system

Abstract

We provide the system. [Solution] Means of collecting environmental data, Methods for analyzing medical data, A means of identifying the needs of evacuees using generative artificial intelligence, Means for optimizing relief supplies based on identified needs, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional shelters, it has been difficult to accurately grasp the different needs and health conditions of each evacuee, and as a result, there has been a problem that appropriate relief supplies do not reach quickly. Also, when the distribution of supplies is not properly carried out, there is a problem that the quality of life of evacuees deteriorates and further stress is imposed. In order to improve such a situation, it is required to identify needs in real time and quickly provide individualized support.

Means for Solving the Problems

[0005] The present invention is a system comprising means for collecting environmental data, means for analyzing medical data, and means for identifying the needs of evacuees using generative artificial intelligence. This system includes means for optimizing relief supplies based on identified needs, thereby accurately determining the needs of each evacuee and ensuring the provision of appropriate supplies. Furthermore, by incorporating functions for monitoring the situation at evacuation centers using image recognition technology and functions for directly collecting requests from users through a chatbot, it is possible to formulate and implement more detailed and rapid support plans.

[0006] "Environmental data" refers to information about the physical environment of an evacuation center, such as temperature, humidity, air pressure, and noise levels.

[0007] "Medical data" refers to information about the health status of evacuees, and is the data necessary to assess the physical condition and health risks of each individual evacuee.

[0008] "Generative artificial intelligence" refers to artificial intelligence technology that uses collected data to analyze the needs and health status of evacuees and generates plans to provide optimal support.

[0009] "Needs" refers to the supplies, services, or other support that evacuees currently require.

[0010] Optimizing "relief supplies" means adjusting the types and quantities of necessary supplies based on the actual needs of evacuees, aiming for efficient and effective provision.

[0011] "Image recognition technology" refers to technology that analyzes video data acquired using cameras and sensors to understand the situation inside evacuation shelters.

[0012] A "chatbot" is a program designed to collect user requests and information and engage in dialogue; it refers to an automated response system capable of natural language communication. [Brief explanation of the drawing]

[0013] [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]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by a processor.

[0018] In the following embodiments, a numbered 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 (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for understanding the diverse needs and health conditions of evacuees in evacuation shelters in real time and providing optimal relief supplies. In one embodiment, it is necessary to first place multiple sensors and cameras within the evacuation shelter. These sensors continuously acquire environmental data such as temperature, humidity, atmospheric pressure, and noise, and collect this information on a terminal. The cameras analyze the behavior and facial expressions of evacuees using image recognition technology.

[0035] The terminals collect this data, compress it periodically, and send it to the server. The server analyzes the received data and uses artificial intelligence to individually identify the needs of the evacuees. For example, if the server detects an abnormal temperature change, it will prioritize providing warm clothing to the evacuees. By analyzing medical data, it can also quickly determine if a particular evacuee is requesting medication for a chronic illness.

[0036] Users can interact with a chatbot via a terminal and directly communicate their needs. This information is collected on a server and used to facilitate real-time decision-making. For example, if a user enters "I need diapers," that information is immediately reflected in the list of available supplies, and if it's a high priority, it will be addressed immediately.

[0037] Furthermore, based on the analysis results, the server creates a list of necessary relief supplies. This list is sent to the relief team, and an optimal relief schedule is developed, taking priorities into consideration. For example, if it is determined that there is a food shortage in a certain area, the server immediately issues instructions to procure the required amount of food and deliver it quickly to the shelters.

[0038] In this way, the system efficiently identifies the individual needs of evacuees and provides prompt and accurate support. This improves the quality of life for evacuees in shelters and facilitates their adaptation to the environment.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires environmental data from sensors installed in the evacuation center. This includes information such as temperature, humidity, atmospheric pressure, and noise levels. It also uses a camera to capture images of evacuees' behavior and facial expressions, and collects that image data.

[0042] Step 2:

[0043] The terminal compresses the collected environmental and image data and prepares it for transmission to the server. The compressed data packets are sent to the server over the network.

[0044] Step 3:

[0045] The server analyzes the data received from the terminals. First, it analyzes environmental data and performs threshold checks to detect anomalies within the evacuation center. Next, it performs image analysis and uses AI to infer the psychological and health status of evacuees from their facial expressions and behavior.

[0046] Step 4:

[0047] The server uses natural language processing technology to analyze requests collected directly from users through the chatbot, classifies the needs based on this analysis, and evaluates their importance.

[0048] Step 5:

[0049] Based on the analysis results, the server identifies the needs of each evacuee and generates a list of appropriate relief supplies. This list includes the type, quantity, and priority of the supplies to be provided.

[0050] Step 6:

[0051] The server sends the generated list of relief supplies to the relief team, who then develop a detailed relief plan. This ensures that the supplies are properly arranged and delivered quickly to their designated destinations.

[0052] Step 7:

[0053] Users provide feedback on the donated supplies to the server via a chatbot, reporting their satisfaction with the supplies and any additional requests. This feedback will be incorporated into future aid plans.

[0054] Step 8:

[0055] The terminal continuously collects environmental data and retransmits new data if the situation at the evacuation center changes. The server uses this data to reassess needs and consider providing additional relief supplies.

[0056] (Example 1)

[0057] 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."

[0058] In evacuation shelters, it is essential to quickly understand the individual needs and health conditions of evacuees and provide the most appropriate relief supplies based on that information. However, conventional methods make it difficult to efficiently collect and analyze this information, making it challenging to provide timely and appropriate support.

[0059] 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.

[0060] In this invention, the server includes means for acquiring environmental information, means for analyzing human health conditions, and means for identifying the needs of evacuees using a generative model. This enables comprehensive collection and analysis of information in evacuation shelters, and makes it possible to provide relief supplies quickly and accurately to individual evacuees.

[0061] "Environmental information" refers to data that indicates physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise levels.

[0062] "Analysis of human health status" is the process of analyzing information about the health of evacuees and identifying the necessary medical care and supplies.

[0063] A "generative model" is a system that uses machine learning algorithms based on collected data to predict and identify the needs of evacuees.

[0064] "Relief supplies" refers to essential items such as food, clothing, and medicine provided to evacuees at evacuation centers.

[0065] "Image processing technology" is a technique for analyzing images acquired from cameras and other visual devices to determine a situation.

[0066] "Interactive software" is a program that enables interaction with the user and collects specific requests or information.

[0067] "Data compression" is a technology that reduces the amount of data while preserving information, thereby enabling more efficient communication.

[0068] An "information processing device" is a computer system that receives and analyzes large amounts of data and creates a list of necessary relief supplies.

[0069] One embodiment of this invention is a system for quickly identifying the needs of evacuees in shelters and providing optimal relief supplies. This system uses multiple hardware and software components to perform comprehensive information gathering and analysis.

[0070] First, the terminal acquires environmental information in real time using various sensors (temperature, humidity, atmospheric pressure, noise sensors, etc.) installed within the evacuation center. This data is collected using compact computer devices such as Raspberry Pi. In addition, it uses cameras to analyze the behavior and facial expressions of evacuees using image processing technology and collects that data as well.

[0071] Next, the terminal compresses the data and sends all collected information to a central server via a secure communication protocol (e.g., TLS). This compression process improves communication efficiency while ensuring data reliability.

[0072] Based on the received data, the server utilizes a generative AI model to identify the health status and individual needs of evacuees. The generative AI model uses machine learning algorithms to detect unusual environmental changes and individual medical needs, and lists priority relief supplies. In this process, the server has mechanisms in place to quickly detect extreme weather and health risks, and to facilitate necessary support actions.

[0073] Furthermore, users can directly input their needs through interactive software (chatbots) installed in evacuation centers. These chatbots use natural language processing to analyze the user's text input and send it to the server. For example, a specific prompt might be, "We are running low on water, so please replenish it," and that information will be reflected in the support list.

[0074] This allows the system to comprehensively understand the needs of evacuees in real time and provide appropriate relief supplies. This, in turn, can improve the quality of life in evacuation shelters and facilitate smoother adaptation to the new environment.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The terminal acquires environmental information through sensors installed within the evacuation center. The input for this step consists of direct data such as temperature, humidity, atmospheric pressure, and noise. After acquiring this information, the terminal reads the raw data from the sensors and formats it as time-series data. This formatted data is necessary for subsequent analysis.

[0078] Step 2:

[0079] The terminal uses a camera to monitor the evacuees and acquire image data. The input is still images or video data capturing the evacuees' actions and facial expressions. The terminal incorporates image recognition technology to analyze the acquired image data in real time and detect abnormal behavior or emergencies among the evacuees. The output of the analysis results is a status tag for the identified evacuees.

[0080] Step 3:

[0081] The terminal compresses the collected environmental and image data into a single data packet. The input consists of the format data and analysis results obtained in steps 1 and 2. The terminal uses a data compression algorithm to reduce the amount of data. The compressed data is then ready to be sent to the server.

[0082] Step 4:

[0083] The terminal sends compressed data packets to the server via a secure communication protocol. The input is a compressed data packet. This data is sent to the server using encryption technology such as TLS. Once the server confirms receipt of the data, a successful transmission is indicated as output.

[0084] Step 5:

[0085] The server decompresses the compressed data received from the terminal and begins analysis. The input is the transmitted data packets. The server uses a generating AI model to detect abnormalities in the health status and environment of the evacuees and identify items that should be prioritized for support. The output is a needs list based on the condition of each evacuee.

[0086] Step 6:

[0087] Users input their requests through interactive software installed at the evacuation center. This input is text-based information about the user's needs. The chatbot analyzes this information and immediately sends it to the server. As a result of the analysis, the user's request is added to the support list and reflected in the system.

[0088] Step 7:

[0089] The server creates a list of relief supplies and issues instructions to the support team based on all analysis results and user requests. Inputs are analysis results and user request information. The server determines the types, quantities, and priorities of necessary relief supplies and sends this information to the support team. The output is a specific and optimized list of relief supplies.

[0090] (Application Example 1)

[0091] 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."

[0092] There is a challenge in providing individuals with diverse needs with the most suitable information and resources in real time, leading to decreased satisfaction and difficulties in providing efficient services. In particular, a system capable of rapid and accurate responses is needed in situations where individualized responses tailored to the environment and behavior are required.

[0093] 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.

[0094] In this invention, the server includes means for collecting environmental information, means for analyzing personal health information, and means for identifying people's requests using generated artificial intelligence. This makes it possible to provide personalized product information and goods.

[0095] "Environmental information" refers to data that measures the surrounding physical conditions, such as temperature, humidity, atmospheric pressure, and noise.

[0096] "Personal health information" refers to data that indicates an individual's physical condition and health status, such as body temperature, heart rate, and exercise level.

[0097] Artificial intelligence is a computational model used to analyze vast amounts of data and predict specific patterns or needs.

[0098] "Means of identifying needs" refers to methods of analyzing collected data to identify the products and services that individuals desire.

[0099] "Means of optimizing resources" refers to methods for efficiently allocating and providing necessary resources based on identified needs.

[0100] "Image recognition technology" is a technology that uses cameras to analyze people's actions and facial expressions and extracts information based on that analysis.

[0101] A "dialogue mechanism" is an interface that receives information from the user and generates a response for the system.

[0102] To implement this invention, it is first necessary to place multiple sensors and cameras within the store. These sensors continuously acquire environmental information such as temperature, humidity, and location, while the cameras analyze the behavior and facial expressions of customers using image recognition technology. A server collects this data, analyzes it using artificial intelligence, and identifies the individual needs of each customer. Based on the analysis results, it notifies each customer of product information and services optimized for them through their device. A smartphone application is used for notifications, allowing customers to receive customized information in real time.

[0103] Specifically, the server manages data using AWS (registered trademark) cloud services and operates AI models using TENSORFLOW (registered trademark). A mobile app developed with React Native is used as the terminal, allowing customers to obtain information via their smartphones within the store. For example, if a customer spends some time looking at a particular product, a camera captures their actions, the server analyzes information related to that product, and notifies the smartphone. This allows customers to obtain detailed information about products they are interested in, increasing their purchasing intent.

[0104] An example of a prompt for the generating AI model is: "Identify customer interest in products based on their behavior and facial expressions in the store, and generate personalized app suggestions." Based on this prompt, the AI ​​performs analysis to provide the most relevant information to the customer.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] Sensors and cameras are activated to collect environmental information within the store and data on customer behavior and facial expressions in real time. The sensors measure temperature and humidity, and the cameras acquire images. The acquired data is transmitted to a server via the network. The input includes environmental data and camera footage, and the output is transmitted to the server.

[0108] Step 2:

[0109] The server analyzes the environmental information and image data it receives. Using a generative AI model, it analyzes the customer's behavior patterns and facial expression changes from the image data to identify products that the customer may be interested in. AI frameworks such as TensorFlow are utilized in this process. The input is the data acquired in step 1, and the output is the identified products of interest and related information.

