system

Abstract

We provide the system. [Solution] The present invention relates to a return-home support system that, for users who are unable to return home due to the suspension of public transportation caused by natural disasters, etc., integrates diverse information on the server side, such as pedestrian flow data, public transportation operation information, traffic congestion information and traffic history, traffic regulation information, temporary accommodation facility and related facility information, and SNS information, and analyzes this information in a complex and dynamic manner using generative AI to generate and present in real time the optimal return route, means of returning home, suggestions for using temporary accommodation facilities, and measures to avoid dangerous and congested areas for each user. It is characterized by achieving highly accurate, individually optimized return-home support by taking into account user attributes, desired conditions, real-time conditions on site, and past disaster knowledge.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence associated with a description related to the character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] The problem that this invention aims to solve is that when public transportation is shut down due to sudden events such as natural disasters, and conventional means of transportation and route guidance become ineffective, it has been difficult to quickly and accurately present the optimal means and routes for the safe and efficient return of a large number of people who are unable to return home. Specifically, conventional return-home support systems can only acquire fragmentary information from individual information sources. For example, they cannot comprehensively and cross-sectionally analyze a vast amount of diverse information, such as pedestrian flow data, the operating status of public transportation, road congestion and closures, traffic regulations, the availability of temporary accommodations and related facilities, and even real-time reports from the scene posted on social media, etc., in order to generate the optimal method and route for each user to return home. As a result, users are forced to make their own judgments amid uncertain information, leading to problems such as choosing dangerous routes, unnecessary travel, or being forced to stay in the area for long periods of time. Furthermore, because the situation changes moment by moment during a disaster, there is a need for flexible route re-proposals that reflect the latest information and suggestions for the use of temporary accommodations, but conventional technology has found it difficult to provide these in real time and with individual optimization. The present invention addresses the challenge of ensuring the safety of people who have difficulty returning home and providing efficient support for their return by using generative AI to dynamically and comprehensively analyze data obtained from diverse information sources and generating and presenting in real time the optimal return route, means of transportation, and temporary accommodation facility suggestions for each user. [Means for solving the problem]

[0005] The present invention provides a means for solving the problems of the present invention, comprising a pedestrian flow information acquisition unit, a transportation information acquisition unit, and a route generation unit. The pedestrian flow information acquisition unit acquires pedestrian flow data, congestion status, traffic history, and on-site report information extracted from SNS at the user's current location and surrounding area. The transportation information acquisition unit acquires the operating status of public transportation, information on service suspensions, availability of alternative transportation, road congestion information, traffic regulation information, and availability of temporary accommodation facilities and related facilities. The route generation unit comprehensively analyzes the various information acquired from the pedestrian flow information acquisition unit and the transportation information acquisition unit using a generation AI, and generates and presents in real time the optimal return route and means of returning home for each user, as well as action guidelines including suggestions for using temporary accommodation facilities. This configuration realizes integrated analysis of diverse information and individually optimized return-home support, which was previously difficult. [Brief explanation of the drawing]

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0008] First, let's explain the terminology used in the following explanation.

[0009] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0010] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0011] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0012] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Further, in the present specification, when three or more items are expressed linked by "and / or", the same concept as that for "A and / or B" is applied.

[0014] [First Embodiment]

[0015] FIG. 1 illustrates an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. An example of the data processing apparatus 12 is a server.

[0017] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of the "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network), and the like.

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, the camera 42, and the communication I / F 44 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user inputs. The touch panel 38A receives a user input from contact of an indicator (for example, a pen, a finger, or the like) by detecting the contact of the indicator. The microphone 38B receives a voice-based user input by detecting the user's voice. A control unit 46A in the processor 46 transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A, a speaker 40B, and the like, and presents data to the user 20 by outputting the data in a representation form perceivable by the user 20 (for example, voice and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs voice in accordance with instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0025] 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 the 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 processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models. 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.

[0026] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0027] (Example 1)

[0028] The flow of a specific process in Example 1 will now be described. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0029] (Embodiments for carrying out the present invention)

[0030] Embodiments of the present invention will be described in more detail. The system of the present invention consists of a server and a terminal. The server side is equipped with a pedestrian flow information acquisition unit, a transportation information acquisition unit, an SNS information acquisition unit, a facility information acquisition unit, a generation AI processing unit, a route generation unit, and a database management unit. The terminal side is equipped with a user interface unit, a location information acquisition unit, a notification / display unit, and a user input unit. The functional division of each unit is as follows.

[0031] First, the user uses a device to obtain their current location information via GPS or similar means, and inputs their destination address and desired conditions (for example, "I want to get home as soon as possible," "I want to prioritize a safe route," "I want to avoid congestion," "I want a place to rest along the way," etc.). The device then sends this information to the server. The device can be implemented in various forms, such as a smartphone, tablet, wearable device, or in-car terminal, and interfaces tailored to user attributes can also be implemented, such as voice input and output for visually impaired users, and vibration notifications and text display for hearing impaired users.

[0032] The server-side pedestrian flow information acquisition unit collects pedestrian flow data from multiple sources, including anonymous location information from mobile devices, data from ticket gates using transportation IC cards, people counting through image analysis from surveillance cameras, pedestrian and vehicle passage data from road sensors, and location-tagged posts on social media. For example, it can grasp detailed congestion conditions in real time, such as observing five times the normal pedestrian flow on major arterial road A, restricting entry near the ticket gates at station B, experiencing a significant decrease in pedestrian speed due to congestion on bridge C, and finding underpass D relatively empty. Furthermore, this data is accumulated over time and used for congestion prediction and analysis of changes in pedestrian flow using generative AI.

[0033] The Transportation Information Acquisition Unit obtains information such as operating status, suspended sections, delays, availability of alternative transportation, and estimated resumption times from APIs for public transportation services such as railways, buses, taxis, shared bicycles, and car sharing. For example, it collects detailed operating status such as: railway line E is completely suspended; bus F is operating only on major sections; taxi G is experiencing long waiting times due to high demand; and shared bicycle H has suspended rentals at some ports. Furthermore, it acquires traffic restriction information from road traffic information centers and announcements from local governments and police, such as: road I is closed due to flooding; road J is operating with alternating one-way traffic due to a landslide; and road K is restricted to emergency vehicles only.

[0034] The SNS information acquisition unit collects disaster-related keywords and posts with location information from social media, and uses generative AI-powered natural language processing to extract information on passability, danger information, congestion levels, the level of congestion at evacuation centers, and real-time changes in the situation on site. For example, it extracts firsthand accounts from the field that cannot be grasped from official information, such as "Road L is flooded and impassable," "The area in front of Station M is overflowing with people," "Gymnasium N still has vacancies," "Bridge O is blocked off by the police," "Temporary toilets have been set up in Park P," "Underpass Q is flooded," and "Extra buses are running on Bus R," and also performs reliability evaluations and determines the freshness of the information.

