation plans through the estimation of the psychological state of a group and the simulation of crowd movements.

[0429] "Characteristic information" refers to information about the attributes and characteristics of individuals who make up a group, including age, gender, and areas of interest.

[0430] "Psychological state" refers to the mental and emotional state of an individual or group, and indicators such as anxiety level, calmness level, and panic level are used.

[0431] "Psychology" refers to the overall mental state aggregated from the individual psychological states, and is useful for predicting the average emotions and behaviors of a group.

[0432] "Movement simulation" is a process that predicts what behavioral patterns and movement paths will be taken under specific conditions, based on group psychology.

[0433] "Display technology" refers to technologies that use methods such as augmented reality to overlay digital information onto the real world.

[0434] Users input characteristic information about a specific group through a terminal. This terminal has a dedicated application installed, allowing users to easily register data such as age group, gender, and areas of interest.

[0435] The terminal sends the input information to the server. The server has high parallel processing capabilities and, after receiving the data, uses a multimodal AI model to estimate the psychological state. In this process, AI software such as TensorFlow and PyTorch operates, scoring each individual's level of anxiety, calmness, and panic. This allows for a quantitative evaluation of each individual's psychological state.

[0436] The server aggregates the psychological states of each individual to form the overall psychology of the group. Then, based on this collective psychology, it simulates crowd behavior. The simulation is performed to predict crowd behavior during disasters and runs in a real-world environment using Unity or other simulation engines.

[0437] Finally, the device receives the simulation results and presents them to the user using augmented reality technology. By wearing AR glasses, the user can visually experience the simulated crowd movements and evacuation routes overlaid on the real world.

[0438] For example, if a facility conducts a disaster prevention drill simulating an earthquake, this system can be used to simulate evacuation routes for visitors and areas where people tend to gather, allowing for the development of an appropriate evacuation plan in advance. Another possible prompt for the generating AI model is, "Simulate the crowd psychology of 100 students in their 20s during an earthquake and provide recommended evacuation routes."

[0439] In this way, this system contributes to ensuring safety and facilitating efficient evacuation during disasters by understanding and applying group psychology.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] Users use their devices to input characteristic information about a group. This information includes age group and gender. The application on the device collects this data and sends it to the server for the next processing step.

[0443] Step 2:

[0444] The device transmits the collected characteristic information to the server. The data is securely transmitted to the server via the internet, and this input data becomes the basic data used by the server for estimating the user's psychological state.

[0445] Step 3:

[0446] The server receives feature information and uses a multimodal AI model to estimate each individual's psychological state. Based on the input feature information, the AI ​​model calculates psychological indicators such as anxiety, calmness, and panic. The AI ​​model is executed using TensorFlow or PyTorch, and as a result, each individual's psychological state score is output.

[0447] Step 4:

[0448] The server aggregates the calculated individual psychological state scores to generate the overall group psychology. By summing and averaging each psychological state, the group's psychological state is formed. This output provides the foundation for analyzing the group's behavioral tendencies.

[0449] Step 5:

[0450] The server simulates crowd movement based on collective psychology. Using simulation engines such as Unity, it realistically reproduces evacuation routes and crowd flow. This simulation allows for the acquisition of specific behavioral prediction patterns.

[0451] Step 6:

[0452] The server sends the simulation results to the terminal. This output is the data needed to be presented to the user.

[0453] Step 7:

[0454] The device displays the received simulation results to the user via AR glasses. The user can visually confirm the movement of the crowd superimposed on the real world and optimize their evacuation actions. In this process, the user can edit and modify their actions in real time based on the predicted behavioral patterns.

[0455] (Application Example 1)

[0456] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] In places where crowds gather, predicting emergency evacuation procedures and congestion levels is crucial. However, conventional methods have made it difficult to grasp changes in group psychology in real time and take immediate action, thus hindering effective crowd management and disaster prevention measures.

[0458] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0459] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating individual psychological states, a device for generating group psychology, a device for simulating crowd movement, a visualization device utilizing augmented reality technology, and a device for presenting real-time crowd management support information. This enables real-time prediction and visualization of crowd evacuation behavior and congestion in emergencies, allowing for a rapid and appropriate response.

[0460] A "device for inputting characteristic information of a group" is a device that allows the constituent elements and characteristics of a group to be input as data.

[0461] A "device for estimating individual psychological states" is a device that analyzes each individual's psychological response based on collected characteristic information.

[0462] A "device for generating group psychology" is a device that aggregates estimated individual psychological states to form an overall psychological trend.

[0463] A "device that simulates crowd movement" is a device that virtually reproduces crowd behavior patterns based on group psychology.

[0464] A "visualization device utilizing augmented reality technology" is a device that uses technology to overlay virtual information onto the real world and present it visually.

[0465] A "device that provides real-time crowd management support information" is a device that provides information necessary for group management immediately based on simulation results.

[0466] In the system that implements this application, each component works in a coordinated manner to predict and visualize crowd movement. The server first receives group characteristic information input from the user and then performs calculations to estimate the psychological state of each individual based on that information. A multimodal AI model is used for estimation to generate psychological state data for each individual.

[0467] Next, the server aggregates this individual psychological data to form data representing the overall psychology of the group. Based on this group psychology data, it simulates how the crowd's behavior will change. Simulation software and algorithms are used for the simulation.