[0110] Step 3:

[0111] The server generates individual prompt messages based on the analysis results. These prompt messages contain instructions to provide information about products that the customer is deemed interested in. These prompt messages are sent to the terminal. The input is the analysis results obtained in step 2, and the output is the prompt messages.

[0112] Step 4:

[0113] The terminal processes the prompt message received from the server and notifies the customer via a smartphone application. Specific product information and related coupons are displayed as push notifications. Users receive this information in real time. The input is the prompt message, and the output is the notification content sent to the user.

[0114] Step 5:

[0115] The user receives a notification and can view further product details or use coupons. This encourages purchasing behavior. The input is the notification content displayed in step 4, and the output is the user's action. User feedback may also be sent to the server as needed.

[0116] 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.

[0117] This invention provides a system that combines an emotion engine to provide more individualized support to evacuees in shelters. This system is characterized by collecting and analyzing environmental and medical data of evacuees, and further evaluating the emotional state of evacuees using image recognition technology and an emotion engine.

[0118] The terminals are installed within evacuation shelters and acquire environmental data such as temperature, humidity, atmospheric pressure, and noise from sensors, and use cameras to capture the behavior and facial expressions of evacuees. This image data is temporarily stored on the terminals and then transmitted to a server.

[0119] The server analyzes environmental and image data received from terminals. Environmental data analysis determines the physical condition of the evacuation center, while simultaneously passing image data to an emotion engine to analyze the evacuees' facial expressions and voice tones to assess their emotional state. The emotion engine uses AI technology to identify emotions such as stress, relief, anxiety, and joy from facial and voice characteristics.

[0120] Users interact with the chatbot via their device and input their individual needs. During this process, the emotion engine analyzes the emotional nuances of the text entered by the user to identify their psychological needs. In this way, needs that take the user's emotional state into account are sent to the server.

[0121] The server evaluates the user's needs, including the results of analysis from the emotion engine, and formulates appropriate support supplies. For example, if the server determines that the user is experiencing high stress levels, it will consider providing goods or services that can help reduce stress. This generates a list of support supplies, which is then sent to the support team.

[0122] Based on the list received from the server, the support team arranges for necessary supplies and delivers them quickly to designated shelters. Throughout this process, feedback is collected again via chatbot and reflected in the system, continuously improving the quality of support.

[0123] Thus, this system takes into account the individual emotional states of evacuees and provides support in both psychological and material aspects. It is expected that this will make life in evacuation shelters more comfortable and help maintain mental health.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The device collects environmental data such as temperature, humidity, air pressure, and noise within the evacuation center and temporarily stores the data from sensors. It also captures the actions and facial expressions of evacuees through its camera and saves the data as image data.

[0127] Step 2:

[0128] The terminal compresses the collected environmental and image data and sends it to the server. This transmission is performed periodically to ensure that the server always receives the most up-to-date data.

[0129] Step 3:

[0130] The server analyzes environmental data received from terminals to determine the state of the physical environment within the evacuation center. If an anomaly is detected, it generates an alert and considers necessary countermeasures.

[0131] Step 4:

[0132] The server passes the image data to the emotion engine, which analyzes the evacuees' facial expressions and movements. The emotion engine uses an AI algorithm to identify the expressed emotions as stress, relief, anxiety, etc.

[0133] Step 5:

[0134] Users input their needs and requests through the chatbot. The entered text is then analyzed by an emotion engine, which evaluates the emotional nuances expressed in the words.

[0135] Step 6:

[0136] The server integrates the results of environmental data analysis, emotion engine evaluation, and user chatbot input to identify needs and generate a list of support supplies that should be prioritized.

[0137] Step 7:

[0138] A list of necessary supplies, generated on the server, is sent to the support team. This list includes the types of supplies needed and their priorities, and is designed to enable rapid procurement.

[0139] Step 8:

[0140] Users provide feedback via a chatbot on whether the support provided was appropriate. This feedback is incorporated into future support plans and used to improve the service.

[0141] Step 9:

[0142] The terminals continue to collect environmental data and monitor changes in the situation at the evacuation centers. New data is resent to the server, ensuring that support is always provided based on the latest information.

[0143] (Example 2)

[0144] 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".

[0145] Traditional support systems in evacuation shelters face challenges in accurately assessing the individual emotional states of evacuees and providing appropriate support based on those assessments. Furthermore, shortcomings in emotional analysis and real-time assessment of support needs mean that the emotional and material needs of evacuees cannot be adequately met.

[0146] 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.

[0147] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, and means for evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence. This makes it possible to provide personalized support that takes into account the emotional state of evacuees and to quickly and appropriately meet the needs of evacuation centers.

[0148] "Means of collecting environmental information" refers to a system that uses sensors installed in evacuation centers to acquire information such as temperature, humidity, atmospheric pressure, and noise levels.

[0149] "Means of analyzing medical information" refers to a system that analyzes data on the health status and past medical history provided by evacuees to evaluate their health condition.

[0150] "A means of evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence" refers to a system that utilizes AI technology to infer emotions from evacuees' facial expressions and voices, and then identifies the need for specific support based on those emotions.

[0151] "Means for optimizing support resources based on identified emotional states and needs" refers to a system that assesses the emotions and individual needs of evacuees and determines appropriate support supplies and services.

[0152] "A means of monitoring the situation in evacuation shelters and the emotions of evacuees using image recognition technology" refers to a system that uses cameras inside evacuation shelters to analyze images and monitor the situation and changes in emotions.

[0153] "A means of collecting and analyzing user requests and emotional nuances through a chatbot" refers to a system that uses conversational AI to collect user requests and emotions as text data, and then uses that analysis to determine specific support strategies.

[0154] This system is designed to provide individualized support to evacuees in shelters. Specifically, it analyzes environmental information and the emotional state of evacuees, and then optimizes the distribution of relief supplies based on that analysis.

[0155] First, the device has the ability to collect environmental data such as temperature, humidity, atmospheric pressure, and noise using sensors. In addition, the device uses a camera and image recognition technology to capture the actions and facial expressions of evacuees. All collected data is sent to a server for analysis.

[0156] Upon receiving data, the server first analyzes environmental information to assess the physical condition of the evacuation center. General data analysis software is used for this process. Next, the image data is passed to an emotion engine, which uses AI technology to evaluate the emotional state of the evacuees. This emotion engine identifies emotions such as stress, relief, anxiety, and joy through analysis of facial expressions and voice.

[0157] Meanwhile, users can interact with the chatbot through their device and input their individual needs. The information entered as text is analyzed by an emotion engine on the server, and a psychological evaluation is made based on the user's needs.

[0158] Through the collection and analysis of this data, the server evaluates the analysis results and determines the most appropriate relief supplies and services. At this stage, the relief team is required to take swift and appropriate action, and a system is put in place to quickly deliver aid to evacuees.

[0159] For example, if an evacuee complains of being unable to sleep, the system uses an emotion engine to analyze the underlying stress and decides whether to provide stress-reducing items. It can also be used in generative AI models as a prompt, such as, "When a user describes their experience in the evacuation center, the system analyzes the emotional nuances and makes suggestions for stress reduction." This functionality enables adaptive and flexible support in evacuation centers.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The device uses sensors to collect environmental information within the evacuation center in real time. Specifically, it acquires data such as temperature, humidity, atmospheric pressure, and noise. This environmental data is temporarily stored within the device. The input is real-time physical information obtained from the sensors, and the output is an environmental dataset necessary for analysis.

[0163] Step 2:

[0164] The device uses a camera to capture the actions and facial expressions of evacuees. The resulting image data serves as an important source of information for evaluating the emotional state of the evacuees. The input is image data captured by the camera, and the output is an image data file for analysis.

[0165] Step 3:

[0166] The terminal sends the collected environmental and image data to the server. This transmission is performed in real time or in batch processing, depending on the network environment. The input is temporarily stored data, and the output is the data transferred to the server.

[0167] Step 4:

[0168] The server analyzes the received environmental data and evaluates the physical condition of the evacuation center. This is done using data analysis software equipped with an analysis algorithm. The input is environmental data from the terminals, and the output is the evaluation result regarding the condition of the evacuation center.

[0169] Step 5:

[0170] The server passes image data to the emotion engine, which uses AI technology to analyze the evacuees' emotions. The emotion engine uses a generative AI model to identify multiple emotions such as stress, relief, and anxiety. The input is image data, and the output is an evaluation of the emotional state.

[0171] Step 6:

[0172] Users interact with the chatbot via their device, inputting their emotions and needs. This information is parsed as text and further analyzed by an emotion engine. The input is text data from the user, and the output is an analyzed needs assessment.

[0173] Step 7:

[0174] The server identifies the most suitable support resources and services based on the emotional analysis results and the user's needs. This includes the process of generating a list of support items. The input is the emotional engine's analysis results and needs assessment, and the output is a list of support resources.

[0175] Step 8:

[0176] The support team prepares necessary supplies based on the list of support resources received from the server and delivers them quickly to the shelters. Feedback is collected again through the chatbot and reflected in the system. The input is the list of support resources, and the output is the supply of supplies to the shelters.

[0177] (Application Example 2)

[0178] 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".

[0179] The present invention aims to develop a system that provides individualized support to improve the living environment of evacuees in evacuation shelters and can address their psychological and material needs. Furthermore, it aims to enable the provision of rapid and accurate support by evaluating the emotional state of evacuees in real time.

[0180] 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.

[0181] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, means for identifying the needs of evacuees using generative artificial intelligence, means for optimizing support resources based on the identified needs, means for evaluating the emotional state of evacuees using an emotion analysis engine, and means for providing countermeasures based on the evaluated emotional state. This enables accurate support and resource allocation in response to the needs and emotional state of evacuees within the evacuation center.

[0182] "Environmental information" refers to data about the surrounding physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise.

[0183] "Medical information" refers to data related to the health status, medical history, and physical health of evacuees.

[0184] "Generative artificial intelligence" is an artificial intelligence technology used to generate new information based on large amounts of data and to accomplish specific tasks.

[0185] "Demands" refer to the needs of evacuees for supplies, services, or emotional support they require in their daily lives.

[0186] "Support resources" include physical and psychological support such as food, clothing, medical supplies, and counseling services provided to evacuees.

[0187] An "emotion analysis engine" is a program and algorithm that analyzes image and audio data to automatically evaluate the emotional state of a subject.

[0188] "Response measures" refer to specific action plans and support methods provided in accordance with the identified needs and emotional states of evacuees.

[0189] One embodiment of the present invention is to construct a system for supporting evacuees in evacuation shelters.

[0190] The main components of this system include sensor-equipped terminals for collecting environmental information, a database for analyzing medical information, and a generative artificial intelligence model for identifying the needs of evacuees. Furthermore, it utilizes an emotion analysis engine to analyze the emotional state of evacuees in real time and provides appropriate countermeasures based on the evaluated emotional state. This makes it possible to keep the living environment of evacuees safer and more comfortable.

[0191] The terminals are installed within the evacuation shelters and collect data on temperature, humidity, atmospheric pressure, and noise using sensors. They also capture the facial expressions and voices of evacuees using cameras and microphones, and temporarily store the data on the terminals. This data is later sent to a cloud server.

[0192] The server not only analyzes the received environmental and medical information, but also uses an emotion analysis engine to identify emotions from images and audio. This engine utilizes commercially available APIs, such as Microsoft Azure's Emotion API. This allows for the identification of stress, anxiety, and feelings of security, and the feeding of this information back to the administrator system.

[0193] The user (shelter manager) receives this information, selects appropriate support resources as needed, and provides them to the evacuees. This improves the accuracy of support and increases the emotional and material satisfaction of the evacuees.

[0194] For example, if a toddler becomes anxious and starts crying in an evacuation center, the system detects changes in their facial expression and voice and sends an alert to the administrator stating, "Your child is feeling anxious. Giving them their favorite toy might help them feel more at ease." An example of a prompt to the AI ​​model in this case would be, "Generate a concrete example of data integration using Azure Emotion API and Firebase in an emotion analysis app for an evacuation center environment."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The terminal collects environmental information such as temperature, humidity, atmospheric pressure, and noise within the evacuation center using sensors. The input is raw data from various sensors, and the output is aggregated environmental information data. This data is temporarily stored within the terminal.

[0198] Step 2:

[0199] The device captures the facial expressions and voices of evacuees using its camera and microphone. The input is audio and video data from the camera and microphone, and the output is that same video and audio data. This data is temporarily stored on the device.

[0200] Step 3:

[0201] The terminal sends environmental information data and video / audio data to the cloud server. The input is the data aggregated and captured in steps 1 and 2, and the output is the data uploaded to the cloud.

[0202] Step 4:

[0203] The server analyzes environmental data uploaded to the cloud. The input is environmental data on the cloud, and the output is the analysis results regarding the physical condition of the evacuation shelter. This analysis is used to evaluate the physical condition of the evacuation shelter.