[0035] The facility information acquisition unit acquires information on the availability, usability, capacity, and facilities (toilets, showers, power outlets, Wi-Fi, drinking water, food stockpiles, nursing rooms, barrier-free access, etc.) of evacuation centers, temporary accommodation facilities, and related facilities (e.g., gymnasiums, community centers, schools, hotels, karaoke boxes, internet cafes, family restaurants, convenience stores, etc.). For example, it collects detailed information such as: Gymnasium S is at full capacity and unavailable; Community Center T has vacancies; Hotel U has only some rooms available; Karaoke Box V is available for nighttime use; Internet Cafe W is full; Family Restaurant X is temporarily closed; and Convenience Store Y is open and has drinking water in stock.

[0036] The generative AI processing unit comprehensively analyzes this diverse information. The generative AI considers the reliability and freshness of each piece of information, eliminates duplicate information sources, analyzes time-series trends, takes into account user attributes and preferences, and incorporates big data and simulation results from past disasters to generate the optimal route and means of returning home. For example, for elderly people who have difficulty walking, it prioritizes barrier-free routes, routes with few steps, and stations with elevators, and recommends the use of temporary resting facilities along the way. For people with children, it prioritizes routes with many toilets and rest areas, and safe paths with nighttime lighting. In rainy weather, it prioritizes covered routes such as arcades and underpasses. If congestion is high, it suggests routes to avoid congestion and temporary shelters. Furthermore, for example, if a user requests to "eat along the way," it considers the location information, congestion status, and inventory information of open restaurants and convenience stores, and suggests stores that can be visited along the route. In times of disaster, we can flexibly accommodate specific needs such as "shelters that allow pets," "facilities with nursing rooms," "places where you can charge your devices," and "facilities with Wi-Fi access."

[0037] The route generation unit generates action guidelines for each user, combining the optimal return route, means of transportation, suggestions for temporary resting places along the way, information on related facilities that can be visited, and routes to avoid dangerous or crowded areas, based on the results analyzed by the generation AI. For example, it can propose detailed routes combining multiple means of transportation and facilities, such as: "Walk north along road Z from your current location, wait temporarily at gymnasium AA, then take the temporary bus BB to station CC, and board train DD from station CC as soon as it resumes operation to return home"; "Road EE is impassable due to flooding, underpass FF is passable but crowded, take a break at community center GG along the way and wait for the congestion to ease"; or "Use the pedestrian overpass HH with elevators as a barrier-free route, take a meal break at family restaurant II along the way, and return home from the nearest taxi stand JJ."

[0038] Generative AI reacquires information in real time during disasters when the situation is constantly changing, and re-evaluates routes and means of transportation. For example, if road KK on the recommended route becomes newly closed, it immediately proposes an alternative route and notifies the user's terminal. If shelter LL is full, it suggests other available facilities. If there is information on social media that "road MM has reopened," it cross-references it with official information and, if reliable, reflects it in the route. If a user wishes to change their route or stay temporarily along the way, the generative AI immediately generates a new optimal route and presents it to the terminal. Furthermore, if multiple users use the same route simultaneously, it also predicts congestion and guides users to distribute their routes. For example, dynamic optimization such as "If everyone continues to use route NN, congestion will worsen, so we will suggest route OO to some users" is also possible.

[0039] On the terminal side, the user interface provides map display, route guidance, voice guidance, and notification functions, enabling users to easily understand the situation and take action. For example, dangerous or congested areas along the route are color-coded on the map, and voice guidance such as "There is a flooded area 200 meters after turning right. Turn left to avoid it" is provided. If there are suggestions for route changes or temporary shelters, users are immediately notified via push notifications or vibration. When users themselves post on social media or report from the site, the information is sent from the terminal to the server, contributing to the improvement of overall information accuracy. For example, if a user posts on-site information such as "Road PP was actually passable" or "Shelter QQ had vacancies," the generative AI immediately analyzes this and reflects it in route suggestions for other users.

[0040] Furthermore, the generative AI utilizes big data and simulation results from past disasters, incorporating insights such as "roads were restored quickly during similar past disasters" and "shelter SS tends to fill up quickly each time," thereby enabling more accurate return-home support. For example, a generative AI that has learned from past typhoon disaster data on human flow, transportation restoration patterns, shelter congestion history, and SNS posting trends can predict future congestion and recovery prospects, and provide users with predictive information such as "railway TT is expected to resume operation in 30 minutes" and "shelter UU is likely to fill up within an hour."

[0041] As described above, in the embodiments of the present invention, the server side implements a human flow information acquisition unit, a transportation information acquisition unit, an SNS information acquisition unit, a facility information acquisition unit, a generative AI processing unit, a route generation unit, and a database management unit, while the terminal side implements a user interface unit, a location information acquisition unit, a notification / display unit, and a user input unit. This configuration makes it possible to provide highly accurate and flexible suggestions for return routes, means of transportation, and temporary accommodation facilities to people who have difficulty returning home during a disaster, using generative AI. Furthermore, a major feature of the present invention is that it comprehensively considers various factors such as user attributes, desired conditions, real-time conditions on site, and past disaster knowledge to realize detailed and practical return-home support.

[0042] (System Configuration)