[0468] The results of this simulation are provided to the user through visualization devices utilizing augmented reality technology. For example, smart glasses or head-mounted displays can serve this purpose, displaying crowd movement and congestion levels in real time, superimposed onto the real space. This makes it easier for disaster management and security personnel to appropriately control crowd flow.

[0469] A concrete example of its use is in large-scale music events. By collecting participant characteristic information, analyzing group psychology, and visualizing areas where congestion is expected, safe operation can be supported. This system functions effectively when prompted with the string, "Based on participant characteristic data, simulate safe evacuation routes in the event of an earthquake."

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] Users use a device to input characteristic information about a group. This input information includes age group, gender composition, and areas of interest. The device collects this information as digital data and sends it to the server.

[0473] Step 2:

[0474] The server estimates an individual's psychological state based on the received characteristic information. Using a multimodal AI model, it calculates individual psychological indicators such as anxiety, calmness, and panic. By analyzing the input data and quantifying the psychological state, it generates psychological data for each individual.

[0475] Step 3:

[0476] The server aggregates the generated individual psychological state data to form a collective psychology. It statistically processes each individual's psychological indicators as a set and outputs data that shows the overall psychological trend of the group.

[0477] Step 4:

[0478] The server simulates crowd movement based on collective psychology data. Using simulation software, it visually reproduces how a crowd moves based on the input psychology data.

[0479] Step 5:

[0480] The simulation results are transmitted to a visualization device using augmented reality technology. For example, smart glasses act as a receiver, overlaying the results onto the real world. This allows users to see crowd movement patterns and changes in movement in real time.

[0481] Step 6:

[0482] The server provides crowd management support information using simulation results that are updated in real time. This allows users to obtain the information they need at the right time and take appropriate countermeasures.

[0483] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0484] The present invention will now describe an embodiment of the system. This system consists of a user, a terminal, and a server unit that work together, and also incorporates an emotion engine that recognizes the user's emotions.

[0485] Users provide group characteristic information to the system via their devices. This characteristic information includes age group, gender composition, and areas of interest. The devices send this data to a server. The server uses a multimodal AI model to analyze the input characteristic information and estimate the individual's psychological state.

[0486] In this estimation process, the emotion engine plays a crucial role. The emotion engine utilizes speech recognition and facial expression analysis technologies to grasp the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of the individual's psychological state.

[0487] The server aggregates the psychological states of all individuals and generates collective psychology. Based on this generated collective psychology, the server simulates crowd movements during a disaster. This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0488] The simulation results are visualized to the user using augmented reality technology on the terminal. As a concrete example, consider an earthquake drill in a shopping mall. Using this system, facility managers can visualize the emotional state and psychological tendencies of visitors, enabling them to develop effective evacuation protocols in the event of an actual disaster. Thus, this system plays a crucial role in the formulation and implementation of disaster prevention plans.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] Users input group characteristics information using a terminal. This characteristics include age group, gender composition, and areas of interest. The terminal formats the input data, creating a format that the server can receive.

[0492] Step 2:

[0493] The terminal sends the formatted characteristic information of the group to the server. The server verifies the consistency of the received data and stores it in the appropriate database.

[0494] Step 3:

[0495] The server uses an emotion engine to analyze the user's emotions from their voice and video feeds. This analysis is achieved using speech recognition and facial expression analysis technologies.

[0496] Step 4:

[0497] The server integrates emotional information acquired by the emotion engine into a multimodal AI model to estimate individual psychological states. It then generates numerical scores representing each user's level of anxiety and calmness.

[0498] Step 5:

[0499] The server aggregates individual psychological states to form a collective psychology. This collective psychology is considered to reflect the behavioral tendencies of the entire group.

[0500] Step 6:

[0501] The server uses a generated AI model to simulate crowd movement based on collective psychology. The results are obtained by considering factors such as evacuation routes, congestion predictions, and crowd flow patterns.

[0502] Step 7:

[0503] The server formats the simulation results into a visually usable format and sends them to the terminal.

[0504] Step 8:

[0505] The device displays the simulation results received from the server on the AR glasses. Users can then see the crowd movements visualized overlaid on the real world, gaining a more immersive experience.

[0506] (Example 2)

[0507] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] There is a challenge in accurately understanding crowd movements and emotional states, and in more accurately predicting evacuation behavior during emergencies. In particular, formulating evacuation plans that take emotions into account is not easy, so realistic and practical simulations are needed.

[0509] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0510] In this invention, the server includes means for inputting characteristic information of a group, means for estimating psychological states using emotion analysis technology, and means for simulating crowd movements based on group psychology. This makes it possible to analyze the emotional state of users in real time, practically predict evacuation behavior, and present the optimal evacuation route.

[0511] "Group characteristic information" refers to data including each individual's age group, gender composition, and areas of interest, and represents information that shows the overall trend of the group.

[0512] "Individual psychological state" refers to the state of an individual user that indicates their emotions and mental tendencies, and specifically includes emotional information obtained through voice recognition and facial expression analysis.

[0513] "Group psychology" refers to the overall psychological characteristics and tendencies of a crowd, obtained by aggregating the individual psychological states of the group members.

[0514] "Methods for simulating crowd movement" refer to technologies that virtually reproduce the behavior and movement patterns of crowds in specific situations based on generated group psychology.