[0204] Step 5:

[0205] The server uses an emotion analysis engine to analyze video and audio data and evaluate the emotional state of evacuees. The input is video and audio data stored in the cloud, and the output is the analysis results regarding emotional state. For example, it uses the Microsoft Azure Emotion API to identify stress and feelings of security.

[0206] Step 6:

[0207] The server uses generated artificial intelligence to identify the needs of evacuees. Inputs include analyzed environmental information, medical information, and emotional state. Outputs are information about the evacuees' needs and their priorities. The AI ​​model generates the specified information using prompt statements.

[0208] Step 7:

[0209] The server optimizes support resources based on identified requests. The input is the evacuee's request data, and the output is a list of optimized support resources. This allows for the selection and efficient distribution of necessary resources.

[0210] Step 8:

[0211] The user receives information about support resources from the server and prepares and provides appropriate countermeasures. The input is an optimized list of support resources, and the output is the specific support provided. This allows evacuees to receive the necessary support more quickly.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a system for understanding the diverse needs and health conditions of evacuees in evacuation shelters in real time and providing optimal relief supplies. In one embodiment, it is necessary to first place multiple sensors and cameras within the evacuation shelter. These sensors continuously acquire environmental data such as temperature, humidity, atmospheric pressure, and noise, and collect this information on a terminal. The cameras analyze the behavior and facial expressions of evacuees using image recognition technology.

[0229] The terminals collect this data, compress it periodically, and send it to the server. The server analyzes the received data and uses artificial intelligence to individually identify the needs of the evacuees. For example, if the server detects an abnormal temperature change, it will prioritize providing warm clothing to the evacuees. By analyzing medical data, it can also quickly determine if a particular evacuee is requesting medication for a chronic illness.

[0230] Users can interact with a chatbot via a terminal and directly communicate their needs. This information is collected on a server and used to facilitate real-time decision-making. For example, if a user enters "I need diapers," that information is immediately reflected in the list of available supplies, and if it's a high priority, it will be addressed immediately.

[0231] Furthermore, based on the analysis results, the server creates a list of necessary relief supplies. This list is sent to the relief team, and an optimal relief schedule is developed, taking priorities into consideration. For example, if it is determined that there is a food shortage in a certain area, the server immediately issues instructions to procure the required amount of food and deliver it quickly to the shelters.

[0232] In this way, the system efficiently identifies the individual needs of evacuees and provides prompt and accurate support. This improves the quality of life for evacuees in shelters and facilitates their adaptation to the environment.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The device acquires environmental data from sensors installed in the evacuation center. This includes information such as temperature, humidity, atmospheric pressure, and noise levels. It also uses a camera to capture images of evacuees' behavior and facial expressions, and collects that image data.

[0236] Step 2:

[0237] The terminal compresses the collected environmental and image data and prepares it for transmission to the server. The compressed data packets are sent to the server over the network.

[0238] Step 3:

[0239] The server analyzes the data received from the terminals. First, it analyzes environmental data and performs threshold checks to detect anomalies within the evacuation center. Next, it performs image analysis and uses AI to infer the psychological and health status of evacuees from their facial expressions and behavior.

[0240] Step 4:

[0241] The server uses natural language processing technology to analyze requests collected directly from users through the chatbot, classifies the needs based on this analysis, and evaluates their importance.

[0242] Step 5:

[0243] Based on the analysis results, the server identifies the needs of each evacuee and generates a list of appropriate relief supplies. This list includes the type, quantity, and priority of the supplies to be provided.

[0244] Step 6:

[0245] The server sends the generated list of relief supplies to the relief team, who then develop a detailed relief plan. This ensures that the supplies are properly arranged and delivered quickly to their designated destinations.

[0246] Step 7:

[0247] Users provide feedback on the donated supplies to the server via a chatbot, reporting their satisfaction with the supplies and any additional requests. This feedback will be incorporated into future aid plans.

[0248] Step 8:

[0249] The terminal continuously collects environmental data and retransmits new data if the situation at the evacuation center changes. The server uses this data to reassess needs and consider providing additional relief supplies.

[0250] (Example 1)

[0251] 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."

[0252] In evacuation shelters, it is essential to quickly understand the individual needs and health conditions of evacuees and provide the most appropriate relief supplies based on that information. However, conventional methods make it difficult to efficiently collect and analyze this information, making it challenging to provide timely and appropriate support.

[0253] 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.

[0254] In this invention, the server includes means for acquiring environmental information, means for analyzing human health conditions, and means for identifying the needs of evacuees using a generative model. This enables comprehensive collection and analysis of information in evacuation shelters, and makes it possible to provide relief supplies quickly and accurately to individual evacuees.

[0255] "Environmental information" refers to data that indicates physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise levels.

[0256] "Analysis of human health status" is the process of analyzing information about the health of evacuees and identifying the necessary medical care and supplies.

[0257] A "generative model" is a system that uses machine learning algorithms based on collected data to predict and identify the needs of evacuees.

[0258] "Relief supplies" refers to essential items such as food, clothing, and medicine provided to evacuees at evacuation centers.

[0259] "Image processing technology" is a technique for analyzing images acquired from cameras and other visual devices to determine a situation.

[0260] "Interactive software" is a program that enables interaction with the user and collects specific requests or information.

[0261] "Data compression" is a technology that reduces the amount of data while preserving information, thereby enabling more efficient communication.

[0262] An "information processing device" is a computer system that receives and analyzes large amounts of data and creates a list of necessary relief supplies.

[0263] One embodiment of this invention is a system for quickly identifying the needs of evacuees in shelters and providing optimal relief supplies. This system uses multiple hardware and software components to perform comprehensive information gathering and analysis.

[0264] First, the terminal acquires environmental information in real time using various sensors (temperature, humidity, atmospheric pressure, noise sensors, etc.) installed within the evacuation center. This data is collected using compact computer devices such as Raspberry Pi. In addition, it uses cameras to analyze the behavior and facial expressions of evacuees using image processing technology and collects that data as well.

[0265] Next, the terminal compresses the data and sends all collected information to a central server via a secure communication protocol (e.g., TLS). This compression process improves communication efficiency while ensuring data reliability.

[0266] Based on the received data, the server utilizes a generative AI model to identify the health status and individual needs of evacuees. The generative AI model uses machine learning algorithms to detect unusual environmental changes and individual medical needs, and lists priority relief supplies. In this process, the server has mechanisms in place to quickly detect extreme weather and health risks, and to facilitate necessary support actions.

[0267] Furthermore, users can directly input their needs through interactive software (chatbots) installed in evacuation centers. These chatbots use natural language processing to analyze the user's text input and send it to the server. For example, a specific prompt might be, "We are running low on water, so please replenish it," and that information will be reflected in the support list.

[0268] This allows the system to comprehensively understand the needs of evacuees in real time and provide appropriate relief supplies. This, in turn, can improve the quality of life in evacuation shelters and facilitate smoother adaptation to the new environment.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The terminal acquires environmental information through sensors installed within the evacuation center. The input for this step consists of direct data such as temperature, humidity, atmospheric pressure, and noise. After acquiring this information, the terminal reads the raw data from the sensors and formats it as time-series data. This formatted data is necessary for subsequent analysis.

[0272] Step 2:

[0273] The terminal uses a camera to monitor the evacuees and acquire image data. The input is still images or video data capturing the evacuees' actions and facial expressions. The terminal incorporates image recognition technology to analyze the acquired image data in real time and detect abnormal behavior or emergencies among the evacuees. The output of the analysis results is a status tag for the identified evacuees.

[0274] Step 3:

[0275] The terminal compresses the collected environmental and image data into a single data packet. The input consists of the format data and analysis results obtained in steps 1 and 2. The terminal uses a data compression algorithm to reduce the amount of data. The compressed data is then ready to be sent to the server.

[0276] Step 4:

[0277] The terminal sends compressed data packets to the server via a secure communication protocol. The input is a compressed data packet. This data is sent to the server using encryption technology such as TLS. Once the server confirms receipt of the data, a successful transmission is indicated as output.

[0278] Step 5:

[0279] The server decompresses the compressed data received from the terminal and begins analysis. The input is the transmitted data packets. The server uses a generating AI model to detect abnormalities in the health status and environment of the evacuees and identify items that should be prioritized for support. The output is a needs list based on the condition of each evacuee.

[0280] Step 6:

[0281] The user inputs their requests through the interactive software installed at the evacuation site. The input is the need information in text form by the user. The chatbot analyzes this information and immediately sends it to the server. As an analysis result, the user's requests are added to and reflected in the support list.

[0282] Step 7:

[0283] Based on all the analysis results and the requests from the user, the server creates a relief supply list and gives instructions to the support team. The input is the analysis results and the user's request information. The server determines the type and quantity of the necessary relief supplies and their priorities, and sends that information to the support team. The output is a specific and optimized relief supply list.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] There is a problem that it is impossible to provide optimal information and supplies in real time to individuals with diverse needs, and it is difficult to reduce dissatisfaction and provide efficient services. In particular, in scenarios where individualized responses according to the environment and actions are required, a system that can respond quickly and accurately is necessary.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for collecting environmental information, means for analyzing personal health information, and means for identifying people's requests using the generated artificial intelligence. Thereby, it becomes possible to provide individualized product information and supplies.

[0289] "Environmental information" is data obtained by measuring the surrounding physical conditions such as temperature, humidity, atmospheric pressure, and noise.

[0290] "Personal health information" refers to data that indicates an individual's physical condition and health status, such as body temperature, heart rate, and exercise level.

[0291] Artificial intelligence is a computational model used to analyze vast amounts of data and predict specific patterns or needs.

[0292] "Means of identifying needs" refers to methods of analyzing collected data to identify the products and services that individuals desire.

[0293] "Means of optimizing resources" refers to methods for efficiently allocating and providing necessary resources based on identified needs.

[0294] "Image recognition technology" is a technology that uses cameras to analyze people's actions and facial expressions and extracts information based on that analysis.

[0295] A "dialogue mechanism" is an interface that receives information from the user and generates a response for the system.

[0296] To implement this invention, it is first necessary to place multiple sensors and cameras within the store. These sensors continuously acquire environmental information such as temperature, humidity, and location, while the cameras analyze the behavior and facial expressions of customers using image recognition technology. A server collects this data, analyzes it using artificial intelligence, and identifies the individual needs of each customer. Based on the analysis results, it notifies each customer of product information and services optimized for them through their device. A smartphone application is used for notifications, allowing customers to receive customized information in real time.

[0297] Specifically, the server manages data using AWS cloud services and operates AI models using TensorFlow. A mobile app developed with React Native is used as the terminal, and customers obtain information via their smartphones while in the store. For example, if a customer looks at a product for a while, the camera captures their behavior, the server analyzes information related to that product, and notifies the smartphone. This allows customers to obtain detailed information about products they are interested in, increasing their willingness to purchase.

[0298] An example of a prompt for the generating AI model is: "Identify customer interest in products based on their behavior and facial expressions in the store, and generate personalized app suggestions." Based on this prompt, the AI ​​performs analysis to provide the most relevant information to the customer.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] Sensors and cameras are activated to collect environmental information within the store and data on customer behavior and facial expressions in real time. The sensors measure temperature and humidity, and the cameras acquire images. The acquired data is transmitted to a server via the network. The input includes environmental data and camera footage, and the output is transmitted to the server.

[0302] Step 2:

[0303] The server analyzes the environmental information and image data it receives. Using a generative AI model, it analyzes the customer's behavior patterns and facial expression changes from the image data to identify products that the customer may be interested in. AI frameworks such as TensorFlow are utilized in this process. The input is the data acquired in step 1, and the output is the identified products of interest and related information.

[0304] Step 3:

[0305] The server generates individual prompt texts based on the analysis results. The prompt texts include instructions for providing information on products that the store visitors are determined to be interested in. These prompt texts are sent to the terminal. The input is the analysis result obtained in Step 2, and the output is the prompt text.

[0306] Step 4:

[0307] The terminal processes the prompt text received from the server and notifies the store visitors via the smartphone application. Specific product information and related coupons are displayed as push notifications. The user obtains information in real time by receiving this. The input is the prompt text, and the output is the content of the notification to the user.

[0308] Step 5:

[0309] The user receives the notification and browses further details of the product or uses the coupon. This promotes the purchasing behavior. The input is the content of the notification displayed in Step 4, and the output is the user's action. Feedback from the user may also be sent to the server as appropriate.

[0310] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0311] The present invention provides a system combined with an emotion engine to provide more individualized support to the evacuees in the shelter. This system is characterized by collecting and analyzing the environmental data and medical data of the evacuees, and further evaluating the emotional state of the evacuees using image recognition technology and an emotion engine.

[0312] The terminals are installed within evacuation shelters and acquire environmental data such as temperature, humidity, atmospheric pressure, and noise from sensors, and use cameras to capture the behavior and facial expressions of evacuees. This image data is temporarily stored on the terminals and then transmitted to a server.