[0043] The system according to this embodiment includes a pedestrian flow information acquisition unit, a transportation information acquisition unit, an SNS information acquisition unit, a facility information acquisition unit, a generation AI processing unit, a route generation unit, a database management unit, a user interface unit, a location information acquisition unit, a notification / display unit, and a user input unit. The pedestrian flow information acquisition unit is a unit that collects pedestrian flow data from multiple sources, such as anonymous location information from mobile terminals, ticket gate passage data from transportation IC cards, people counting by image analysis from surveillance cameras, pedestrian and vehicle passage data from road sensors, and location-information-attached posts on SNS. For example, it can detect a sudden increase in pedestrian flow on major arterial roads in real time, identify the occurrence of entry restrictions near station ticket gates, detect a decrease in traffic speed on specific routes such as bridges and underpasses, and analyze the degree of congestion around plazas and evacuation centers where people tend to congregate during disasters in a time-series format. The Transportation Information Acquisition Unit acquires operational status, suspended sections, delays, availability of alternative transportation, and estimated resumption times for public transportation such as railways, buses, taxis, shared bicycles, and car-sharing services from APIs. Furthermore, it acquires traffic regulation information such as road closures due to flooding or landslides, alternating one-way traffic, and restrictions for emergency vehicles from announcements by road traffic information centers, local governments, and police. This unit can provide detailed information on, for example, the complete suspension or partial operation of railway lines, the operation of extra bus services or increased waiting times due to concentrated dispatch, road closures due to flooding or collapses, and traffic signal malfunctions and traffic control personnel deployment at major intersections. The SNS Information Acquisition Department collects disaster-related keywords and location-based posts from social media, and uses generative AI-powered natural language processing to extract information on passability, danger, congestion levels, the level of congestion at evacuation centers, and real-time changes in the situation on site. For example, it analyzes posts such as "The roads are flooded and impassable," "The area in front of the station is overflowing with people," "There are still vacancies in the gymnasium," "The bridge is blocked off by the police," "Temporary toilets have been set up in the park," "The underpass is flooded," and "Extra buses are running," extracting firsthand accounts from the field that cannot be grasped from official information, and can also perform reliability assessments and determine the freshness of the information.The facility information acquisition department acquires information on the availability, usability, capacity, and facilities (toilets, showers, power, Wi-Fi, drinking water, food stockpiles, nursing rooms, barrier-free access, etc.) of evacuation centers, temporary accommodation facilities, and related facilities. For example, it can collect detailed information such as whether a gymnasium is unusable due to reaching its capacity, the availability of community centers, the availability of some hotel rooms, whether karaoke boxes are available for nighttime use, whether internet cafes are fully booked, whether family restaurants are temporarily closed, and the operating status and drinking water inventory of convenience stores. The generation AI processing unit comprehensively analyzes this diverse information, taking into account the reliability and freshness of each piece of information, deduction of duplicate information sources, trend analysis of time-series changes, user attributes and preferences, and big data and simulation results from past disasters to generate the optimal route and means of returning home. For example, for elderly people who have difficulty walking, it prioritizes barrier-free routes, routes with few steps, and stations with elevators, and recommends the use of temporary rest facilities along the way. For people with children, it prioritizes routes with many toilets and rest areas, and safe roads with nighttime lighting. In rainy weather, it prioritizes covered routes such as arcades and underpasses. In cases of high congestion, it suggests routes to avoid congestion and temporary shelters. If a user wishes to eat along the way, it can also suggest stores along the route that can be visited, taking into account the location information, congestion status, and inventory information of open restaurants and convenience stores. The route generation unit generates action guidelines for each user, combining the optimal return route, means of transportation, suggestions for temporary resting places along the way, information on related facilities that can be visited, and routes to avoid dangerous or crowded areas, based on the results analyzed by the generation AI. For example, it can provide detailed route suggestions that combine multiple means of transportation and facilities, such as "Walk north along the main road from your current location, wait temporarily at the gymnasium along the way, then take a special bus to the station, and board the train as soon as it resumes operation to return home," "The road is impassable due to flooding, but the underpass is passable although crowded, so take a break at the community center along the way and wait for the congestion to subside," or "Use the pedestrian overpass with elevators as a barrier-free route, take a meal break at a family restaurant along the way, and return home from the nearest taxi stand."The Database Management Department stores and manages various acquired information, past disaster data, user attribute information, facility history information, transportation recovery patterns, and SNS posting history in chronological order, and provides it in a format that can be referenced by generative AI and route generation departments. For example, a generative AI that has learned from past typhoon disaster data on human flow, transportation recovery patterns, evacuation center congestion history, and SNS posting trends can predict future congestion and recovery prospects, and provide users with predictive information such as "railway service is expected to resume in 30 minutes" or "evacuation centers are likely to be full within an hour." The user interface unit provides map display, route guidance, voice guidance, and notification functions on the terminal, enabling users to easily understand the situation and take action. For example, it can color-code dangerous or congested areas on the map, provide voice guidance such as "There is a flooded area 200 meters after turning right. Turn left to avoid it," and immediately notify users via push notifications or vibration if there are suggestions for route changes or temporary accommodations. It can also send information from the terminal to the server when users post on social media or report from the site, contributing to the overall improvement of information accuracy. The location information acquisition unit acquires the user's current location with high accuracy using the terminal's GPS, base station information, Wi-Fi positioning, etc. For example, it can improve location accuracy by using GPS positioning outdoors and Wi-Fi or Bluetooth beacons in combination indoors or underground. In the event of a disaster when it is difficult to acquire location information, it can estimate the location from the user's manual input or past movement history. The notification and display unit is a part of the device that visually or audibly notifies and displays information such as route guidance, warnings, facility congestion status, and information on the resumption of public transportation services. For example, it can display information such as "The road ahead is closed," "The shelter is full," or "An extra bus is departing" as a pop-up on the screen, or it can issue emergency warnings by voice or alert the user with vibration.The user input section is where users input their destination address, desired conditions, attribute information (elderly, disabled, traveling with children, traveling with pets, etc.), current health condition, availability of transportation, and desired stops along the way (meals, restrooms, breaks, charging, etc.). It offers a variety of input methods, such as voice input, touch panel input, and multiple-choice questionnaire input, allowing for a detailed understanding of the user's situation and preferences.

[0044] Specific examples of prompt statements to be fed into the generative AI when implementing the present invention include: "Based on the following information, propose the optimal route and means for the user to return home safely and efficiently from their current location. Pedestrian flow data: Major road A is congested, underpass B has available space. Transportation information: Railway C is suspended, bus D is partially operating, taxi E is experiencing high demand. SNS information: Bridge F is closed, gymnasium G has available space, road H is flooded. Facility information: Community center I has available space, hotel J is fully booked. User attributes: elderly, has difficulty walking, desires barrier-free access. Desired conditions: wants to return home as soon as possible, desires to rest along the way. Analyze this information comprehensively and generate action guidelines including the optimal return route, means, suggestions for temporary accommodation, and measures to avoid dangerous areas." Furthermore, by providing the generative AI with a variety of prompts tailored to the situation, user attributes, and desired conditions, such as "If a user wishes to eat along the way, suggest restaurants or convenience stores along the route that are open and not crowded," "Extract information on passability and dangers in the area from SNS posts, compare it with official information to evaluate its reliability, and reflect this in route selection," and "If multiple users use the same route simultaneously, predict congestion levels and guide them to distribute their routes, then re-suggest a route that is optimal for the overall group," it becomes possible to realize detailed and practical assistance for people returning home.

[0045] (Implementation Steps)

[0046] Step 1: Obtaining user information and desired conditions Users use their devices to obtain their current location information via GPS or Wi-Fi positioning, and input their destination address and desired conditions (e.g., want to get home as quickly as possible, prioritize safe routes, avoid crowds, want places to rest along the way, want barrier-free routes, want pet-friendly routes, etc.). The device then transmits this information to the server. Users can also input their attributes (elderly, disabled, with children, difficulty walking, etc.), health condition, availability of transportation, and desired stops along the way (meals, restrooms, breaks, charging, etc.). A variety of input methods are provided, including voice input and multiple-choice questionnaires.

[0047] Step 2: Gathering information on pedestrian flow, transportation, facilities, and social media. Each information acquisition unit on the server side collects pedestrian flow data from anonymous location information of mobile devices, ticket gate passage data from transportation IC cards, image analysis from surveillance cameras, road sensors, and location-tagged posts on social media. In addition, it acquires operational status, suspended sections, delays, availability of alternative transportation, and estimated resumption times from APIs for public transportation such as railways, buses, taxis, shared bicycles, and car sharing. It also acquires traffic restriction information such as road closures due to flooding or landslides, alternating one-way traffic, and restrictions for emergency vehicles from announcements by road traffic information centers, local governments, and police. Furthermore, it collects information on the availability, usability, capacity, and facilities (toilets, showers, power, Wi-Fi, drinking water, food stockpiles, nursing rooms, barrier-free access, etc.) of evacuation centers, temporary accommodation facilities, and related facilities. From social media, it collects disaster-related keywords and location-tagged posts to extract information on local passability, danger information, congestion levels, evacuation center congestion, and real-time changes in the situation on site.