[0515] Augmented reality technology is a technology that overlays virtual information onto the real world, enabling users to visually perceive digital information within the real world.

[0516] This system functions through the coordinated efforts of user, terminal, and server units. Users provide group characteristic information to the system using terminals. This characteristic information includes age group, gender composition, and areas of interest. Terminals are responsible for collecting this data and transmitting it to the server.

[0517] The server analyzes the data using a multimodal AI model based on the received feature information. The emotion engine plays a crucial role in this analysis process. The emotion engine utilizes speech recognition technology (e.g., APIs that convert voice input to text) and facial expression analysis technology (e.g., software that detects emotions from camera footage) to understand the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of each individual's psychological state.

[0518] The aggregated individual psychological states are then transformed into collective psychology using an algorithm. Based on this collective psychology, the server utilizes a generative AI model to simulate crowd behavior during a disaster. An example of a specific prompt is, "Analyze the anxiety levels of a group of men aged 25 to 35." This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0519] Finally, the simulation results are visualized to the user using augmented reality technology on the device. Augmented reality technology (e.g., through the use of AR software and display technology) overlays virtual evacuation routes onto the user's real space, enabling intuitive understanding. A system constructed in this way is extremely useful in the development and implementation of disaster prevention plans.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] Users input group characteristics information, including age group, gender composition, and areas of interest, using their devices. This input data is filled into the application form on the device, and the data is collected when the submit button is pressed.

[0523] Step 2:

[0524] The terminal sends the characteristic information entered by the user to the server. This transmission process uses encryption technology to ensure the data is securely transferred. The input data is sent here and passed to the server as output.

[0525] Step 3:

[0526] The server operates a multimodal AI model based on the received feature information. The input data is feature information, and the output is an estimated individual psychological state. This process employs data analysis techniques, and the AI ​​model identifies each user's psychological pattern by comparing it to the dataset.

[0527] Step 4:

[0528] The server uses an emotion engine to perform real-time speech recognition and facial expression analysis to understand the user's emotions. Input is speech and facial expression data, and output is emotional information. This utilizes speech recognition APIs and facial recognition software.

[0529] Step 5:

[0530] The server aggregates estimated individual psychological states and emotional information to generate group psychology. The input data consists of individual psychological states and emotional information, while the output is group psychology. An algorithm is used in this aggregation process to reveal the overall psychological tendencies of the group.

[0531] Step 6:

[0532] The server uses a generative AI model to simulate crowd behavior based on generated group psychology. The input is group psychology, and the output is the simulation result. Specifically, prompts are input into the model to obtain detailed insights into evacuation behavior and evacuation routes.

[0533] Step 7:

[0534] The device visualizes simulation results sent from the server to the user using augmented reality technology. The input is the simulation result, and the output is visual information superimposed on the real world. Specifically, virtual information is displayed in the user's real environment using AR technology.

[0535] (Application Example 2)

[0536] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] The objective of this invention is to provide safe and optimal evacuation routes that take into account the individual psychological and emotional states of users in spaces where diverse users gather. In particular, in emergency situations, rapid and accurate evacuation is necessary, and a system that reflects the psychology and behavior of the group in real time is required.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0539] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating the psychological state of individuals, and a device for generating group psychology. This makes it possible to analyze the emotional state of users in real time and provide customized evacuation routes based on that analysis.

[0540] "Group characteristic information" refers to basic information about a group, such as age group, gender composition, and areas of interest, which is necessary to estimate the psychological state of individuals within that group.

[0541] "Individual psychological state" refers to information that describes the unique psychological state of each individual within a group, and usually includes tendencies in emotions and thoughts.

[0542] "Group psychology" refers to information that represents the overall psychological tendencies and behavioral patterns of a group, obtained by aggregating the psychological states of individuals.

[0543] "Crowd movement simulation" is a process that virtually reproduces how crowds move or behave based on group psychology.

[0544] Augmented reality technology is a technology that overlays digital information onto real-world space, and typically uses smartphones or dedicated devices.

[0545] An "emotion recognition device" is a device that analyzes voice and facial expression data to identify an individual's emotional state in real time.

[0546] A "customized evacuation route" is a safe and effective evacuation route optimized based on group characteristics and individual psychological states.

[0547] The system for implementing this invention primarily aims to estimate the individual psychological states based on group characteristic information and aggregate them to generate group psychology. The system uses a smartphone or dedicated device as hardware, and utilizes TensorFlow, OpenCV, and Unity as software necessary for emotion recognition and data analysis.

[0548] Users input group characteristics such as age group, gender composition, and areas of interest through their devices. The emotion recognition device within the device collects voice and facial expression data in real time and analyzes individual emotional states. TensorFlow and OpenCV are used for this analysis, and the resulting emotional data is sent to a server.

[0549] The server generates collective psychology based on aggregated individual psychological states and uses that data to simulate crowd movement. The simulated evacuation routes are visualized in AR format on the user's device using Unity, thereby providing a customized evacuation route for each individual user.

[0550] For example, in a shopping mall, if an emergency suddenly occurs, the system can instantly analyze the emotional state of the users and visually guide them to a safe evacuation route. This allows users to avoid confusion and evacuate safely.

[0551] An example of a prompt for a generative AI model is: "Analyze the psychological state of groups, including families, in a shopping mall in real time and customize safe evacuation routes."

[0552] Thus, the present invention supports a rapid and safe evacuation process by providing an evacuation plan that reflects emotional states.