[0313] The server analyzes environmental and image data received from terminals. Environmental data analysis determines the physical condition of the evacuation center, while simultaneously passing image data to an emotion engine to analyze the evacuees' facial expressions and voice tones to assess their emotional state. The emotion engine uses AI technology to identify emotions such as stress, relief, anxiety, and joy from facial and voice characteristics.

[0314] Users interact with the chatbot via their device and input their individual needs. During this process, the emotion engine analyzes the emotional nuances of the text entered by the user to identify their psychological needs. In this way, needs that take the user's emotional state into account are sent to the server.

[0315] The server evaluates the user's needs, including the results of analysis from the emotion engine, and formulates appropriate support supplies. For example, if the server determines that the user is experiencing high stress levels, it will consider providing goods or services that can help reduce stress. This generates a list of support supplies, which is then sent to the support team.

[0316] Based on the list received from the server, the support team arranges for necessary supplies and delivers them quickly to designated shelters. Throughout this process, feedback is collected again via chatbot and reflected in the system, continuously improving the quality of support.

[0317] Thus, this system takes into account the individual emotional states of evacuees and provides support in both psychological and material aspects. It is expected that this will make life in evacuation shelters more comfortable and help maintain mental health.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The device collects environmental data such as temperature, humidity, air pressure, and noise within the evacuation center and temporarily stores the data from sensors. It also captures the actions and facial expressions of evacuees through its camera and saves the data as image data.

[0321] Step 2:

[0322] The terminal compresses the collected environmental and image data and sends it to the server. This transmission is performed periodically to ensure that the server always receives the most up-to-date data.

[0323] Step 3:

[0324] The server analyzes environmental data received from terminals to determine the state of the physical environment within the evacuation center. If an anomaly is detected, it generates an alert and considers necessary countermeasures.

[0325] Step 4:

[0326] The server passes the image data to the emotion engine, which analyzes the evacuees' facial expressions and movements. The emotion engine uses an AI algorithm to identify the expressed emotions as stress, relief, anxiety, etc.

[0327] Step 5:

[0328] Users input their needs and requests through the chatbot. The entered text is then analyzed by an emotion engine, which evaluates the emotional nuances expressed in the words.

[0329] Step 6:

[0330] The server integrates the results of environmental data analysis, emotion engine evaluation, and user chatbot input to identify needs and generate a list of support supplies that should be prioritized.

[0331] Step 7:

[0332] A list of necessary supplies, generated on the server, is sent to the support team. This list includes the types of supplies needed and their priorities, and is designed to enable rapid procurement.

[0333] Step 8:

[0334] Users provide feedback via a chatbot on whether the support provided was appropriate. This feedback is incorporated into future support plans and used to improve the service.

[0335] Step 9:

[0336] The terminals continue to collect environmental data and monitor changes in the situation at the evacuation centers. New data is resent to the server, ensuring that support is always provided based on the latest information.

[0337] (Example 2)

[0338] 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".

[0339] Traditional support systems in evacuation shelters face challenges in accurately assessing the individual emotional states of evacuees and providing appropriate support based on those assessments. Furthermore, shortcomings in emotional analysis and real-time assessment of support needs mean that the emotional and material needs of evacuees cannot be adequately met.

[0340] 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.

[0341] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, and means for evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence. This makes it possible to provide personalized support that takes into account the emotional state of evacuees and to quickly and appropriately meet the needs of evacuation centers.

[0342] "Means of collecting environmental information" refers to a system that uses sensors installed in evacuation centers to acquire information such as temperature, humidity, atmospheric pressure, and noise levels.

[0343] "Means of analyzing medical information" refers to a system that analyzes data on the health status and past medical history provided by evacuees to evaluate their health condition.

[0344] "A means of evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence" refers to a system that utilizes AI technology to infer emotions from evacuees' facial expressions and voices, and then identifies the need for specific support based on those emotions.

[0345] "Means for optimizing support resources based on identified emotional states and needs" refers to a system that assesses the emotions and individual needs of evacuees and determines appropriate support supplies and services.

[0346] "A means of monitoring the situation in evacuation shelters and the emotions of evacuees using image recognition technology" refers to a system that uses cameras inside evacuation shelters to analyze images and monitor the situation and changes in emotions.

[0347] "A means of collecting and analyzing user requests and emotional nuances through a chatbot" refers to a system that uses conversational AI to collect user requests and emotions as text data, and then uses that analysis to determine specific support strategies.

[0348] This system is designed to provide individualized support to evacuees in shelters. Specifically, it analyzes environmental information and the emotional state of evacuees, and then optimizes the distribution of relief supplies based on that analysis.

[0349] First, the device has the ability to collect environmental data such as temperature, humidity, atmospheric pressure, and noise using sensors. In addition, the device uses a camera and image recognition technology to capture the actions and facial expressions of evacuees. All collected data is sent to a server for analysis.

[0350] Upon receiving data, the server first analyzes environmental information to assess the physical condition of the evacuation center. General data analysis software is used for this process. Next, the image data is passed to an emotion engine, which uses AI technology to evaluate the emotional state of the evacuees. This emotion engine identifies emotions such as stress, relief, anxiety, and joy through analysis of facial expressions and voice.

[0351] Meanwhile, users can interact with the chatbot through their device and input their individual needs. The information entered as text is analyzed by an emotion engine on the server, and a psychological evaluation is made based on the user's needs.

[0352] Through the collection and analysis of this data, the server evaluates the analysis results and determines the most appropriate relief supplies and services. At this stage, the relief team is required to take swift and appropriate action, and a system is put in place to quickly deliver aid to evacuees.

[0353] For example, if an evacuee complains of being unable to sleep, the system uses an emotion engine to analyze the underlying stress and decides whether to provide stress-reducing items. It can also be used in generative AI models as a prompt, such as, "When a user describes their experience in the evacuation center, the system analyzes the emotional nuances and makes suggestions for stress reduction." This functionality enables adaptive and flexible support in evacuation centers.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The device uses sensors to collect environmental information within the evacuation center in real time. Specifically, it acquires data such as temperature, humidity, atmospheric pressure, and noise. This environmental data is temporarily stored within the device. The input is real-time physical information obtained from the sensors, and the output is an environmental dataset necessary for analysis.

[0357] Step 2:

[0358] The device uses a camera to capture the actions and facial expressions of evacuees. The resulting image data serves as an important source of information for evaluating the emotional state of the evacuees. The input is image data captured by the camera, and the output is an image data file for analysis.

[0359] Step 3:

[0360] The terminal sends the collected environmental and image data to the server. This transmission is performed in real time or in batch processing, depending on the network environment. The input is temporarily stored data, and the output is the data transferred to the server.

[0361] Step 4:

[0362] The server analyzes the received environmental data and evaluates the physical condition of the evacuation center. This is done using data analysis software equipped with an analysis algorithm. The input is environmental data from the terminals, and the output is the evaluation result regarding the condition of the evacuation center.

[0363] Step 5:

[0364] The server passes image data to the emotion engine, which uses AI technology to analyze the evacuees' emotions. The emotion engine uses a generative AI model to identify multiple emotions such as stress, relief, and anxiety. The input is image data, and the output is an evaluation of the emotional state.

[0365] Step 6:

[0366] Users interact with the chatbot via their device, inputting their emotions and needs. This information is parsed as text and further analyzed by an emotion engine. The input is text data from the user, and the output is an analyzed needs assessment.

[0367] Step 7:

[0368] The server identifies the most suitable support resources and services based on the emotional analysis results and the user's needs. This includes the process of generating a list of support items. The input is the emotional engine's analysis results and needs assessment, and the output is a list of support resources.

[0369] Step 8:

[0370] The support team prepares necessary supplies based on the list of support resources received from the server and delivers them quickly to the shelters. Feedback is collected again through the chatbot and reflected in the system. The input is the list of support resources, and the output is the supply of supplies to the shelters.

[0371] (Application Example 2)

[0372] 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."

[0373] The present invention aims to develop a system that provides individualized support to improve the living environment of evacuees in evacuation shelters and can address their psychological and material needs. Furthermore, it aims to enable the provision of rapid and accurate support by evaluating the emotional state of evacuees in real time.

[0374] 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.

[0375] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, means for identifying the needs of evacuees using generative artificial intelligence, means for optimizing support resources based on the identified needs, means for evaluating the emotional state of evacuees using an emotion analysis engine, and means for providing countermeasures based on the evaluated emotional state. This enables accurate support and resource allocation in response to the needs and emotional state of evacuees within the evacuation center.

[0376] "Environmental information" refers to data about the surrounding physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise.

[0377] "Medical information" refers to data related to the health status, medical history, and physical health of evacuees.

[0378] "Generative artificial intelligence" is an artificial intelligence technology used to generate new information based on large amounts of data and to accomplish specific tasks.

[0379] "Demands" refer to the needs of evacuees for supplies, services, or emotional support they require in their daily lives.

[0380] "Support resources" include physical and psychological support such as food, clothing, medical supplies, and counseling services provided to evacuees.

[0381] An "emotion analysis engine" is a program and algorithm that analyzes image and audio data to automatically evaluate the emotional state of a subject.

[0382] "Response measures" refer to specific action plans and support methods provided in accordance with the identified needs and emotional states of evacuees.

[0383] One embodiment of the present invention is to construct a system for supporting evacuees in evacuation shelters.

[0384] The main components of this system include sensor-equipped terminals for collecting environmental information, a database for analyzing medical information, and a generative artificial intelligence model for identifying the needs of evacuees. Furthermore, it utilizes an emotion analysis engine to analyze the emotional state of evacuees in real time and provides appropriate countermeasures based on the evaluated emotional state. This makes it possible to keep the living environment of evacuees safer and more comfortable.

[0385] The terminals are installed within the evacuation shelters and collect data on temperature, humidity, atmospheric pressure, and noise using sensors. They also capture the facial expressions and voices of evacuees using cameras and microphones, and temporarily store the data on the terminals. This data is later sent to a cloud server.

[0386] The server not only analyzes the received environmental and medical information, but also uses an emotion analysis engine to identify emotions from images and audio. This engine utilizes commercially available APIs, such as Microsoft Azure's Emotion API. This allows for the identification of stress, anxiety, and feelings of security, and the feeding of this information back to the administrator system.

[0387] The user (shelter manager) receives this information, selects appropriate support resources as needed, and provides them to the evacuees. This improves the accuracy of support and increases the emotional and material satisfaction of the evacuees.

[0388] For example, if a toddler becomes anxious and starts crying in an evacuation center, the system detects changes in their facial expression and voice and sends an alert to the administrator stating, "Your child is feeling anxious. Giving them their favorite toy might help them feel more at ease." An example of a prompt to the generating AI model in this case would be, "Generate a concrete example of data integration using Azure Emotion API and Firebase in an emotion analysis app for an evacuation center environment."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The terminal collects environmental information such as temperature, humidity, atmospheric pressure, and noise within the evacuation center using sensors. The input is raw data from various sensors, and the output is aggregated environmental information data. This data is temporarily stored within the terminal.

[0392] Step 2:

[0393] The device captures the facial expressions and voices of evacuees using its camera and microphone. The input is audio and video data from the camera and microphone, and the output is that same video and audio data. This data is temporarily stored on the device.

[0394] Step 3:

[0395] The terminal sends environmental information data and video / audio data to the cloud server. The input is the data aggregated and captured in steps 1 and 2, and the output is the data uploaded to the cloud.

[0396] Step 4:

[0397] The server analyzes environmental data uploaded to the cloud. The input is environmental data on the cloud, and the output is the analysis results regarding the physical condition of the evacuation shelter. This analysis is used to evaluate the physical condition of the evacuation shelter.

[0398] Step 5:

[0399] The server uses an emotion analysis engine to analyze video and audio data and evaluate the emotional state of evacuees. The input is video and audio data stored in the cloud, and the output is the analysis results regarding emotional state. For example, it uses the Microsoft Azure Emotion API to identify stress and feelings of security.

[0400] Step 6:

[0401] The server uses generated artificial intelligence to identify the needs of evacuees. Inputs include analyzed environmental information, medical information, and emotional state. Outputs are information about the evacuees' needs and their priorities. The AI ​​model generates the specified information using prompt statements.

[0402] Step 7:

[0403] The server optimizes support resources based on identified requests. The input is the evacuee's request data, and the output is a list of optimized support resources. This allows for the selection and efficient distribution of necessary resources.

[0404] Step 8:

[0405] The user receives information about support resources from the server and prepares and provides appropriate countermeasures. The input is an optimized list of support resources, and the output is the specific support provided. This allows evacuees to receive the necessary support more quickly.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] This invention is a system for understanding the diverse needs and health conditions of evacuees in evacuation shelters in real time and providing optimal relief supplies. In one embodiment, it is necessary to first place multiple sensors and cameras within the evacuation shelter. These sensors continuously acquire environmental data such as temperature, humidity, atmospheric pressure, and noise, and collect this information on a terminal. The cameras analyze the behavior and facial expressions of evacuees using image recognition technology.