[0048] Step 3: Information integration and analysis using generative AI The generative AI processing unit comprehensively analyzes collected pedestrian flow data, transportation information, facility information, social media information, user attributes and preferences, and big data and simulation results from past disasters. The generative AI considers the reliability and freshness of each piece of information, eliminates duplicate information sources, analyzes time-series trends, and takes into account user attributes and preferences to generate the optimal route and means of returning home. For example, for elderly people who have difficulty walking, it prioritizes barrier-free routes, routes with few steps, and stations with elevators, and recommends the use of temporary resting facilities along the way. For people with children, it prioritizes routes with many toilets and rest areas, and safe roads with nighttime lighting. In rainy weather, it prioritizes covered routes such as arcades and underpasses. If congestion is high, it suggests routes to avoid congestion and temporary shelters. Specific examples of prompts to be fed into the generating AI include: "Based on the following information, suggest the optimal route and means for the user to return home safely and efficiently from their current location. Pedestrian flow data: Major road A is congested, underpass B has available space. Transportation information: Railway C is suspended, bus D is partially operating, taxi E is experiencing high demand. SNS information: Bridge F is closed, gymnasium G has available space, road H is flooded. Facility information: Community center I has available space, hotel J is fully booked. User attributes: elderly, has difficulty walking, desires barrier-free access. Desired conditions: wants to return home as soon as possible, desires to rest along the way. Analyze this information comprehensively and generate action guidelines including the optimal return route, means, suggestions for temporary accommodation, and measures to avoid dangerous areas."

[0049] Step 4: Generating optimal route, means, and facility proposals The route generation unit generates action guidelines for each user, combining the optimal return route, means of transportation, suggestions for temporary resting places along the way, information on related facilities that can be visited, and routes to avoid dangerous or crowded areas, based on the analysis results of the generating AI. For example, it provides detailed route suggestions that combine multiple means of transportation and facilities, such as "Walk north along the main road from your current location, wait temporarily at the gymnasium along the way, then take a special bus to the station, and board the train as soon as it resumes operation to return home," "The road is impassable due to flooding, but the underpass is passable although crowded, so take a break at the community center along the way and wait for the congestion to subside," or "Use the pedestrian overpass with elevators as a barrier-free route, take a meal break at a family restaurant along the way, and return home from the nearest taxi stand."

[0050] Step 5: Notification and display to user devices and feedback acquisition The user interface provides map display, route guidance, voice guidance, and notification functions, enabling users to easily understand the situation and take action. Dangerous and congested areas along the route are color-coded on the map, and voice guidance such as "There is a flooded area 200 meters after turning right. Turn left to avoid it" is provided. Push notifications and vibrations are also used to immediately inform users of route changes or suggestions for temporary accommodations. When users post on social media or report from the site themselves, the information is sent from the terminal to the server, contributing to the improvement of overall information accuracy. User feedback and on-site reports are again analyzed by the generation AI and used to optimize routes and suggestions in real time.

[0051] Step 6: Real-time response to changing circumstances and reanalysis During a disaster, the situation changes rapidly, so the server-side information acquisition and AI processing units periodically or triggered by events to reacquire information and re-evaluate routes and means of transportation. For example, if a road on the recommended route becomes newly closed, an alternative route is immediately proposed and notified to the user's terminal. If an evacuation center becomes full, other available facilities are suggested. If there is information on social media that "the road has reopened," it is cross-referenced with official information and, if reliable, reflected in the route. If the user wishes to change their route or stay temporarily along the way, the AI ​​generates a new optimal route immediately and presents it to the terminal. Specific examples of prompts to be fed into the AI ​​include: "If a road on the recommended route becomes newly closed while the user is traveling from their current location to their destination, immediately propose alternative routes and means of transportation based on the latest pedestrian flow data, transportation information, social media information, and facility information. Consider the user's attributes and desired conditions, and generate action guidelines that balance danger avoidance with efficient return home."

[0052] (Specific use cases)

[0053] For example, imagine a scenario where a large-scale earthquake occurs, causing widespread disruption to public transportation such as trains and buses in urban areas, and making some roads impassable due to landslides, flooding, and traffic restrictions. In this case, users would use a terminal to obtain their current location via GPS and input their desired conditions along with their destination address, such as "I want to get home as quickly as possible," "I want to prioritize a safe route," "I would like a place to rest along the way," and "I would like a barrier-free route." A variety of user demographics are anticipated, including the elderly, those with mobility difficulties, those traveling with children, those with pets, pregnant women, and people with disabilities. The terminal would then transmit this information to a server.

[0054] On the server side, the pedestrian flow information acquisition unit collects real-time congestion and pedestrian flow data around major arterial roads, bridges, underpasses, plazas, and evacuation centers from anonymous location information of mobile devices, data from ticket gate passages of transportation IC cards, image analysis from surveillance cameras, road sensors, and location-tagged posts on social media. The transportation information acquisition unit obtains information on suspended sections and expected resumption of service from railway and bus operation information APIs, taxi and shared bicycle dispatch status, and traffic restriction information such as road flooding, landslides, road closures, alternating one-way traffic, and restrictions for emergency vehicles from announcements by road traffic information centers, local governments, and police. The facility information acquisition unit collects information on the availability, usability, capacity, and facilities (toilets, showers, power, Wi-Fi, drinking water, food stockpiles, nursing rooms, barrier-free access, etc.) of evacuation centers, temporary accommodation facilities, and related facilities (gymnasiums, community centers, schools, hotels, karaoke boxes, internet cafes, family restaurants, convenience stores, etc.). The SNS information acquisition unit collects disaster-related keywords and posts with location information, and uses natural language processing by generation AI to extract information on whether areas are passable, danger information, congestion levels, the level of congestion at evacuation centers, and real-time changes in the situation on site.

[0055] The AI ​​processing unit comprehensively analyzes this diverse information, taking into account the reliability and freshness of each piece of information, deduplicating information sources, analyzing time-series trends, user attributes and preferences, and big data and simulation results from past disasters, to generate the optimal route and means of returning home. For example, for elderly people who have difficulty walking, it prioritizes barrier-free routes, routes with few steps, and stations with elevators, and recommends the use of temporary shelters along the way. For those with children, it prioritizes routes with many toilets and rest areas, and safe roads with nighttime lighting. In rainy weather, it prioritizes covered routes such as shopping arcades and underpasses. If congestion is high, it suggests routes to avoid congestion and temporary shelters. For example, if a major road is flooded, it suggests a route via an underpass or elevated walkway, recommends temporary waiting at a gymnasium or community center along the way, and encourages returning home when congestion has eased. If extra bus services are running, it combines walking routes to the nearest bus stop with bus boarding information. If evacuation centers are full, it suggests using other vacant facilities, such as hotels, karaoke boxes, or internet cafes that are open. If users wish to stop for a meal or restroom break along the way, restaurants and convenience stores that are open and not crowded will be incorporated into the route.

[0056] The route generation unit generates action plans for each user, combining the optimal return route, means of transportation, suggestions for temporary resting places along the way, information on related facilities that can be visited, and routes to avoid dangerous or crowded areas, based on the analysis results of the generating AI. For example, it provides detailed route suggestions that combine multiple means of transportation and facilities, such as "Walk north along the main road from your current location, wait temporarily at the gymnasium along the way, then take a special bus to the station, and board the train as soon as it resumes operation to return home," "The road is impassable due to flooding, but the underpass is passable although crowded, so take a break at the community center along the way and wait for the congestion to subside," or "Use the pedestrian overpass with elevators as a barrier-free route, take a meal break at a family restaurant along the way, and return home from the nearest taxi stand."