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] Users input characteristic information about a group, such as age group, gender composition, and areas of interest, using their device.

[0556] The entered information is formatted for transmission to the server after initial verification and data format conversion are performed on the terminal.

[0557] Step 2:

[0558] The device uses its camera and microphone to collect user voice and facial expression data.

[0559] The collected data is analyzed using OpenCV and TensorFlow to identify individual emotional states.

[0560] The results of the emotion recognition are output as a numerical emotion score and sent to the server.

[0561] Step 3:

[0562] The server receives group characteristic information and sentiment scores sent from the terminal.

[0563] Based on this information, a multimodal AI model is used to estimate the individual's psychological state.

[0564] The estimation results are stored as intermediate data.

[0565] Step 4:

[0566] The server aggregates intermediate data and performs a process to generate collective psychology.

[0567] Here, common psychological tendencies and patterns within a group are extracted and output as group psychology.

[0568] Step 5:

[0569] Based on the generated group psychology, the server performs a simulation of crowd movement.

[0570] The simulation results include recommended evacuation routes, and these scenarios are developed by a generative AI model.

[0571] Step 6:

[0572] The server converts the simulation results into data for visualization using augmented reality technology and sends it to the terminal.

[0573] On the device, Unity is used to display the evacuation route on the user's screen, overlaid on the real world.

[0574] This series of processes enables users to receive safe and effective evacuation instructions tailored to their emotional state in real time.

[0575] 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.

[0576] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0577] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0578] [Fourth Embodiment]

[0579] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0580] 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.

[0581] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0582] 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.

[0583] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0584] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0585] 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.

[0586] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0587] 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.

[0588] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0589] The 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.

[0590] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0591] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0592] In embodiments of this invention, a system is constructed in which the user, terminal, and server components function in coordination. This invention envisions a specific implementation of a group psychology simulation system for disaster prevention purposes.

[0593] Users input characteristic information about a group using their devices. This collected data is then sent to a server. Based on the received data, the server utilizes a multimodal AI model to estimate each individual's psychological state. This psychological state is then scored using specific indicators such as anxiety, calmness, and panic.

[0594] Next, the server aggregates the individual psychological states to form a collective psychology that represents the overall psychology of the group. Using this formed collective psychology, the server simulates crowd movement. This simulation is conducted to predict what behavioral patterns will emerge during a disaster, analyzing evacuation route selection and crowd flow.

[0595] The simulation results are transmitted to the terminal and presented to the user through AR glasses using augmented reality technology. This allows the user to experience the movement of crowds in the real world in a real-time, visualized form. For example, if a facility conducts an earthquake drill, this system can be used to simulate visitor evacuation routes and areas where congestion may occur, enabling the development of an appropriate evacuation plan. In this way, this system contributes to improving the accuracy of actual disaster prevention drills and policy considerations.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] Users use a terminal to input characteristic information about a group. This information includes age group, gender composition, and areas of interest. The terminal organizes this information into a database format and prepares it for transmission to the server.

[0599] Step 2:

[0600] The terminal sends characteristic information about the group to the server. The server verifies the integrity of the received data and stores it in a database. This allows the data to be quickly accessed in subsequent processing steps.

[0601] Step 3:

[0602] The server activates a multimodal AI model and estimates each individual's psychological state based on stored feature information. The estimation results are generated as numerical values ​​such as anxiety level, panic level, and calmness level.

[0603] Step 4:

[0604] The server aggregates individual psychological states to form a collective psychology. This collective psychology is then integrated into a score that represents overall anxiety and calmness.

[0605] Step 5:

[0606] The server uses a generated AI model to simulate crowd movements during disasters based on collective psychology. The simulation evaluates evacuation routes, congestion predictions, and the likelihood of panic.

[0607] Step 6:

[0608] The server formats the data to visualize the simulation results and prepares it for transmission to the terminal.

[0609] Step 7:

[0610] The device receives visualization data from the server and sends it to the AR glasses. Through the AR glasses, the user can visualize the simulation results overlaid on the real world and observe the movement of the crowd.

[0611] (Example 1)

[0612] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] It is difficult to grasp changes in the psychological state and behavioral patterns of a group in real time and to formulate an effective evacuation plan during a disaster. In particular, there are technical challenges in accurately assessing the psychological state of the entire group and presenting simulation results based on that assessment in a realistic manner in order to achieve safe and efficient evacuation.

[0614] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0615] In this invention, the server includes means for inputting characteristic information, means for estimating psychological states, and means for generating psychological states. This makes it possible to formulate more accurate evacuation plans through the estimation of the psychological state of a group and the simulation of crowd movements.

[0616] "Characteristic information" refers to information about the attributes and characteristics of individuals who make up a group, including age, gender, and areas of interest.

[0617] "Psychological state" refers to the mental and emotional state of an individual or group, and indicators such as anxiety level, calmness level, and panic level are used.

[0618] "Psychology" refers to the overall mental state aggregated from the individual psychological states, and is useful for predicting the average emotions and behaviors of a group.

[0619] "Movement simulation" is a process that predicts what behavioral patterns and movement paths will be taken under specific conditions, based on group psychology.

[0620] "Display technology" refers to technologies that use methods such as augmented reality to overlay digital information onto the real world.