[0423] The terminals collect this data, compress it periodically, and send it to the server. The server analyzes the received data and uses artificial intelligence to individually identify the needs of the evacuees. For example, if the server detects an abnormal temperature change, it will prioritize providing warm clothing to the evacuees. By analyzing medical data, it can also quickly determine if a particular evacuee is requesting medication for a chronic illness.

[0424] Users can interact with a chatbot via a terminal and directly communicate their needs. This information is collected on a server and used to facilitate real-time decision-making. For example, if a user enters "I need diapers," that information is immediately reflected in the list of available supplies, and if it's a high priority, it will be addressed immediately.

[0425] Furthermore, based on the analysis results, the server creates a list of necessary relief supplies. This list is sent to the relief team, and an optimal relief schedule is developed, taking priorities into consideration. For example, if it is determined that there is a food shortage in a certain area, the server immediately issues instructions to procure the required amount of food and deliver it quickly to the shelters.

[0426] In this way, the system efficiently identifies the individual needs of evacuees and provides prompt and accurate support. This improves the quality of life for evacuees in shelters and facilitates their adaptation to the environment.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The device acquires environmental data from sensors installed in the evacuation center. This includes information such as temperature, humidity, atmospheric pressure, and noise levels. It also uses a camera to capture images of evacuees' behavior and facial expressions, and collects that image data.

[0430] Step 2:

[0431] The terminal compresses the collected environmental and image data and prepares it for transmission to the server. The compressed data packets are sent to the server over the network.

[0432] Step 3:

[0433] The server analyzes the data received from the terminals. First, it analyzes environmental data and performs threshold checks to detect anomalies within the evacuation center. Next, it performs image analysis and uses AI to infer the psychological and health status of evacuees from their facial expressions and behavior.

[0434] Step 4:

[0435] The server uses natural language processing technology to analyze requests collected directly from users through the chatbot, classifies the needs based on this analysis, and evaluates their importance.

[0436] Step 5:

[0437] Based on the analysis results, the server identifies the needs of each evacuee and generates a list of appropriate relief supplies. This list includes the type, quantity, and priority of the supplies to be provided.

[0438] Step 6:

[0439] The server sends the generated list of relief supplies to the relief team, who then develop a detailed relief plan. This ensures that the supplies are properly arranged and delivered quickly to their designated destinations.

[0440] Step 7:

[0441] Users provide feedback on the donated supplies to the server via a chatbot, reporting their satisfaction with the supplies and any additional requests. This feedback will be incorporated into future aid plans.

[0442] Step 8:

[0443] The terminal continuously collects environmental data and retransmits new data if the situation at the evacuation center changes. The server uses this data to reassess needs and consider providing additional relief supplies.

[0444] (Example 1)

[0445] 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."

[0446] In evacuation shelters, it is essential to quickly understand the individual needs and health conditions of evacuees and provide the most appropriate relief supplies based on that information. However, conventional methods make it difficult to efficiently collect and analyze this information, making it challenging to provide timely and appropriate support.

[0447] 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.

[0448] In this invention, the server includes means for acquiring environmental information, means for analyzing human health conditions, and means for identifying the needs of evacuees using a generative model. This enables comprehensive collection and analysis of information in evacuation shelters, and makes it possible to provide relief supplies quickly and accurately to individual evacuees.

[0449] "Environmental information" refers to data that indicates physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise levels.

[0450] "Analysis of human health status" is the process of analyzing information about the health of evacuees and identifying the necessary medical care and supplies.

[0451] A "generative model" is a system that uses machine learning algorithms based on collected data to predict and identify the needs of evacuees.

[0452] "Relief supplies" refers to essential items such as food, clothing, and medicine provided to evacuees at evacuation centers.

[0453] "Image processing technology" is a technique for analyzing images acquired from cameras and other visual devices to determine a situation.

[0454] "Interactive software" is a program that enables interaction with the user and collects specific requests or information.

[0455] "Data compression" is a technology that reduces the amount of data while preserving information, thereby enabling more efficient communication.

[0456] An "information processing device" is a computer system that receives and analyzes large amounts of data and creates a list of necessary relief supplies.

[0457] One embodiment of this invention is a system for quickly identifying the needs of evacuees in shelters and providing optimal relief supplies. This system uses multiple hardware and software components to perform comprehensive information gathering and analysis.

[0458] First, the terminal acquires environmental information in real time using various sensors (temperature, humidity, atmospheric pressure, noise sensors, etc.) installed within the evacuation center. This data is collected using compact computer devices such as Raspberry Pi. In addition, it uses cameras to analyze the behavior and facial expressions of evacuees using image processing technology and collects that data as well.

[0459] Next, the terminal compresses the data and sends all collected information to a central server via a secure communication protocol (e.g., TLS). This compression process improves communication efficiency while ensuring data reliability.

[0460] Based on the received data, the server utilizes a generative AI model to identify the health status and individual needs of evacuees. The generative AI model uses machine learning algorithms to detect unusual environmental changes and individual medical needs, and lists priority relief supplies. In this process, the server has mechanisms in place to quickly detect extreme weather and health risks, and to facilitate necessary support actions.

[0461] Furthermore, users can directly input their needs through interactive software (chatbots) installed in evacuation centers. These chatbots use natural language processing to analyze the user's text input and send it to the server. For example, a specific prompt might be, "We are running low on water, so please replenish it," and that information will be reflected in the support list.

[0462] This allows the system to comprehensively understand the needs of evacuees in real time and provide appropriate relief supplies. This, in turn, can improve the quality of life in evacuation shelters and facilitate smoother adaptation to the new environment.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The terminal acquires environmental information through sensors installed within the evacuation center. The input for this step consists of direct data such as temperature, humidity, atmospheric pressure, and noise. After acquiring this information, the terminal reads the raw data from the sensors and formats it as time-series data. This formatted data is necessary for subsequent analysis.

[0466] Step 2:

[0467] The terminal uses a camera to monitor the evacuees and acquire image data. The input is still images or video data capturing the evacuees' actions and facial expressions. The terminal incorporates image recognition technology to analyze the acquired image data in real time and detect abnormal behavior or emergencies among the evacuees. The output of the analysis results is a status tag for the identified evacuees.

[0468] Step 3:

[0469] The terminal compresses the collected environmental and image data into a single data packet. The input consists of the format data and analysis results obtained in steps 1 and 2. The terminal uses a data compression algorithm to reduce the amount of data. The compressed data is then ready to be sent to the server.

[0470] Step 4:

[0471] The terminal sends compressed data packets to the server via a secure communication protocol. The input is a compressed data packet. This data is sent to the server using encryption technology such as TLS. Once the server confirms receipt of the data, a successful transmission is indicated as output.

[0472] Step 5:

[0473] The server decompresses the compressed data received from the terminal and begins analysis. The input is the transmitted data packets. The server uses a generating AI model to detect abnormalities in the health status and environment of the evacuees and identify items that should be prioritized for support. The output is a needs list based on the condition of each evacuee.

[0474] Step 6:

[0475] Users input their requests through interactive software installed at the evacuation center. This input is text-based information about the user's needs. The chatbot analyzes this information and immediately sends it to the server. As a result of the analysis, the user's request is added to the support list and reflected in the system.

[0476] Step 7:

[0477] The server creates a list of relief supplies and issues instructions to the support team based on all analysis results and user requests. Inputs are analysis results and user request information. The server determines the types, quantities, and priorities of necessary relief supplies and sends this information to the support team. The output is a specific and optimized list of relief supplies.

[0478] (Application Example 1)

[0479] 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."

[0480] There is a challenge in providing individuals with diverse needs with the most suitable information and resources in real time, leading to decreased satisfaction and difficulties in providing efficient services. In particular, a system capable of rapid and accurate responses is needed in situations where individualized responses tailored to the environment and behavior are required.

[0481] 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.

[0482] In this invention, the server includes means for collecting environmental information, means for analyzing personal health information, and means for identifying people's requests using generated artificial intelligence. This makes it possible to provide personalized product information and goods.

[0483] "Environmental information" refers to data that measures the surrounding physical conditions, such as temperature, humidity, atmospheric pressure, and noise.

[0484] "Personal health information" refers to data that indicates an individual's physical condition and health status, such as body temperature, heart rate, and exercise level.

[0485] Artificial intelligence is a computational model used to analyze vast amounts of data and predict specific patterns or needs.

[0486] "Means of identifying needs" refers to methods of analyzing collected data to identify the products and services that individuals desire.

[0487] "Means of optimizing resources" refers to methods for efficiently allocating and providing necessary resources based on identified needs.

[0488] "Image recognition technology" is a technology that uses cameras to analyze people's actions and facial expressions and extracts information based on that analysis.

[0489] A "dialogue mechanism" is an interface that receives information from the user and generates a response for the system.

[0490] To implement this invention, it is first necessary to place multiple sensors and cameras within the store. These sensors continuously acquire environmental information such as temperature, humidity, and location, while the cameras analyze the behavior and facial expressions of customers using image recognition technology. A server collects this data, analyzes it using artificial intelligence, and identifies the individual needs of each customer. Based on the analysis results, it notifies each customer of product information and services optimized for them through their device. A smartphone application is used for notifications, allowing customers to receive customized information in real time.

[0491] Specifically, the server manages data using AWS cloud services and operates AI models using TensorFlow. A mobile app developed with React Native is used as the terminal, and customers obtain information via their smartphones while in the store. For example, if a customer looks at a product for a while, the camera captures their behavior, the server analyzes information related to that product, and notifies the smartphone. This allows customers to obtain detailed information about products they are interested in, increasing their willingness to purchase.

[0492] An example of a prompt for the generating AI model is: "Identify customer interest in products based on their behavior and facial expressions in the store, and generate personalized app suggestions." Based on this prompt, the AI ​​performs analysis to provide the most relevant information to the customer.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] Sensors and cameras are activated to collect environmental information within the store and data on customer behavior and facial expressions in real time. The sensors measure temperature and humidity, and the cameras acquire images. The acquired data is transmitted to a server via the network. The input includes environmental data and camera footage, and the output is transmitted to the server.

[0496] Step 2:

[0497] The server analyzes the environmental information and image data it receives. Using a generative AI model, it analyzes the customer's behavior patterns and facial expression changes from the image data to identify products that the customer may be interested in. AI frameworks such as TensorFlow are utilized in this process. The input is the data acquired in step 1, and the output is the identified products of interest and related information.

[0498] Step 3:

[0499] The server generates individual prompt messages based on the analysis results. These prompt messages contain instructions to provide information about products that the customer is deemed interested in. These prompt messages are sent to the terminal. The input is the analysis results obtained in step 2, and the output is the prompt messages.

[0500] Step 4:

[0501] The terminal processes the prompt message received from the server and notifies the customer via a smartphone application. Specific product information and related coupons are displayed as push notifications. Users receive this information in real time. The input is the prompt message, and the output is the notification content sent to the user.

[0502] Step 5:

[0503] The user receives a notification and can view further product details or use coupons. This encourages purchasing behavior. The input is the notification content displayed in step 4, and the output is the user's action. User feedback may also be sent to the server as needed.

[0504] 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.

[0505] This invention provides a system that combines an emotion engine to provide more individualized support to evacuees in shelters. This system is characterized by collecting and analyzing environmental and medical data of evacuees, and further evaluating the emotional state of evacuees using image recognition technology and an emotion engine.

[0506] The terminals are installed within evacuation shelters and acquire environmental data such as temperature, humidity, atmospheric pressure, and noise from sensors, and use cameras to capture the behavior and facial expressions of evacuees. This image data is temporarily stored on the terminals and then transmitted to a server.

[0507] The server analyzes environmental and image data received from terminals. Environmental data analysis determines the physical condition of the evacuation center, while simultaneously passing image data to an emotion engine to analyze the evacuees' facial expressions and voice tones to assess their emotional state. The emotion engine uses AI technology to identify emotions such as stress, relief, anxiety, and joy from facial and voice characteristics.

[0508] Users interact with the chatbot via their device and input their individual needs. During this process, the emotion engine analyzes the emotional nuances of the text entered by the user to identify their psychological needs. In this way, needs that take the user's emotional state into account are sent to the server.

[0509] The server evaluates the user's needs, including the results of analysis from the emotion engine, and formulates appropriate support supplies. For example, if the server determines that the user is experiencing high stress levels, it will consider providing goods or services that can help reduce stress. This generates a list of support supplies, which is then sent to the support team.

[0510] Based on the list received from the server, the support team arranges for necessary supplies and delivers them quickly to designated shelters. Throughout this process, feedback is collected again via chatbot and reflected in the system, continuously improving the quality of support.

[0511] Thus, this system takes into account the individual emotional states of evacuees and provides support in both psychological and material aspects. It is expected that this will make life in evacuation shelters more comfortable and help maintain mental health.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The device collects environmental data such as temperature, humidity, air pressure, and noise within the evacuation center and temporarily stores the data from sensors. It also captures the actions and facial expressions of evacuees through its camera and saves the data as image data.