[0057] The user interface provides map display, route guidance, voice guidance, and notification functions, enabling users to easily understand the situation and take action. Dangerous and congested areas along the route are color-coded on the map, and voice guidance such as "There is a flooded area 200 meters after turning right. Turn left to avoid it" is provided. Push notifications and vibrations are also used to immediately inform users of route changes or suggestions for temporary accommodations. When users themselves post on social media or report from the scene, the information is sent from the terminal to the server, contributing to the improvement of overall information accuracy. User feedback and on-site reports are again analyzed by the generation AI and used to optimize routes and suggestions in real time.

[0058] Specific examples of prompts to be fed into the generating AI include: "Based on the following information, suggest the optimal route and means for the user to return home safely and efficiently from their current location. Pedestrian flow data: Major road A is congested, underpass B has available space. Transportation information: Railway C is suspended, bus D is partially operating, taxi E is experiencing high demand. SNS information: Bridge F is closed, gymnasium G has available space, road H is flooded. Facility information: Community center I has available space, hotel J is fully booked. User attributes: elderly, has difficulty walking, desires barrier-free access. Desired conditions: wants to return home as soon as possible, desires to rest along the way. Analyze this information comprehensively and generate action guidelines including the optimal return route, means, suggestions for temporary accommodation, and measures to avoid dangerous areas." Furthermore, by providing the AI ​​with a variety of prompts tailored to the situation, user attributes, and desired conditions, such as "If a user wishes to eat along the way, suggest restaurants or convenience stores along the route that are open and not crowded," "Extract information on passability and dangers in the area from SNS posts, compare it with official information to evaluate its reliability, and reflect this in route selection," and "If multiple users use the same route simultaneously, predict congestion levels and guide them to distribute their routes, then re-suggest a route that is optimal for the overall group," it becomes possible to realize detailed and practical assistance for people returning home.

[0059] (Application Example 1) The flow of a specific process in Application Example 1 will be explained below. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] (Embodiments for carrying out the present invention)

[0061] Embodiments for carrying out the present invention will be described in more specific and detail. The urban disaster response support system of the present invention is equipped with an extremely wide range of functional units, including an urban information acquisition unit, an infrastructure information acquisition unit, a generative AI analysis unit, a user interface unit, a notification / display unit, a database management unit, a location information acquisition unit, a feedback collection unit, an attribute information management unit, a priority determination unit, and a resource optimization unit. Each of these units is configured to be flexibly expandable and linked according to the size of the city, the type of disaster, the number of users, and the diversity of information sources.

[0062] The Urban Information Acquisition Unit collects diverse information in real time across the entire city, including pedestrian flow data, public transport operation status, road traffic information, availability of evacuation centers and temporary shelters, operational status of medical facilities, environmental data from IoT sensors, and on-site reports extracted from social media. For example, it acquires data on pedestrian and vehicle flow, dwell time, movement speed, congestion levels, and anomaly detection (falls, tumbles, crowd congestion, wrong-way driving, fire outbreaks, etc.) from cameras, infrared sensors, traffic counters, beacons, drones, smart streetlights, smart traffic lights, smart bus stops, smart trash cans, etc., installed in all areas of the city, such as major roads, underpasses, bridges, squares, around train stations, commercial facilities, residential areas, industrial zones, tourist spots, and event venues. For example, detailed situations such as pedestrian traffic being eight times higher than normal on major road A, underpass B being temporarily closed, bridge C being restricted due to deterioration, plaza D being overcrowded with evacuees, station E having entry restrictions at the ticket gates, commercial facility F being temporarily closed, some roads collapsing in residential area G, warnings of hazardous material leaks in industrial area H, frequent congestion of foreign tourists in tourist area I, and increased risk due to crowd density at event venue J are being monitored in real time.

[0063] Regarding the status of public transportation operations, the system retrieves operational information from APIs for all modes of transportation within the city, including trains, buses, trams, monorails, subways, bicycle sharing, car sharing, taxis, ride sharing, and on-demand transportation. This information includes operational status, suspended sections, delays, extra services, dispatch status, passenger numbers, vehicle location, estimated recovery time, and priority transport targets (healthcare workers, people with disabilities, pregnant women, people with infants, people requiring care, etc.). For example, it collects detailed operational information such as: train line K is completely suspended; bus L is operating only on some sections; taxi M has a waiting time of over 60 minutes due to high demand; bicycle sharing N is not available at port O; car sharing P has gasoline supply restrictions; ride sharing Q has increased fares during disasters; and on-demand transportation R is prioritizing the elderly.

[0064] Road traffic information is obtained from road traffic information centers, local governments, police, fire departments, construction companies, resident reports, SNS posts, and IoT sensors (road surface temperature, vibration, flooding, sinkholes, rockfalls, landslides, snow accumulation, freezing, fires, gas leaks, hazardous material leaks, etc.) to acquire information such as road closures, alternating one-way traffic, restrictions for emergency vehicles only, vehicle entry bans, pedestrian-only areas, barrier-free routes, temporary bridge installations, temporary road openings, traffic light malfunctions, traffic controller deployments, and road restoration progress. For example, detailed road conditions such as road S being closed due to flooding, road T having alternating one-way traffic due to a landslide, road U being for emergency vehicles only, road V having a temporary bridge installed, road W having a traffic light malfunction, road X having increased traffic controllers, road Y undergoing restoration work, and road Z having no nighttime lighting can be grasped in real time.

[0065] Regarding the availability of evacuation shelters and temporary accommodation facilities, real-time information will be obtained on the availability, capacity, and facilities (toilets, showers, power outlets, Wi-Fi, drinking water, food stockpiles, nursing rooms, barrier-free access, pet-friendly options, foreign language support, on-site medical staff, infection control measures, privacy, heating and air conditioning, charging facilities, laundry facilities, children's spaces, women-only spaces, LGBTQ-friendly spaces, etc.) of all facilities within the city. For example, we will collect detailed information such as: Gymnasium AA is unavailable due to reaching capacity; Community Center BB has vacancies; Hotel CC has only some rooms available; Karaoke Box DD is open for nighttime use; Internet Cafe EE is full; Family Restaurant FF is temporarily closed; Convenience Store GG is open and has drinking water in stock; Shopping Mall HH has opened as a temporary shelter; Temple II is accepting foreign tourists; Temporary Housing JJ prioritizes the elderly; and Tent Village KK allows pets.

[0066] Regarding the operational status of medical facilities, the system will acquire in real time information such as the availability of hospitals, clinics, emergency hospitals, disaster base hospitals, temporary medical facilities, pharmacies, home nursing stations, dental clinics, and veterinary hospitals, the types of illnesses and injuries that can be treated, the availability of emergency response, the availability of medical staff, the inventory of pharmaceuticals and medical equipment, infection control measures, foreign language support, barrier-free access, means of transportation (ambulance, doctor helicopter, drone transport), medical collaboration systems, and the availability of telemedicine. For example, it will provide detailed information on medical resources such as: Hospital LL is available to accept patients; Clinic MM is at full capacity and cannot accept patients; Emergency Hospital NN prioritizes severely ill patients; Temporary Medical Facility OO is for mildly ill patients only; Pharmacy PP has insufficient stock; Home Nursing Station QQ is currently visiting elderly people's homes; Dental Clinic RR is only providing emergency treatment; and Veterinary Hospital SS prioritizes evacuees with pets.