[0621] Users input characteristic information about a specific group through a terminal. This terminal has a dedicated application installed, allowing users to easily register data such as age group, gender, and areas of interest.

[0622] The terminal sends the input information to the server. The server has high parallel processing capabilities and, after receiving the data, uses a multimodal AI model to estimate the psychological state. In this process, AI software such as TensorFlow and PyTorch operates, scoring each individual's level of anxiety, calmness, and panic. This allows for a quantitative evaluation of each individual's psychological state.

[0623] The server aggregates the psychological states of each individual to form the overall psychology of the group. Then, based on this collective psychology, it simulates crowd behavior. The simulation is performed to predict crowd behavior during disasters and runs in a real-world environment using Unity or other simulation engines.

[0624] Finally, the device receives the simulation results and presents them to the user using augmented reality technology. By wearing AR glasses, the user can visually experience the simulated crowd movements and evacuation routes overlaid on the real world.

[0625] For example, if a facility conducts a disaster prevention drill simulating an earthquake, this system can be used to simulate evacuation routes for visitors and areas where people tend to gather, allowing for the development of an appropriate evacuation plan in advance. Another possible prompt for the generating AI model is, "Simulate the crowd psychology of 100 students in their 20s during an earthquake and provide recommended evacuation routes."

[0626] In this way, this system contributes to ensuring safety and facilitating efficient evacuation during disasters by understanding and applying group psychology.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] Users use their devices to input characteristic information about a group. This information includes age group and gender. The application on the device collects this data and sends it to the server for the next processing step.

[0630] Step 2:

[0631] The device transmits the collected characteristic information to the server. The data is securely transmitted to the server via the internet, and this input data becomes the basic data used by the server for estimating the user's psychological state.

[0632] Step 3:

[0633] The server receives feature information and uses a multimodal AI model to estimate each individual's psychological state. Based on the input feature information, the AI ​​model calculates psychological indicators such as anxiety, calmness, and panic. The AI ​​model is executed using TensorFlow or PyTorch, and as a result, each individual's psychological state score is output.

[0634] Step 4:

[0635] The server aggregates the calculated individual psychological state scores to generate the overall group psychology. By summing and averaging each psychological state, the group's psychological state is formed. This output provides the foundation for analyzing the group's behavioral tendencies.

[0636] Step 5:

[0637] The server simulates crowd movement based on collective psychology. Using simulation engines such as Unity, it realistically reproduces evacuation routes and crowd flow. This simulation allows for the acquisition of specific behavioral prediction patterns.

[0638] Step 6:

[0639] The server sends the simulation results to the terminal. This output is the data needed to be presented to the user.

[0640] Step 7:

[0641] The device displays the received simulation results to the user via AR glasses. The user can visually confirm the movement of the crowd superimposed on the real world and optimize their evacuation actions. In this process, the user can edit and modify their actions in real time based on the predicted behavioral patterns.

[0642] (Application Example 1)

[0643] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] In places where crowds gather, predicting emergency evacuation procedures and congestion levels is crucial. However, conventional methods have made it difficult to grasp changes in group psychology in real time and take immediate action, thus hindering effective crowd management and disaster prevention measures.

[0645] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0646] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating individual psychological states, a device for generating group psychology, a device for simulating crowd movement, a visualization device utilizing augmented reality technology, and a device for presenting real-time crowd management support information. This enables real-time prediction and visualization of crowd evacuation behavior and congestion in emergencies, allowing for a rapid and appropriate response.

[0647] A "device for inputting characteristic information of a group" is a device that allows the constituent elements and characteristics of a group to be input as data.

[0648] A "device for estimating individual psychological states" is a device that analyzes each individual's psychological response based on collected characteristic information.

[0649] A "device for generating group psychology" is a device that aggregates estimated individual psychological states to form an overall psychological trend.

[0650] A "device that simulates crowd movement" is a device that virtually reproduces crowd behavior patterns based on group psychology.

[0651] A "visualization device utilizing augmented reality technology" is a device that uses technology to overlay virtual information onto the real world and present it visually.

[0652] A "device that provides real-time crowd management support information" is a device that provides information necessary for group management immediately based on simulation results.

[0653] In the system that implements this application, each component works in a coordinated manner to predict and visualize crowd movement. The server first receives group characteristic information input from the user and then performs calculations to estimate the psychological state of each individual based on that information. A multimodal AI model is used for estimation to generate psychological state data for each individual.

[0654] Next, the server aggregates this individual psychological data to form data representing the overall psychology of the group. Based on this group psychology data, it simulates how the crowd's behavior will change. Simulation software and algorithms are used for the simulation.

[0655] The results of this simulation are provided to the user through visualization devices utilizing augmented reality technology. For example, smart glasses or head-mounted displays can serve this purpose, displaying crowd movement and congestion levels in real time, superimposed onto the real space. This makes it easier for disaster management and security personnel to appropriately control crowd flow.

[0656] A concrete example of its use is in large-scale music events. By collecting participant characteristic information, analyzing group psychology, and visualizing areas where congestion is expected, safe operation can be supported. This system functions effectively when prompted with the string, "Based on participant characteristic data, simulate safe evacuation routes in the event of an earthquake."

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] Users use a device to input characteristic information about a group. This input information includes age group, gender composition, and areas of interest. The device collects this information as digital data and sends it to the server.