[0515] Step 2:

[0516] The terminal compresses the collected environmental and image data and sends it to the server. This transmission is performed periodically to ensure that the server always receives the most up-to-date data.

[0517] Step 3:

[0518] The server analyzes environmental data received from terminals to determine the state of the physical environment within the evacuation center. If an anomaly is detected, it generates an alert and considers necessary countermeasures.

[0519] Step 4:

[0520] The server passes the image data to the emotion engine, which analyzes the evacuees' facial expressions and movements. The emotion engine uses an AI algorithm to identify the expressed emotions as stress, relief, anxiety, etc.

[0521] Step 5:

[0522] Users input their needs and requests through the chatbot. The entered text is then analyzed by an emotion engine, which evaluates the emotional nuances expressed in the words.

[0523] Step 6:

[0524] The server integrates the results of environmental data analysis, emotion engine evaluation, and user chatbot input to identify needs and generate a list of support supplies that should be prioritized.

[0525] Step 7:

[0526] A list of necessary supplies, generated on the server, is sent to the support team. This list includes the types of supplies needed and their priorities, and is designed to enable rapid procurement.

[0527] Step 8:

[0528] Users provide feedback via a chatbot on whether the support provided was appropriate. This feedback is incorporated into future support plans and used to improve the service.

[0529] Step 9:

[0530] The terminals continue to collect environmental data and monitor changes in the situation at the evacuation centers. New data is resent to the server, ensuring that support is always provided based on the latest information.

[0531] (Example 2)

[0532] 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."

[0533] Traditional support systems in evacuation shelters face challenges in accurately assessing the individual emotional states of evacuees and providing appropriate support based on those assessments. Furthermore, shortcomings in emotional analysis and real-time assessment of support needs mean that the emotional and material needs of evacuees cannot be adequately met.

[0534] 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.

[0535] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, and means for evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence. This makes it possible to provide personalized support that takes into account the emotional state of evacuees and to quickly and appropriately meet the needs of evacuation centers.

[0536] "Means of collecting environmental information" refers to a system that uses sensors installed in evacuation centers to acquire information such as temperature, humidity, atmospheric pressure, and noise levels.

[0537] "Means of analyzing medical information" refers to a system that analyzes data on the health status and past medical history provided by evacuees to evaluate their health condition.

[0538] "A means of evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence" refers to a system that utilizes AI technology to infer emotions from evacuees' facial expressions and voices, and then identifies the need for specific support based on those emotions.

[0539] "Means for optimizing support resources based on identified emotional states and needs" refers to a system that assesses the emotions and individual needs of evacuees and determines appropriate support supplies and services.

[0540] "A means of monitoring the situation in evacuation shelters and the emotions of evacuees using image recognition technology" refers to a system that uses cameras inside evacuation shelters to analyze images and monitor the situation and changes in emotions.

[0541] "A means of collecting and analyzing user requests and emotional nuances through a chatbot" refers to a system that uses conversational AI to collect user requests and emotions as text data, and then uses that analysis to determine specific support strategies.

[0542] This system is designed to provide individualized support to evacuees in shelters. Specifically, it analyzes environmental information and the emotional state of evacuees, and then optimizes the distribution of relief supplies based on that analysis.

[0543] First, the device has the ability to collect environmental data such as temperature, humidity, atmospheric pressure, and noise using sensors. In addition, the device uses a camera and image recognition technology to capture the actions and facial expressions of evacuees. All collected data is sent to a server for analysis.

[0544] Upon receiving data, the server first analyzes environmental information to assess the physical condition of the evacuation center. General data analysis software is used for this process. Next, the image data is passed to an emotion engine, which uses AI technology to evaluate the emotional state of the evacuees. This emotion engine identifies emotions such as stress, relief, anxiety, and joy through analysis of facial expressions and voice.

[0545] Meanwhile, users can interact with the chatbot through their device and input their individual needs. The information entered as text is analyzed by an emotion engine on the server, and a psychological evaluation is made based on the user's needs.

[0546] Through the collection and analysis of this data, the server evaluates the analysis results and determines the most appropriate relief supplies and services. At this stage, the relief team is required to take swift and appropriate action, and a system is put in place to quickly deliver aid to evacuees.

[0547] For example, if an evacuee complains of being unable to sleep, the system uses an emotion engine to analyze the underlying stress and decides whether to provide stress-reducing items. It can also be used in generative AI models as a prompt, such as, "When a user describes their experience in the evacuation center, the system analyzes the emotional nuances and makes suggestions for stress reduction." This functionality enables adaptive and flexible support in evacuation centers.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The device uses sensors to collect environmental information within the evacuation center in real time. Specifically, it acquires data such as temperature, humidity, atmospheric pressure, and noise. This environmental data is temporarily stored within the device. The input is real-time physical information obtained from the sensors, and the output is an environmental dataset necessary for analysis.

[0551] Step 2:

[0552] The device uses a camera to capture the actions and facial expressions of evacuees. The resulting image data serves as an important source of information for evaluating the emotional state of the evacuees. The input is image data captured by the camera, and the output is an image data file for analysis.

[0553] Step 3:

[0554] The terminal sends the collected environmental and image data to the server. This transmission is performed in real time or in batch processing, depending on the network environment. The input is temporarily stored data, and the output is the data transferred to the server.

[0555] Step 4:

[0556] The server analyzes the received environmental data and evaluates the physical condition of the evacuation center. This is done using data analysis software equipped with an analysis algorithm. The input is environmental data from the terminals, and the output is the evaluation result regarding the condition of the evacuation center.

[0557] Step 5:

[0558] The server passes image data to the emotion engine, which uses AI technology to analyze the evacuees' emotions. The emotion engine uses a generative AI model to identify multiple emotions such as stress, relief, and anxiety. The input is image data, and the output is an evaluation of the emotional state.

[0559] Step 6:

[0560] Users interact with the chatbot via their device, inputting their emotions and needs. This information is parsed as text and further analyzed by an emotion engine. The input is text data from the user, and the output is an analyzed needs assessment.

[0561] Step 7:

[0562] The server identifies the most suitable support resources and services based on the emotional analysis results and the user's needs. This includes the process of generating a list of support items. The input is the emotional engine's analysis results and needs assessment, and the output is a list of support resources.

[0563] Step 8:

[0564] The support team prepares necessary supplies based on the list of support resources received from the server and delivers them quickly to the shelters. Feedback is collected again through the chatbot and reflected in the system. The input is the list of support resources, and the output is the supply of supplies to the shelters.

[0565] (Application Example 2)

[0566] 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."

[0567] The present invention aims to develop a system that provides individualized support to improve the living environment of evacuees in evacuation shelters and can address their psychological and material needs. Furthermore, it aims to enable the provision of rapid and accurate support by evaluating the emotional state of evacuees in real time.

[0568] 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.

[0569] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, means for identifying the needs of evacuees using generative artificial intelligence, means for optimizing support resources based on the identified needs, means for evaluating the emotional state of evacuees using an emotion analysis engine, and means for providing countermeasures based on the evaluated emotional state. This enables accurate support and resource allocation in response to the needs and emotional state of evacuees within the evacuation center.

[0570] "Environmental information" refers to data about the surrounding physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise.

[0571] "Medical information" refers to data related to the health status, medical history, and physical health of evacuees.

[0572] "Generative artificial intelligence" is an artificial intelligence technology used to generate new information based on large amounts of data and to accomplish specific tasks.

[0573] "Demands" refer to the needs of evacuees for supplies, services, or emotional support they require in their daily lives.

[0574] "Support resources" include physical and psychological support such as food, clothing, medical supplies, and counseling services provided to evacuees.

[0575] An "emotion analysis engine" is a program and algorithm that analyzes image and audio data to automatically evaluate the emotional state of a subject.

[0576] "Response measures" refer to specific action plans and support methods provided in accordance with the identified needs and emotional states of evacuees.

[0577] One embodiment of the present invention is to construct a system for supporting evacuees in evacuation shelters.

[0578] The main components of this system include sensor-equipped terminals for collecting environmental information, a database for analyzing medical information, and a generative artificial intelligence model for identifying the needs of evacuees. Furthermore, it utilizes an emotion analysis engine to analyze the emotional state of evacuees in real time and provides appropriate countermeasures based on the evaluated emotional state. This makes it possible to keep the living environment of evacuees safer and more comfortable.

[0579] The terminals are installed within the evacuation shelters and collect data on temperature, humidity, atmospheric pressure, and noise using sensors. They also capture the facial expressions and voices of evacuees using cameras and microphones, and temporarily store the data on the terminals. This data is later sent to a cloud server.

[0580] The server not only analyzes the received environmental and medical information, but also uses an emotion analysis engine to identify emotions from images and audio. This engine utilizes commercially available APIs, such as Microsoft Azure's Emotion API. This allows for the identification of stress, anxiety, and feelings of security, and the feeding of this information back to the administrator system.

[0581] The user (shelter manager) receives this information, selects appropriate support resources as needed, and provides them to the evacuees. This improves the accuracy of support and increases the emotional and material satisfaction of the evacuees.

[0582] For example, if a toddler becomes anxious and starts crying in an evacuation center, the system detects changes in their facial expression and voice and sends an alert to the administrator stating, "Your child is feeling anxious. Giving them their favorite toy might help them feel more at ease." An example of a prompt to the AI ​​model in this case would be, "Generate a concrete example of data integration using Azure Emotion API and Firebase in an emotion analysis app for an evacuation center environment."

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The terminal collects environmental information such as temperature, humidity, atmospheric pressure, and noise within the evacuation center using sensors. The input is raw data from various sensors, and the output is aggregated environmental information data. This data is temporarily stored within the terminal.

[0586] Step 2:

[0587] The device captures the facial expressions and voices of evacuees using its camera and microphone. The input is audio and video data from the camera and microphone, and the output is that same video and audio data. This data is temporarily stored on the device.

[0588] Step 3:

[0589] The terminal sends environmental information data and video / audio data to the cloud server. The input is the data aggregated and captured in steps 1 and 2, and the output is the data uploaded to the cloud.

[0590] Step 4:

[0591] The server analyzes environmental data uploaded to the cloud. The input is environmental data on the cloud, and the output is the analysis results regarding the physical condition of the evacuation shelter. This analysis is used to evaluate the physical condition of the evacuation shelter.

[0592] Step 5:

[0593] The server uses an emotion analysis engine to analyze video and audio data and evaluate the emotional state of evacuees. The input is video and audio data stored in the cloud, and the output is the analysis results regarding emotional state. For example, it uses the Microsoft Azure Emotion API to identify stress and feelings of security.

[0594] Step 6:

[0595] The server uses generated artificial intelligence to identify the needs of evacuees. Inputs include analyzed environmental information, medical information, and emotional state. Outputs are information about the evacuees' needs and their priorities. The AI ​​model generates the specified information using prompt statements.

[0596] Step 7:

[0597] The server optimizes support resources based on identified requests. The input is the evacuee's request data, and the output is a list of optimized support resources. This allows for the selection and efficient distribution of necessary resources.

[0598] Step 8:

[0599] The user receives information about support resources from the server and prepares and provides appropriate countermeasures. The input is an optimized list of support resources, and the output is the specific support provided. This allows evacuees to receive the necessary support more quickly.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention is a system for understanding the diverse needs and health conditions of evacuees in evacuation shelters in real time and providing optimal relief supplies. In one embodiment, it is necessary to first place multiple sensors and cameras within the evacuation shelter. These sensors continuously acquire environmental data such as temperature, humidity, atmospheric pressure, and noise, and collect this information on a terminal. The cameras analyze the behavior and facial expressions of evacuees using image recognition technology.

[0618] The terminals collect this data, compress it periodically, and send it to the server. The server analyzes the received data and uses artificial intelligence to individually identify the needs of the evacuees. For example, if the server detects an abnormal temperature change, it will prioritize providing warm clothing to the evacuees. By analyzing medical data, it can also quickly determine if a particular evacuee is requesting medication for a chronic illness.

[0619] Users can interact with a chatbot via a terminal and directly communicate their needs. This information is collected on a server and used to facilitate real-time decision-making. For example, if a user enters "I need diapers," that information is immediately reflected in the list of available supplies, and if it's a high priority, it will be addressed immediately.

[0620] Furthermore, based on the analysis results, the server creates a list of necessary relief supplies. This list is sent to the relief team, and an optimal relief schedule is developed, taking priorities into consideration. For example, if it is determined that there is a food shortage in a certain area, the server immediately issues instructions to procure the required amount of food and deliver it quickly to the shelters.

[0621] In this way, the system efficiently identifies the individual needs of evacuees and provides prompt and accurate support. This improves the quality of life for evacuees in shelters and facilitates their adaptation to the environment.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The device acquires environmental data from sensors installed in the evacuation center. This includes information such as temperature, humidity, atmospheric pressure, and noise levels. It also uses a camera to capture images of evacuees' behavior and facial expressions, and collects that image data.