[0067] Regarding environmental data from IoT sensors, data from a variety of sensors is acquired in real time, including urban air quality, radiation, river water levels, ground deformation, temperature, humidity, noise, vibration, PM2.5, pollen, volcanic ash, ultraviolet radiation, CO2 concentration, odor, rainfall, wind speed, wind direction, lightning, tsunami warnings, seismometers, aftershock predictions, fire alarms, gas leak detection, hazardous material leak detection, animal intrusion detection, and drone surveillance footage. For example, detailed environmental information can be obtained such as rising river water levels increasing the risk of flooding, good air quality, normal radiation levels, observed ground deformation increasing the risk of aftershocks, increased PM2.5 concentration increasing the risk of respiratory illness, volcanic ash dispersion restricting outdoor activities, tsunami warnings issued, fire alarms activated, gas leaks detected, crop damage due to animal intrusion, and landslide monitoring via drone footage.

[0068] For on-site reports extracted from social media, the system collects and analyzes information from diverse sources such as local residents, tourists, government officials, media outlets, volunteers, and NPOs. This includes disaster-related posts and on-site reports, image / video / audio posts, posts with location information, disaster hashtags, foreign language posts, AI automatic translation, reliability assessment, information freshness determination, misinformation detection, and cross-referencing with official information. For example, it extracts firsthand accounts and image / video evidence such as "Road TT is flooded and impassable," "Shelter UU has vacancies," "Hospital VV is crowded," "Underpass WW is flooded," "Bus XX is operating extra services," "Hotel YY can accept foreign tourists," "Facility ZZ that allows pets is full," "Temporary housing AAA prioritizes the elderly," "Tent village BBB is strengthening infectious disease control measures," and "Landslide confirmed with drone footage CCC," and the AI ​​automatically determines its reliability and freshness.

[0069] The generation AI analysis unit comprehensively and dynamically analyzes all of this information, taking into account user attribute information (elderly, disabled, with children, pregnant, foreign tourist, with pet, LGBTQ, presence of pre-existing conditions / allergies, restrictions on means of transportation, language ability, IT literacy, disaster experience, evacuation behavior characteristics, etc.), individual preferences (desire for barrier-free routes, priority for medical facilities, desire for pet-friendly routes, desire for rest stops along the way, desire for meals / toilets / charging / Wi-Fi / privacy, emphasis on infectious disease control, desire for women-only spaces, desire for routes with nighttime lighting, desire for foreign language support, desire for religious considerations, desire for LGBTQ considerations, etc.), real-time conditions in the city during a disaster, big data and simulation results from past disasters, evacuation orders and advisories from the government, prospects for the restoration of urban infrastructure, resource distribution, secondary disaster risk, and optimization of evacuation guidance for the entire city. Based on this comprehensive analysis, it generates and presents to the terminal in real time individually optimized evacuation routes, shelters, medical facilities, necessary preparations, danger avoidance measures, resource distribution, urban function maintenance measures, and secondary disaster prevention measures for each user.

[0070] For example, for elderly people using wheelchairs, we would propose a combination of services including barrier-free routes without steps, pedestrian overpasses with elevators, locations with temporary ramps, barrier-free shelters, facilities with resident medical staff, facilities equipped with toilets, showers, and charging facilities, routes with nighttime lighting, guidance on wheelchair-accessible taxis and on-demand transportation, dispatch of care staff, support for daily life after arriving at the shelter, and arrangements for transportation to medical facilities. For people with children or pregnant women, we would prioritize guiding them to facilities with nursing rooms, diaper changing stations, children's spaces, women-only spaces, facilities with enhanced infection control measures, food stockpiles, milk, stroller-friendly routes, routes with nighttime lighting, safe play areas, facilities with resident childcare workers, and medical facilities that accept maternal and child health handbooks. For foreign tourists, we would propose a combination of services including shelters with foreign language support, multilingual signage, AI automatic translation, facilities with religious considerations, placement of foreign volunteers, Wi-Fi access, collaboration with tourist information centers, embassy contact information, emergency interpretation services, and collaboration with NPOs that support foreigners during disasters. For those traveling with pets, we will provide information on pet-friendly shelters, animal hospitals, pet food stockpiles, pet toilets, pet cage rentals, designated pet-friendly areas, rules for evacuating with pets, and collaborations with animal volunteers.

[0071] To distribute resources throughout the city, congestion predictions and guidance will be implemented to prevent overcrowding in specific shelters and routes. For example, "If everyone continues to use route CCC, congestion will worsen, so route DDD will be recommended for some users," "Shelter EEE is expected to be full, so guidance to shelter FFF will be strengthened," "Medical facility GGG is full with severely ill patients, so those with mild symptoms will be dispersed to medical facility HHH," "Extra bus III services will prioritize the elderly and disabled, while those who can walk will use walking route JJJ," and "Temporary housing KKK will prioritize the elderly and those requiring care, while tent village LLL will prioritize young people and healthy individuals." This will achieve dynamic optimization that maximizes the safety and efficiency of the entire city.

[0072] The user interface section provides a variety of functions, including map display, route guidance, voice guidance, notification functions, chatbot, AI voice assistant, AR navigation, vibration notifications, emergency warnings, push notifications, customized display according to user attributes, voice guidance for the visually impaired, vibration notifications for the hearing impaired, foreign language display, pictogram display, disaster FAQ, evacuation center congestion heat map, medical facility congestion graph, real-time display of on-site SNS reports, and a feedback input screen.

[0073] The notification and display unit visually, audibly, and tactilely notifies and displays information such as route guidance, warnings, facility congestion status, resumption of public transportation services, infrastructure restoration information, government announcements, SNS reports from the field, AI prediction information, arrival reports at evacuation centers, medical facility acceptance status, resource dispersion status, secondary disaster warnings, infectious disease outbreak warnings, heatstroke warnings, cold wave warnings, storm warnings, tsunami warnings, volcanic ash warnings, PM2.5 warnings, pollen warnings, thunderstorm warnings, and animal sighting warnings on the terminal.

[0074] The database management unit stores and manages various acquired information, past disaster data, user attribute information, facility history information, transportation recovery patterns, SNS posting history, IoT sensor history, medical institution operation history, evacuation center congestion history, government announcement history, AI analysis result history, resource distribution history, secondary disaster occurrence history, etc., in chronological order, and provides them in a format that can be referenced by generational AI and route generation units.

[0075] The location information acquisition unit acquires the user's current location with high accuracy using a variety of methods, including the terminal's GPS, base station information, Wi-Fi positioning, Bluetooth beacon, indoor positioning, drone positioning, smart street light positioning, smart traffic light positioning, AR marker positioning, manual input, and estimation of past movement history.

[0076] The Feedback Collection Department collects on-site reports from users, route usage status, shelter arrival reports, facility usage status, SNS posts, image / video / audio posts, AI automatic translation feedback, medical consultation reports, resource usage reports, on-site reports from government officials, volunteers, and NPOs, and conversation history with AI chatbots, and provides this information to the Generative AI Analysis Department in real time.