[0660] Step 2:

[0661] The server estimates an individual's psychological state based on the received characteristic information. Using a multimodal AI model, it calculates individual psychological indicators such as anxiety, calmness, and panic. By analyzing the input data and quantifying the psychological state, it generates psychological data for each individual.

[0662] Step 3:

[0663] The server aggregates the generated individual psychological state data to form a collective psychology. It statistically processes each individual's psychological indicators as a set and outputs data that shows the overall psychological trend of the group.

[0664] Step 4:

[0665] The server simulates crowd movement based on collective psychology data. Using simulation software, it visually reproduces how a crowd moves based on the input psychology data.

[0666] Step 5:

[0667] The simulation results are transmitted to a visualization device using augmented reality technology. For example, smart glasses act as a receiver, overlaying the results onto the real world. This allows users to see crowd movement patterns and changes in movement in real time.

[0668] Step 6:

[0669] The server provides crowd management support information using simulation results that are updated in real time. This allows users to obtain the information they need at the right time and take appropriate countermeasures.

[0670] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0671] The present invention will now describe an embodiment of the system. This system consists of a user, a terminal, and a server unit that work together, and also incorporates an emotion engine that recognizes the user's emotions.

[0672] Users provide group characteristic information to the system via their devices. This characteristic information includes age group, gender composition, and areas of interest. The devices send this data to a server. The server uses a multimodal AI model to analyze the input characteristic information and estimate the individual's psychological state.

[0673] In this estimation process, the emotion engine plays a crucial role. The emotion engine utilizes speech recognition and facial expression analysis technologies to grasp the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of the individual's psychological state.

[0674] The server aggregates the psychological states of all individuals and generates collective psychology. Based on this generated collective psychology, the server simulates crowd movements during a disaster. This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0675] The simulation results are visualized to the user using augmented reality technology on the terminal. As a concrete example, consider an earthquake drill in a shopping mall. Using this system, facility managers can visualize the emotional state and psychological tendencies of visitors, enabling them to develop effective evacuation protocols in the event of an actual disaster. Thus, this system plays a crucial role in the formulation and implementation of disaster prevention plans.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] Users input group characteristics information using a terminal. This characteristics include age group, gender composition, and areas of interest. The terminal formats the input data, creating a format that the server can receive.

[0679] Step 2:

[0680] The terminal sends the formatted characteristic information of the group to the server. The server verifies the consistency of the received data and stores it in the appropriate database.

[0681] Step 3:

[0682] The server uses an emotion engine to analyze the user's emotions from their voice and video feeds. This analysis is achieved using speech recognition and facial expression analysis technologies.

[0683] Step 4:

[0684] The server integrates emotional information acquired by the emotion engine into a multimodal AI model to estimate individual psychological states. It then generates numerical scores representing each user's level of anxiety and calmness.

[0685] Step 5:

[0686] The server aggregates individual psychological states to form a collective psychology. This collective psychology is considered to reflect the behavioral tendencies of the entire group.

[0687] Step 6:

[0688] The server uses a generated AI model to simulate crowd movement based on collective psychology. The results are obtained by considering factors such as evacuation routes, congestion predictions, and crowd flow patterns.

[0689] Step 7:

[0690] The server formats the simulation results into a visually usable format and sends them to the terminal.

[0691] Step 8:

[0692] The device displays the simulation results received from the server on the AR glasses. Users can then see the crowd movements visualized overlaid on the real world, gaining a more immersive experience.

[0693] (Example 2)

[0694] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] There is a challenge in accurately understanding crowd movements and emotional states, and in more accurately predicting evacuation behavior during emergencies. In particular, formulating evacuation plans that take emotions into account is not easy, so realistic and practical simulations are needed.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0697] In this invention, the server includes means for inputting characteristic information of a group, means for estimating psychological states using emotion analysis technology, and means for simulating crowd movements based on group psychology. This makes it possible to analyze the emotional state of users in real time, practically predict evacuation behavior, and present the optimal evacuation route.

[0698] "Group characteristic information" refers to data including each individual's age group, gender composition, and areas of interest, and represents information that shows the overall trend of the group.

[0699] "Individual psychological state" refers to the state of an individual user that indicates their emotions and mental tendencies, and specifically includes emotional information obtained through voice recognition and facial expression analysis.

[0700] "Group psychology" refers to the overall psychological characteristics and tendencies of a crowd, obtained by aggregating the individual psychological states of the group members.

[0701] "Methods for simulating crowd movement" refer to technologies that virtually reproduce the behavior and movement patterns of crowds in specific situations based on generated group psychology.

[0702] Augmented reality technology is a technology that overlays virtual information onto the real world, enabling users to visually perceive digital information within the real world.

[0703] This system functions through the coordinated efforts of user, terminal, and server units. Users provide group characteristic information to the system using terminals. This characteristic information includes age group, gender composition, and areas of interest. Terminals are responsible for collecting this data and transmitting it to the server.

[0704] The server analyzes the data using a multimodal AI model based on the received feature information. The emotion engine plays a crucial role in this analysis process. The emotion engine utilizes speech recognition technology (e.g., APIs that convert voice input to text) and facial expression analysis technology (e.g., software that detects emotions from camera footage) to understand the user's emotions in real time. This allows emotional information, such as anxiety and feelings of security, to be incorporated into the estimation of each individual's psychological state.