[0625] Step 2:

[0626] The terminal compresses the collected environmental and image data and prepares it for transmission to the server. The compressed data packets are sent to the server over the network.

[0627] Step 3:

[0628] The server analyzes the data received from the terminals. First, it analyzes environmental data and performs threshold checks to detect anomalies within the evacuation center. Next, it performs image analysis and uses AI to infer the psychological and health status of evacuees from their facial expressions and behavior.

[0629] Step 4:

[0630] The server uses natural language processing technology to analyze requests collected directly from users through the chatbot, classifies the needs based on this analysis, and evaluates their importance.

[0631] Step 5:

[0632] Based on the analysis results, the server identifies the needs of each evacuee and generates a list of appropriate relief supplies. This list includes the type, quantity, and priority of the supplies to be provided.

[0633] Step 6:

[0634] The server sends the generated list of relief supplies to the relief team, who then develop a detailed relief plan. This ensures that the supplies are properly arranged and delivered quickly to their designated destinations.

[0635] Step 7:

[0636] Users provide feedback on the donated supplies to the server via a chatbot, reporting their satisfaction with the supplies and any additional requests. This feedback will be incorporated into future aid plans.

[0637] Step 8:

[0638] The terminal continuously collects environmental data and retransmits new data if the situation at the evacuation center changes. The server uses this data to reassess needs and consider providing additional relief supplies.

[0639] (Example 1)

[0640] 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".

[0641] In evacuation shelters, it is essential to quickly understand the individual needs and health conditions of evacuees and provide the most appropriate relief supplies based on that information. However, conventional methods make it difficult to efficiently collect and analyze this information, making it challenging to provide timely and appropriate support.

[0642] 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.

[0643] In this invention, the server includes means for acquiring environmental information, means for analyzing human health conditions, and means for identifying the needs of evacuees using a generative model. This enables comprehensive collection and analysis of information in evacuation shelters, and makes it possible to provide relief supplies quickly and accurately to individual evacuees.

[0644] "Environmental information" refers to data that indicates physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise levels.

[0645] "Analysis of human health status" is the process of analyzing information about the health of evacuees and identifying the necessary medical care and supplies.

[0646] A "generative model" is a system that uses machine learning algorithms based on collected data to predict and identify the needs of evacuees.

[0647] "Relief supplies" refers to essential items such as food, clothing, and medicine provided to evacuees at evacuation centers.

[0648] "Image processing technology" is a technique for analyzing images acquired from cameras and other visual devices to determine a situation.

[0649] "Interactive software" is a program that enables interaction with the user and collects specific requests or information.

[0650] "Data compression" is a technology that reduces the amount of data while preserving information, thereby enabling more efficient communication.

[0651] An "information processing device" is a computer system that receives and analyzes large amounts of data and creates a list of necessary relief supplies.

[0652] One embodiment of this invention is a system for quickly identifying the needs of evacuees in shelters and providing optimal relief supplies. This system uses multiple hardware and software components to perform comprehensive information gathering and analysis.

[0653] First, the terminal acquires environmental information in real time using various sensors (temperature, humidity, atmospheric pressure, noise sensors, etc.) installed within the evacuation center. This data is collected using compact computer devices such as Raspberry Pi. In addition, it uses cameras to analyze the behavior and facial expressions of evacuees using image processing technology and collects that data as well.

[0654] Next, the terminal compresses the data and sends all collected information to a central server via a secure communication protocol (e.g., TLS). This compression process improves communication efficiency while ensuring data reliability.

[0655] Based on the received data, the server utilizes a generative AI model to identify the health status and individual needs of evacuees. The generative AI model uses machine learning algorithms to detect unusual environmental changes and individual medical needs, and lists priority relief supplies. In this process, the server has mechanisms in place to quickly detect extreme weather and health risks, and to facilitate necessary support actions.

[0656] Furthermore, users can directly input their needs through interactive software (chatbots) installed in evacuation centers. These chatbots use natural language processing to analyze the user's text input and send it to the server. For example, a specific prompt might be, "We are running low on water, so please replenish it," and that information will be reflected in the support list.

[0657] This allows the system to comprehensively understand the needs of evacuees in real time and provide appropriate relief supplies. This, in turn, can improve the quality of life in evacuation shelters and facilitate smoother adaptation to the new environment.

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] The terminal acquires environmental information through sensors installed within the evacuation center. The input for this step consists of direct data such as temperature, humidity, atmospheric pressure, and noise. After acquiring this information, the terminal reads the raw data from the sensors and formats it as time-series data. This formatted data is necessary for subsequent analysis.

[0661] Step 2:

[0662] The terminal uses a camera to monitor the evacuees and acquire image data. The input is still images or video data capturing the evacuees' actions and facial expressions. The terminal incorporates image recognition technology to analyze the acquired image data in real time and detect abnormal behavior or emergencies among the evacuees. The output of the analysis results is a status tag for the identified evacuees.

[0663] Step 3:

[0664] The terminal compresses the collected environmental and image data into a single data packet. The input consists of the format data and analysis results obtained in steps 1 and 2. The terminal uses a data compression algorithm to reduce the amount of data. The compressed data is then ready to be sent to the server.

[0665] Step 4:

[0666] The terminal sends compressed data packets to the server via a secure communication protocol. The input is a compressed data packet. This data is sent to the server using encryption technology such as TLS. Once the server confirms receipt of the data, a successful transmission is indicated as output.

[0667] Step 5:

[0668] The server decompresses the compressed data received from the terminal and begins analysis. The input is the transmitted data packets. The server uses a generating AI model to detect abnormalities in the health status and environment of the evacuees and identify items that should be prioritized for support. The output is a needs list based on the condition of each evacuee.

[0669] Step 6:

[0670] Users input their requests through interactive software installed at the evacuation center. This input is text-based information about the user's needs. The chatbot analyzes this information and immediately sends it to the server. As a result of the analysis, the user's request is added to the support list and reflected in the system.

[0671] Step 7:

[0672] The server creates a list of relief supplies and issues instructions to the support team based on all analysis results and user requests. Inputs are analysis results and user request information. The server determines the types, quantities, and priorities of necessary relief supplies and sends this information to the support team. The output is a specific and optimized list of relief supplies.

[0673] (Application Example 1)

[0674] 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".

[0675] There is a challenge in providing individuals with diverse needs with the most suitable information and resources in real time, leading to decreased satisfaction and difficulties in providing efficient services. In particular, a system capable of rapid and accurate responses is needed in situations where individualized responses tailored to the environment and behavior are required.

[0676] 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.

[0677] In this invention, the server includes means for collecting environmental information, means for analyzing personal health information, and means for identifying people's requests using generated artificial intelligence. This makes it possible to provide personalized product information and goods.

[0678] "Environmental information" refers to data that measures the surrounding physical conditions, such as temperature, humidity, atmospheric pressure, and noise.

[0679] "Personal health information" refers to data that indicates an individual's physical condition and health status, such as body temperature, heart rate, and exercise level.

[0680] Artificial intelligence is a computational model used to analyze vast amounts of data and predict specific patterns or needs.

[0681] "Means of identifying needs" refers to methods of analyzing collected data to identify the products and services that individuals desire.

[0682] "Means of optimizing resources" refers to methods for efficiently allocating and providing necessary resources based on identified needs.

[0683] "Image recognition technology" is a technology that uses cameras to analyze people's actions and facial expressions and extracts information based on that analysis.

[0684] A "dialogue mechanism" is an interface that receives information from the user and generates a response for the system.

[0685] To implement this invention, it is first necessary to place multiple sensors and cameras within the store. These sensors continuously acquire environmental information such as temperature, humidity, and location, while the cameras analyze the behavior and facial expressions of customers using image recognition technology. A server collects this data, analyzes it using artificial intelligence, and identifies the individual needs of each customer. Based on the analysis results, it notifies each customer of product information and services optimized for them through their device. A smartphone application is used for notifications, allowing customers to receive customized information in real time.

[0686] Specifically, the server manages data using AWS cloud services and operates AI models using TensorFlow. A mobile app developed with React Native is used as the terminal, and customers obtain information via their smartphones while in the store. For example, if a customer looks at a product for a while, the camera captures their behavior, the server analyzes information related to that product, and notifies the smartphone. This allows customers to obtain detailed information about products they are interested in, increasing their willingness to purchase.

[0687] An example of a prompt for the generating AI model is: "Identify customer interest in products based on their behavior and facial expressions in the store, and generate personalized app suggestions." Based on this prompt, the AI ​​performs analysis to provide the most relevant information to the customer.

[0688] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0689] Step 1:

[0690] Sensors and cameras are activated to collect environmental information within the store and data on customer behavior and facial expressions in real time. The sensors measure temperature and humidity, and the cameras acquire images. The acquired data is transmitted to a server via the network. The input includes environmental data and camera footage, and the output is transmitted to the server.

[0691] Step 2:

[0692] The server analyzes the environmental information and image data it receives. Using a generative AI model, it analyzes the customer's behavior patterns and facial expression changes from the image data to identify products that the customer may be interested in. AI frameworks such as TensorFlow are utilized in this process. The input is the data acquired in step 1, and the output is the identified products of interest and related information.

[0693] Step 3:

[0694] The server generates individual prompt messages based on the analysis results. These prompt messages contain instructions to provide information about products that the customer is deemed interested in. These prompt messages are sent to the terminal. The input is the analysis results obtained in step 2, and the output is the prompt messages.

[0695] Step 4:

[0696] The terminal processes the prompt message received from the server and notifies the customer via a smartphone application. Specific product information and related coupons are displayed as push notifications. Users receive this information in real time. The input is the prompt message, and the output is the notification content sent to the user.

[0697] Step 5:

[0698] The user receives a notification and can view further product details or use coupons. This encourages purchasing behavior. The input is the notification content displayed in step 4, and the output is the user's action. User feedback may also be sent to the server as needed.

[0699] 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.

[0700] This invention provides a system that combines an emotion engine to provide more individualized support to evacuees in shelters. This system is characterized by collecting and analyzing environmental and medical data of evacuees, and further evaluating the emotional state of evacuees using image recognition technology and an emotion engine.

[0701] The terminals are installed within evacuation shelters and acquire environmental data such as temperature, humidity, atmospheric pressure, and noise from sensors, and use cameras to capture the behavior and facial expressions of evacuees. This image data is temporarily stored on the terminals and then transmitted to a server.

[0702] The server analyzes environmental and image data received from terminals. Environmental data analysis determines the physical condition of the evacuation center, while simultaneously passing image data to an emotion engine to analyze the evacuees' facial expressions and voice tones to assess their emotional state. The emotion engine uses AI technology to identify emotions such as stress, relief, anxiety, and joy from facial and voice characteristics.

[0703] Users interact with the chatbot via their device and input their individual needs. During this process, the emotion engine analyzes the emotional nuances of the text entered by the user to identify their psychological needs. In this way, needs that take the user's emotional state into account are sent to the server.

[0704] The server evaluates the user's needs, including the results of analysis from the emotion engine, and formulates appropriate support supplies. For example, if the server determines that the user is experiencing high stress levels, it will consider providing goods or services that can help reduce stress. This generates a list of support supplies, which is then sent to the support team.

[0705] Based on the list received from the server, the support team arranges for necessary supplies and delivers them quickly to designated shelters. Throughout this process, feedback is collected again via chatbot and reflected in the system, continuously improving the quality of support.

[0706] Thus, this system takes into account the individual emotional states of evacuees and provides support in both psychological and material aspects. It is expected that this will make life in evacuation shelters more comfortable and help maintain mental health.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The device collects environmental data such as temperature, humidity, air pressure, and noise within the evacuation center and temporarily stores the data from sensors. It also captures the actions and facial expressions of evacuees through its camera and saves the data as image data.

[0710] Step 2:

[0711] The terminal compresses the collected environmental and image data and sends it to the server. This transmission is performed periodically to ensure that the server always receives the most up-to-date data.

[0712] Step 3:

[0713] The server analyzes environmental data received from terminals to determine the state of the physical environment within the evacuation center. If an anomaly is detected, it generates an alert and considers necessary countermeasures.

[0714] Step 4:

[0715] The server passes the image data to the emotion engine, which analyzes the evacuees' facial expressions and movements. The emotion engine uses an AI algorithm to identify the expressed emotions as stress, relief, anxiety, etc.

[0716] Step 5:

[0717] Users input their needs and requests through the chatbot. The entered text is then analyzed by an emotion engine, which evaluates the emotional nuances expressed in the words.

[0718] Step 6:

[0719] The server integrates the results of environmental data analysis, emotion engine evaluation, and user chatbot input to identify needs and generate a list of support supplies that should be prioritized.

[0720] Step 7:

[0721] A list of necessary supplies, generated on the server, is sent to the support team. This list includes the types of supplies needed and their priorities, and is designed to enable rapid procurement.