[0077] The Attribute Information Management Department manages user information such as age, gender, disability status, pregnancy / breastfeeding status, presence of children, presence of pets, foreign language proficiency, religious / cultural considerations, LGBTQ status, chronic illnesses / allergies, IT literacy, disaster experience, evacuation behavior characteristics, and priority assessment results, using this as basic data for individual optimization.

[0078] The priority determination unit comprehensively evaluates factors such as the user's life and health risks, disaster type, secondary disaster risk, infrastructure failure, strain on medical resources, shelter congestion, government directives, priority given to vulnerable groups, resource dispersion, and overall urban safety and efficiency, and dynamically determines the priority of each user, facility, route, and resource.

[0079] The Resource Optimization Unit uses AI to automatically optimize the distribution, dispersion, guidance, coordination, and reallocation of all resources throughout the city, including shelters, medical facilities, transportation, infrastructure, supplies, volunteers, government officials, NPOs, private businesses, residents, tourists, animals, information, communications, energy, space, and time.

[0080] Thus, the embodiments of the present invention are highly advanced and multifunctional systems that optimize disaster response across an entire city in real time and on an individual basis, ensuring the safety of residents and visitors, maintaining urban functions, preventing secondary disasters, efficiently distributing resources, prioritizing the protection of vulnerable groups, strengthening cooperation among government, private sector, residents, and volunteers, improving the overall resilience of the city, and optimizing urban management during disasters.

[0081] (System Configuration)

[0082] The system according to this embodiment includes an urban information acquisition unit, an infrastructure information acquisition unit, a generative AI analysis unit, a user interface unit, a notification / display unit, a database management unit, a location information acquisition unit, a feedback collection unit, an attribute information management unit, a priority determination unit, and a resource optimization unit. The urban information acquisition unit is a unit that collects data in real time such as pedestrian flow data within the city, public transportation operation status, road traffic information, availability of evacuation shelters and temporary accommodation facilities, operating status of medical institutions, environmental data from IoT sensors, and on-site reports extracted from SNS. For example, it collects data on pedestrian and vehicle flow, dwell time, and movement from cameras, infrared sensors, traffic counters, beacons, drones, smart streetlights, smart traffic lights, smart bus stops, smart trash cans, etc., installed in major roads, underpasses, bridges, squares, around stations, commercial facilities, residential areas, industrial zones, tourist spots, event venues, etc. The system acquires speed, congestion levels, and anomaly detection data, allowing for real-time monitoring of detailed conditions in all areas of the city. For example, if pedestrian traffic on major road A is eight times the normal rate, if underpass B is temporarily closed, if bridge C is closed due to deterioration, if plaza D is overcrowded with evacuees, if entry is restricted at station E's ticket gates, if commercial facility F is temporarily closed, if some roads in residential area G collapse, if a hazardous material leak warning is issued in industrial area H, if there is a high concentration of foreign tourists in tourist area I, or if the risk of crowding increases at event venue J.

[0083] The Infrastructure Information Acquisition Unit acquires information on the operational status and expected recovery times of infrastructure such as electricity, water, gas, and communications within the city, as well as information on infrastructure failures during disasters. For example, if water is partially shut off in Area 1, electricity and gas are normal, but communications are congested; if there is a power outage in Area 2, and gas is expected to be restored in 3 hours; or if communication infrastructure is temporarily cut off in Area 3, the unit acquires detailed information on the location of the infrastructure failure, the progress of restoration work, priority areas for lifeline restoration as announced by the government, and the status of emergency vehicle-only roads.

[0084] The Generative AI Analysis Unit dynamically and comprehensively analyzes various types of information acquired from the Urban Information Acquisition Unit and the Infrastructure Information Acquisition Unit using generative AI to generate and present in real time the optimal evacuation routes, shelters, medical facilities, routes to avoid, necessary preparations, action guidelines for maintaining urban functions, resource distribution, and measures to prevent secondary disasters for each user within the city. For example, for elderly wheelchair users, it provides information such as barrier-free routes without steps, pedestrian bridges with elevators, locations with temporary ramps, barrier-free shelters, facilities with resident medical staff, facilities equipped with toilets, showers, and charging facilities, routes with nighttime lighting, guidance on using wheelchair-accessible taxis and on-demand transportation, dispatch of care staff, life support after arrival at shelters, and arrangements for transportation to medical facilities. We will propose a combination of services, prioritizing guidance for those with children or pregnant women to nursing rooms, diaper changing stations, children's spaces, women-only spaces, facilities with enhanced infection control measures, food stockpiles, milk, stroller-friendly routes, nighttime-lit routes, safe playgrounds, facilities with resident childcare workers, and medical institutions that accept maternal and child health handbooks. For foreign tourists, we will provide a combination of services including evacuation shelters with foreign language support, multilingual signage, AI automatic translation, facilities with religious considerations, foreign volunteers, Wi-Fi access, collaboration with tourist information centers, embassy contact information, emergency interpretation services, and collaboration with NPOs that support foreigners during disasters. For those with pets, we will provide guidance on evacuation shelters that allow pets, animal hospitals, pet food stockpiles, pet toilets, pet cage rentals, pet-friendly spaces, rules for evacuation with pets, and collaboration with animal volunteers.

[0085] The user interface section provides a variety of functions, including map display, route guidance, voice guidance, notification functions, chatbot, AI voice assistant, AR navigation, vibration notifications, emergency warnings, push notifications, customized display according to user attributes, voice guidance for the visually impaired, vibration notifications for the hearing impaired, foreign language display, pictogram display, disaster FAQ, evacuation center congestion heatmap, medical facility congestion graph, real-time display of SNS on-site reports, and a feedback input screen. For example, it displays dangerous and congested areas on the route in different colors on the map, provides voice guidance such as "There is a flooded area 200 meters after turning right. Please turn left to avoid it," and immediately notifies users via push notifications or vibration if there are suggestions for route changes or temporary accommodations. When users themselves post on SNS or report on-site, the information is sent from the terminal to the server, contributing to the overall improvement of information accuracy.

[0086] The notification and display unit is responsible for visually, audibly, and tactilely notifying and displaying information such as route guidance, warnings, facility congestion status, resumption of public transportation services, infrastructure restoration information, government announcements, SNS reports from the field, AI prediction information, arrival reports at evacuation centers, medical facility acceptance status, resource dispersion status, secondary disaster warnings, infectious disease outbreak warnings, heatstroke warnings, cold wave warnings, storm warnings, tsunami warnings, volcanic ash warnings, PM2.5 warnings, pollen warnings, thunderstorm warnings, and animal sighting warnings on the terminal. For example, it may display information such as "The road ahead is closed," "The evacuation center is full," or "An extra bus is departing" as a pop-up on the screen, issue emergency warnings by voice, and provide attention through vibration.

[0087] The Database Management Department stores and manages various acquired information, past disaster data, user attribute information, facility history information, transportation recovery patterns, SNS posting history, IoT sensor history, medical institution operation history, evacuation center congestion history, government announcement history, AI analysis result history, resource distribution history, and secondary disaster occurrence history in chronological order, and provides this information in a format that can be referenced by generative AI and route generation departments. For example, generative AI that has learned from past typhoon disaster data on human flow, transportation recovery patterns, evacuation center congestion history, and SNS posting trends can predict future congestion and recovery prospects, and provide users with predictive information such as "railway service is expected to resume in 30 minutes" or "evacuation centers are likely to be full within an hour."