[0705] The aggregated individual psychological states are then transformed into collective psychology using an algorithm. Based on this collective psychology, the server utilizes a generative AI model to simulate crowd behavior during a disaster. An example of a specific prompt is, "Analyze the anxiety levels of a group of men aged 25 to 35." This simulation provides detailed insights into evacuation behavior and the selection of evacuation routes.

[0706] Finally, the simulation results are visualized to the user using augmented reality technology on the device. Augmented reality technology (e.g., through the use of AR software and display technology) overlays virtual evacuation routes onto the user's real space, enabling intuitive understanding. A system constructed in this way is extremely useful in the development and implementation of disaster prevention plans.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] Users input group characteristics information, including age group, gender composition, and areas of interest, using their devices. This input data is filled into the application form on the device, and the data is collected when the submit button is pressed.

[0710] Step 2:

[0711] The terminal sends the characteristic information entered by the user to the server. This transmission process uses encryption technology to ensure the data is securely transferred. The input data is sent here and passed to the server as output.

[0712] Step 3:

[0713] The server operates a multimodal AI model based on the received feature information. The input data is feature information, and the output is an estimated individual psychological state. This process employs data analysis techniques, and the AI ​​model identifies each user's psychological pattern by comparing it to the dataset.

[0714] Step 4:

[0715] The server uses an emotion engine to perform real-time speech recognition and facial expression analysis to understand the user's emotions. Input is speech and facial expression data, and output is emotional information. This utilizes speech recognition APIs and facial recognition software.

[0716] Step 5:

[0717] The server aggregates estimated individual psychological states and emotional information to generate group psychology. The input data consists of individual psychological states and emotional information, while the output is group psychology. An algorithm is used in this aggregation process to reveal the overall psychological tendencies of the group.

[0718] Step 6:

[0719] The server uses a generative AI model to simulate crowd behavior based on generated group psychology. The input is group psychology, and the output is the simulation result. Specifically, prompts are input into the model to obtain detailed insights into evacuation behavior and evacuation routes.

[0720] Step 7:

[0721] The device visualizes simulation results sent from the server to the user using augmented reality technology. The input is the simulation result, and the output is visual information superimposed on the real world. Specifically, virtual information is displayed in the user's real environment using AR technology.

[0722] (Application Example 2)

[0723] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] The objective of this invention is to provide safe and optimal evacuation routes that take into account the individual psychological and emotional states of users in spaces where diverse users gather. In particular, in emergency situations, rapid and accurate evacuation is necessary, and a system that reflects the psychology and behavior of the group in real time is required.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0726] In this invention, the server includes a device for inputting characteristic information of a group, a device for estimating the psychological state of individuals, and a device for generating group psychology. This makes it possible to analyze the emotional state of users in real time and provide customized evacuation routes based on that analysis.

[0727] "Group characteristic information" refers to basic information about a group, such as age group, gender composition, and areas of interest, which is necessary to estimate the psychological state of individuals within that group.

[0728] "Individual psychological state" refers to information that describes the unique psychological state of each individual within a group, and usually includes tendencies in emotions and thoughts.

[0729] "Group psychology" refers to information that represents the overall psychological tendencies and behavioral patterns of a group, obtained by aggregating the psychological states of individuals.

[0730] "Crowd movement simulation" is a process that virtually reproduces how crowds move or behave based on group psychology.

[0731] Augmented reality technology is a technology that overlays digital information onto real-world space, and typically uses smartphones or dedicated devices.

[0732] An "emotion recognition device" is a device that analyzes voice and facial expression data to identify an individual's emotional state in real time.

[0733] A "customized evacuation route" is a safe and effective evacuation route optimized based on group characteristics and individual psychological states.

[0734] The system for implementing this invention primarily aims to estimate the individual psychological states based on group characteristic information and aggregate them to generate group psychology. The system uses a smartphone or dedicated device as hardware, and utilizes TensorFlow, OpenCV, and Unity as software necessary for emotion recognition and data analysis.

[0735] Users input group characteristics such as age group, gender composition, and areas of interest through their devices. The emotion recognition device within the device collects voice and facial expression data in real time and analyzes individual emotional states. TensorFlow and OpenCV are used for this analysis, and the resulting emotional data is sent to a server.

[0736] The server generates collective psychology based on aggregated individual psychological states and uses that data to simulate crowd movement. The simulated evacuation routes are visualized in AR format on the user's device using Unity, thereby providing a customized evacuation route for each individual user.

[0737] For example, in a shopping mall, if an emergency suddenly occurs, the system can instantly analyze the emotional state of the users and visually guide them to a safe evacuation route. This allows users to avoid confusion and evacuate safely.

[0738] An example of a prompt for a generative AI model is: "Analyze the psychological state of groups, including families, in a shopping mall in real time and customize safe evacuation routes."

[0739] Thus, the present invention supports a rapid and safe evacuation process by providing an evacuation plan that reflects emotional states.

[0740] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0741] Step 1:

[0742] Users input characteristic information about a group, such as age group, gender composition, and areas of interest, using their device.

[0743] The entered information is formatted for transmission to the server after initial verification and data format conversion are performed on the terminal.

[0744] Step 2:

[0745] The device uses its camera and microphone to collect user voice and facial expression data.

[0746] The collected data is analyzed using OpenCV and TensorFlow to identify individual emotional states.

[0747] The results of the emotion recognition are output as a numerical emotion score and sent to the server.