[0722] Step 8:

[0723] Users provide feedback via a chatbot on whether the support provided was appropriate. This feedback is incorporated into future support plans and used to improve the service.

[0724] Step 9:

[0725] The terminals continue to collect environmental data and monitor changes in the situation at the evacuation centers. New data is resent to the server, ensuring that support is always provided based on the latest information.

[0726] (Example 2)

[0727] 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".

[0728] Traditional support systems in evacuation shelters face challenges in accurately assessing the individual emotional states of evacuees and providing appropriate support based on those assessments. Furthermore, shortcomings in emotional analysis and real-time assessment of support needs mean that the emotional and material needs of evacuees cannot be adequately met.

[0729] 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.

[0730] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, and means for evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence. This makes it possible to provide personalized support that takes into account the emotional state of evacuees and to quickly and appropriately meet the needs of evacuation centers.

[0731] "Means of collecting environmental information" refers to a system that uses sensors installed in evacuation centers to acquire information such as temperature, humidity, atmospheric pressure, and noise levels.

[0732] "Means of analyzing medical information" refers to a system that analyzes data on the health status and past medical history provided by evacuees to evaluate their health condition.

[0733] "A means of evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence" refers to a system that utilizes AI technology to infer emotions from evacuees' facial expressions and voices, and then identifies the need for specific support based on those emotions.

[0734] "Means for optimizing support resources based on identified emotional states and needs" refers to a system that assesses the emotions and individual needs of evacuees and determines appropriate support supplies and services.

[0735] "A means of monitoring the situation in evacuation shelters and the emotions of evacuees using image recognition technology" refers to a system that uses cameras inside evacuation shelters to analyze images and monitor the situation and changes in emotions.

[0736] "A means of collecting and analyzing user requests and emotional nuances through a chatbot" refers to a system that uses conversational AI to collect user requests and emotions as text data, and then uses that analysis to determine specific support strategies.

[0737] This system is designed to provide individualized support to evacuees in shelters. Specifically, it analyzes environmental information and the emotional state of evacuees, and then optimizes the distribution of relief supplies based on that analysis.

[0738] First, the device has the ability to collect environmental data such as temperature, humidity, atmospheric pressure, and noise using sensors. In addition, the device uses a camera and image recognition technology to capture the actions and facial expressions of evacuees. All collected data is sent to a server for analysis.

[0739] Upon receiving data, the server first analyzes environmental information to assess the physical condition of the evacuation center. General data analysis software is used for this process. Next, the image data is passed to an emotion engine, which uses AI technology to evaluate the emotional state of the evacuees. This emotion engine identifies emotions such as stress, relief, anxiety, and joy through analysis of facial expressions and voice.

[0740] Meanwhile, users can interact with the chatbot through their device and input their individual needs. The information entered as text is analyzed by an emotion engine on the server, and a psychological evaluation is made based on the user's needs.

[0741] Through the collection and analysis of this data, the server evaluates the analysis results and determines the most appropriate relief supplies and services. At this stage, the relief team is required to take swift and appropriate action, and a system is put in place to quickly deliver aid to evacuees.

[0742] For example, if an evacuee complains of being unable to sleep, the system uses an emotion engine to analyze the underlying stress and decides whether to provide stress-reducing items. It can also be used in generative AI models as a prompt, such as, "When a user describes their experience in the evacuation center, the system analyzes the emotional nuances and makes suggestions for stress reduction." This functionality enables adaptive and flexible support in evacuation centers.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The device uses sensors to collect environmental information within the evacuation center in real time. Specifically, it acquires data such as temperature, humidity, atmospheric pressure, and noise. This environmental data is temporarily stored within the device. The input is real-time physical information obtained from the sensors, and the output is an environmental dataset necessary for analysis.

[0746] Step 2:

[0747] The device uses a camera to capture the actions and facial expressions of evacuees. The resulting image data serves as an important source of information for evaluating the emotional state of the evacuees. The input is image data captured by the camera, and the output is an image data file for analysis.

[0748] Step 3:

[0749] The terminal sends the collected environmental and image data to the server. This transmission is performed in real time or in batch processing, depending on the network environment. The input is temporarily stored data, and the output is the data transferred to the server.

[0750] Step 4:

[0751] The server analyzes the received environmental data and evaluates the physical condition of the evacuation center. This is done using data analysis software equipped with an analysis algorithm. The input is environmental data from the terminals, and the output is the evaluation result regarding the condition of the evacuation center.

[0752] Step 5:

[0753] The server passes image data to the emotion engine, which uses AI technology to analyze the evacuees' emotions. The emotion engine uses a generative AI model to identify multiple emotions such as stress, relief, and anxiety. The input is image data, and the output is an evaluation of the emotional state.

[0754] Step 6:

[0755] Users interact with the chatbot via their device, inputting their emotions and needs. This information is parsed as text and further analyzed by an emotion engine. The input is text data from the user, and the output is an analyzed needs assessment.

[0756] Step 7:

[0757] The server identifies the most suitable support resources and services based on the emotional analysis results and the user's needs. This includes the process of generating a list of support items. The input is the emotional engine's analysis results and needs assessment, and the output is a list of support resources.

[0758] Step 8:

[0759] The support team prepares necessary supplies based on the list of support resources received from the server and delivers them quickly to the shelters. Feedback is collected again through the chatbot and reflected in the system. The input is the list of support resources, and the output is the supply of supplies to the shelters.

[0760] (Application Example 2)

[0761] 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".

[0762] The present invention aims to develop a system that provides individualized support to improve the living environment of evacuees in evacuation shelters and can address their psychological and material needs. Furthermore, it aims to enable the provision of rapid and accurate support by evaluating the emotional state of evacuees in real time.

[0763] 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.

[0764] In this invention, the server includes means for collecting environmental information, means for analyzing medical information, means for identifying the needs of evacuees using generative artificial intelligence, means for optimizing support resources based on the identified needs, means for evaluating the emotional state of evacuees using an emotion analysis engine, and means for providing countermeasures based on the evaluated emotional state. This enables accurate support and resource allocation in response to the needs and emotional state of evacuees within the evacuation center.

[0765] "Environmental information" refers to data about the surrounding physical conditions within the evacuation shelter, such as temperature, humidity, atmospheric pressure, and noise.

[0766] "Medical information" refers to data related to the health status, medical history, and physical health of evacuees.

[0767] "Generative artificial intelligence" is an artificial intelligence technology used to generate new information based on large amounts of data and to accomplish specific tasks.

[0768] "Demands" refer to the needs of evacuees for supplies, services, or emotional support they require in their daily lives.

[0769] "Support resources" include physical and psychological support such as food, clothing, medical supplies, and counseling services provided to evacuees.

[0770] An "emotion analysis engine" is a program and algorithm that analyzes image and audio data to automatically evaluate the emotional state of a subject.

[0771] "Response measures" refer to specific action plans and support methods provided in accordance with the identified needs and emotional states of evacuees.

[0772] One embodiment of the present invention is to construct a system for supporting evacuees in evacuation shelters.

[0773] The main components of this system include sensor-equipped terminals for collecting environmental information, a database for analyzing medical information, and a generative artificial intelligence model for identifying the needs of evacuees. Furthermore, it utilizes an emotion analysis engine to analyze the emotional state of evacuees in real time and provides appropriate countermeasures based on the evaluated emotional state. This makes it possible to keep the living environment of evacuees safer and more comfortable.

[0774] The terminals are installed within the evacuation shelters and collect data on temperature, humidity, atmospheric pressure, and noise using sensors. They also capture the facial expressions and voices of evacuees using cameras and microphones, and temporarily store the data on the terminals. This data is later sent to a cloud server.

[0775] The server not only analyzes the received environmental and medical information, but also uses an emotion analysis engine to identify emotions from images and audio. This engine utilizes commercially available APIs, such as Microsoft Azure's Emotion API. This allows for the identification of stress, anxiety, and feelings of security, and the feeding of this information back to the administrator system.

[0776] The user (shelter manager) receives this information, selects appropriate support resources as needed, and provides them to the evacuees. This improves the accuracy of support and increases the emotional and material satisfaction of the evacuees.

[0777] For example, if a toddler becomes anxious and starts crying in an evacuation center, the system detects changes in their facial expression and voice and sends an alert to the administrator stating, "Your child is feeling anxious. Giving them their favorite toy might help them feel more at ease." An example of a prompt to the AI ​​model in this case would be, "Generate a concrete example of data integration using Azure Emotion API and Firebase in an emotion analysis app for an evacuation center environment."

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The terminal collects environmental information such as temperature, humidity, atmospheric pressure, and noise within the evacuation center using sensors. The input is raw data from various sensors, and the output is aggregated environmental information data. This data is temporarily stored within the terminal.

[0781] Step 2:

[0782] The device captures the facial expressions and voices of evacuees using its camera and microphone. The input is audio and video data from the camera and microphone, and the output is that same video and audio data. This data is temporarily stored on the device.

[0783] Step 3:

[0784] The terminal sends environmental information data and video / audio data to the cloud server. The input is the data aggregated and captured in steps 1 and 2, and the output is the data uploaded to the cloud.

[0785] Step 4:

[0786] The server analyzes environmental data uploaded to the cloud. The input is environmental data on the cloud, and the output is the analysis results regarding the physical condition of the evacuation shelter. This analysis is used to evaluate the physical condition of the evacuation shelter.

[0787] Step 5:

[0788] The server uses an emotion analysis engine to analyze video and audio data and evaluate the emotional state of evacuees. The input is video and audio data stored in the cloud, and the output is the analysis results regarding emotional state. For example, it uses the Microsoft Azure Emotion API to identify stress and feelings of security.

[0789] Step 6:

[0790] The server uses generated artificial intelligence to identify the needs of evacuees. Inputs include analyzed environmental information, medical information, and emotional state. Outputs are information about the evacuees' needs and their priorities. The AI ​​model generates the specified information using prompt statements.

[0791] Step 7:

[0792] The server optimizes support resources based on identified requests. The input is the evacuee's request data, and the output is a list of optimized support resources. This allows for the selection and efficient distribution of necessary resources.

[0793] Step 8:

[0794] The user receives information about support resources from the server and prepares and provides appropriate countermeasures. The input is an optimized list of support resources, and the output is the specific support provided. This allows evacuees to receive the necessary support more quickly.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] Means of collecting environmental data,

[0819] Methods for analyzing medical data,

[0820] A means of identifying the needs of evacuees using generative artificial intelligence,

[0821] Means for optimizing relief supplies based on identified needs,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for monitoring the situation in an evacuation center using image recognition technology.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising means for collecting user requests through a chatbot.

[0827] "Example 1"

[0828] (Claim 1)

[0829] Means of acquiring environmental information,

[0830] Means for analyzing human health status,

[0831] A means of identifying the needs of evacuees using a generative model,

[0832] Means for optimizing relief supplies based on identified needs,

[0833] A means of monitoring the situation in evacuation shelters using image processing technology,

[0834] A means of collecting individual requests through interactive software,

[0835] A means for compressing data and transmitting it to an information processing device,

[0836] A means of creating an item list based on the results analyzed by an information processing device,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which uses a generative model to detect anomalies and arrange for necessary items.

[0840] (Claim 3)

[0841] The system according to claim 1, which includes a process for immediately reflecting user requests.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] Means of collecting environmental information,

[0845] Means for analyzing personal health information,

[0846] A means of identifying people's needs using the generated artificial intelligence,

[0847] Means for optimizing supplies based on identified requirements,

[0848] A means of analyzing an individual's behavior and emotions to provide customized product information,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for monitoring the situation using video recognition technology.

[0852] (Claim 3)

[0853] The system according to claim 1, further comprising means for collecting requests from users through dialogue means.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] Means of collecting environmental information,

[0857] Means for analyzing medical information,

[0858] A means of evaluating the emotional state of evacuees and identifying their needs using generative artificial intelligence,

[0859] Means for optimizing support resources based on identified emotional states and needs,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, further comprising means for monitoring the situation in a shelter and the emotions of evacuees using image recognition technology.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for collecting and analyzing user requests and emotional nuances through a chatbot.

[0865] "Application example 2 when combining with an emotional engine"

[0866] (Claim 1)

[0867] Means of collecting environmental information,

[0868] Means for analyzing medical information,

[0869] A means of identifying the demands of evacuees using generative artificial intelligence,

[0870] Means for optimizing support resources based on identified requirements,

[0871] A means of evaluating the emotional state of evacuees using an emotion analysis engine,

[0872] A means of providing countermeasures based on the assessed emotional state,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for monitoring the situation in an evacuation center using image recognition technology.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising means for collecting user requests through a conversational agent. [Explanation of Symbols]

[0878] 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. Means of collecting environmental data, Methods for analyzing medical data, A means of identifying the needs of evacuees using generative artificial intelligence, Means for optimizing relief supplies based on identified needs, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring the situation at an evacuation center using image recognition technology.

3. The system according to claim 1, further comprising means for collecting user requests through a chatbot.

Images