[0748] Step 3:

[0749] The server receives group characteristic information and sentiment scores sent from the terminal.

[0750] Based on this information, a multimodal AI model is used to estimate the individual's psychological state.

[0751] The estimation results are stored as intermediate data.

[0752] Step 4:

[0753] The server aggregates intermediate data and performs a process to generate collective psychology.

[0754] Here, common psychological tendencies and patterns within a group are extracted and output as group psychology.

[0755] Step 5:

[0756] Based on the generated group psychology, the server performs a simulation of crowd movement.

[0757] The simulation results include recommended evacuation routes, and these scenarios are developed by a generative AI model.

[0758] Step 6:

[0759] The server converts the simulation results into data for visualization using augmented reality technology and sends it to the terminal.

[0760] On the device, Unity is used to display the evacuation route on the user's screen, overlaid on the real world.

[0761] This series of processes enables users to receive safe and effective evacuation instructions tailored to their emotional state in real time.

[0762] 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.

[0763] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0765] 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.

[0766] Figure 9 shows an 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.

[0767] 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.

[0768] 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.

[0769] 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, motorcycles, etc., 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, for example, based 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.

[0770] 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."

[0771] 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.

[0772] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0773] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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 the like 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.

[0782] 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 as being incorporated by reference.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] A means for inputting characteristic information of a group,

[0786] A means for estimating an individual's psychological state based on the aforementioned characteristic information,

[0787] A means for generating group psychology by aggregating the aforementioned individual psychological states,

[0788] A means for simulating crowd behavior based on the aforementioned group psychology,

[0789] A display means using augmented reality technology to overlay the aforementioned simulation results onto real space,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, wherein the characteristic information includes age group, gender composition, and areas of interest.

[0793] (Claim 3)

[0794] The system according to claim 1, characterized in that the simulation results predict the evacuation behavior of the crowd.

[0795] "Example 1"

[0796] (Claim 1)

[0797] A means of inputting characteristic information,

[0798] A means for estimating a psychological state based on the aforementioned characteristic information,

[0799] A means for generating psychology by aggregating the aforementioned psychological states,

[0800] A means of simulating movement based on the aforementioned psychology,

[0801] A display means that uses a technique to overlay the aforementioned simulation results onto space,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, wherein the characteristic information includes attribute layers, configuration, and areas of interest.

[0805] (Claim 3)

[0806] The system according to claim 1, characterized in that the simulation results predict action.

[0807] "Application Example 1"

[0808] (Claim 1)

[0809] A device for inputting characteristic information of a group,

[0810] A device for estimating an individual's psychological state based on the aforementioned characteristic information,

[0811] A device that aggregates the aforementioned individual psychological states to generate group psychology,

[0812] A device that simulates crowd behavior based on the aforementioned group psychology,

[0813] A visualization device that utilizes augmented reality technology to overlay the aforementioned simulation results onto real space,

[0814] A device that uses the aforementioned visualization device to display crowd management support information in real time,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, wherein the characteristic information includes age group, gender composition, and areas of interest.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the simulation results predict crowd evacuation behavior and congestion levels.

[0820] "Example 2 of combining an emotion engine"

[0821] (Claim 1)

[0822] A means of inputting characteristic information of a group,

[0823] A means for estimating an individual's psychological state based on the aforementioned characteristic information,

[0824] A means for analyzing emotions using speech recognition technology and facial expression analysis technology,

[0825] A means for generating group psychology by aggregating the aforementioned individual psychological states,

[0826] A means for simulating crowd behavior based on the aforementioned group psychology,

[0827] A display means using augmented reality technology to overlay the aforementioned simulation results onto real space,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, characterized in that the aforementioned characteristic information includes age group, gender composition, and areas of interest, and takes into account the results of voice and facial expression analysis.

[0831] (Claim 3)

[0832] The system according to claim 1, characterized in that the simulation results predict crowd evacuation behavior and optimize evacuation routes.

[0833] "Application example 2 when combining with an emotional engine"

[0834] (Claim 1)

[0835] A device for inputting characteristic information of a group,

[0836] A device for estimating an individual's psychological state based on the aforementioned characteristic information,

[0837] A device that aggregates the aforementioned individual psychological states to generate group psychology,

[0838] A device that simulates crowd behavior based on the aforementioned group psychology,

[0839] A display device using augmented reality technology that overlays the aforementioned simulation results onto real space,

[0840] The aforementioned system includes a device that provides a safe evacuation route based on the user's emotional state,

[0841] An emotion recognition device that analyzes the aforementioned emotional state in real time,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the characteristic information includes age group, gender composition, and areas of interest.

[0845] (Claim 3)

[0846] The system according to claim 1, characterized in that the system provides a user with a customized evacuation route using augmented reality technology. [Explanation of Symbols]

[0847] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting characteristic information of a group, A means for estimating an individual's psychological state based on the aforementioned characteristic information, A means for generating group psychology by aggregating the aforementioned individual psychological states, A means for simulating crowd behavior based on the aforementioned group psychology, A display means using augmented reality technology to overlay the aforementioned simulation results onto real space, A system that includes this.

2. The system according to claim 1, wherein the characteristic information includes age group, gender composition, and areas of interest.

3. The system according to claim 1, characterized in that the simulation results predict the evacuation behavior of the crowd.

Images