[0088] The location information acquisition unit acquires the user's current location with high accuracy using a variety of means, including the terminal's GPS, base station information, Wi-Fi positioning, Bluetooth beacons, indoor positioning, drone positioning, smart street light positioning, smart traffic light positioning, AR marker positioning, manual input, and estimation of past movement history. For example, it uses GPS positioning outdoors, and combines Wi-Fi and Bluetooth beacons indoors and in underground areas to improve positional accuracy. In the event of a disaster when acquiring location information is difficult, it estimates the location from the user's manual input or past movement history.

[0089] The Feedback Collection Department collects on-site reports from users, route usage status, shelter arrival reports, facility usage status, SNS posts, image / video / audio posts, AI automatic translation feedback, medical visit reports, resource usage reports, on-site reports from government officials, volunteers, and NPOs, and conversation history with AI chatbots, and provides this information to the Generative AI Analysis Department in real time. For example, if a user posts on-site information such as "Road P was actually passable" or "Shelter Q had vacancies," the Generative AI immediately analyzes this and reflects it in route suggestions for other users.

[0090] The Attribute Information Management Department manages user information such as age, gender, disability status, pregnancy / breastfeeding status, presence of children, presence of pets, foreign language proficiency, religious / cultural considerations, LGBTQ status, chronic illnesses / allergies, IT literacy, disaster experience, evacuation behavior characteristics, and priority assessment results, using this as foundational data for individual optimization. For example, it manages in detail a wide range of attributes such as the elderly, people with disabilities, pregnant women, people with children, people with pets, foreign tourists, LGBTQ individuals, and those with chronic illnesses / allergies.

[0091] The priority determination unit comprehensively evaluates factors such as the life and health risks of users, the type of disaster, the risk of secondary disasters, infrastructure failures, strain on medical resources, congestion at evacuation centers, government directives, priority given to vulnerable groups, resource distribution, and the overall safety and efficiency of the city. It dynamically determines the priority of each user, facility, route, and resource, prioritizing vulnerable groups such as the elderly, people with disabilities, pregnant women, those with infants, those requiring care, people with chronic illnesses, foreign tourists, and those with pets, thereby achieving optimal resource allocation.

[0092] The Resource Optimization Department is a department where AI automatically optimizes the optimal distribution, dispersion, guidance, coordination, coordination, and reallocation of all resources throughout the city, including shelters, medical facilities, transportation, infrastructure, supplies, volunteers, government officials, NPOs, private businesses, residents, tourists, animals, information, communication, energy, space, and time. For example, it predicts congestion and guides people to avoid overconcentration in specific shelters or routes, recommending routes such as "If everyone continues to use route CCC, congestion will worsen, so we recommend route DDD for some users," "Shelter EEE is expected to be full, so we will strengthen guidance to shelter FFF," "Medical facility GGG is full with severely ill patients, so those with mild symptoms will be dispersed to medical facility HHH," "Extra bus III services will prioritize the elderly and disabled, and those who can walk should use walking route JJJ," and "Temporary housing KKK will prioritize the elderly and those requiring care, while tent village LLL will prioritize young people and healthy individuals." This achieves dynamic optimization that maximizes the safety and efficiency of the entire city.

[0093] Specific examples of prompt statements to be fed into the generating AI when implementing the present invention include: "Based on the following urban information, propose the optimal route, shelter, medical facility, route to avoid, and necessary preparations for elderly people in Area 1 to evacuate safely and efficiently. People flow data: Major road A is congested, underpass B has space. Infrastructure information: Water supply partially shut off, power is normal, communication is congested. Transportation information: Train service suspended, buses partially operating, roads partially closed. Shelter information: Shelter X has space, shelter Y is full. Medical facility information: Hospital A can accept patients, clinic B cannot. IoT sensor: River water level is rising. SNS information: Route A is congested, shelter X has space. Analyze this information comprehensively and generate action guidelines including the optimal evacuation route, shelter, medical facility, risk avoidance measures, and necessary preparations."

[0094] (Implementation Steps)

[0095] Step 1: Input user attributes and desired conditions, and obtain location information. Users obtain their current location information using their devices via GPS, Wi-Fi positioning, Bluetooth beacons, and indoor positioning, and input their preferred evacuation destination and priority conditions (e.g., barrier-free routes preferred, priority given to medical facilities, pet-friendly routes preferred, rest stops requested, foreign language support preferred, infectious disease prevention emphasized, women-only spaces preferred, routes with nighttime lighting preferred, etc.). User attributes (elderly, disabled, with children, pregnant, foreign tourist, with pet, LGBTQ, pre-existing medical conditions / allergies, etc.) are also automatically retrieved from information entered on the device or registered on the device.

[0096] Step 2: Real-time collection of urban information, infrastructure information, facility information, social media information, and IoT sensor information. The Urban Information Acquisition Department acquires pedestrian and vehicle flow, dwell time, movement speed, congestion level, and anomaly detection data from cameras, infrared sensors, traffic counters, beacons, drones, smart streetlights, smart traffic lights, smart bus stops, smart trash cans, etc., installed in major roads, underpasses, bridges, squares, around train stations, commercial facilities, residential areas, industrial zones, tourist spots, and event venues. This data is used to acquire real-time information on public transportation operation status, road traffic information, availability of evacuation centers and temporary accommodations, operating status of medical facilities, air quality, radiation, river water levels, ground deformation, temperature, humidity, noise, vibration, PM2.5, pollen, volcanic ash, ultraviolet rays, CO2 concentration, odor, rainfall, wind speed, wind direction, lightning, tsunami warnings, seismometer readings, aftershock predictions, fire alarms, gas leak detection, hazardous material leak detection, animal intrusion detection, and drone surveillance footage. From social media, we collect disaster-related posts, on-site reports, image / video / audio posts, posts with location information, disaster hashtags, foreign language posts, AI automatic translation, reliability evaluation, information freshness assessment, misinformation detection, and cross-referencing with official information to comprehensively understand the situation throughout the city.

[0097] Step 3: Comprehensive analysis and optimization prompt generation using generative AI The Generative AI Analysis Unit comprehensively analyzes all information acquired from the Urban Information Acquisition Unit and the Infrastructure Information Acquisition Unit, user attributes and preferences, big data and simulation results from past disasters, government evacuation orders and advisories, prospects for urban infrastructure recovery, resource distribution status, secondary disaster risk, and optimization of evacuation guidance for the entire city. Based on this analysis, it generates individually optimized evacuation routes, shelters, medical facilities, necessary preparations, risk avoidance measures, resource distribution, urban function maintenance measures, and secondary disaster prevention measures for each user in real time. Specific examples of prompts to be fed into the generating AI include: "Based on the following city information, propose the optimal route, shelters, medical facilities, routes to avoid, and necessary preparations for elderly people in Area 1 to evacuate safely and efficiently. People flow data: Major road A is congested, underpass B has availability. Infrastructure information: Water supply partially shut off, power is normal, communication is congested. Transportation information: Train service suspended, some buses are running, some roads are closed. Shelter information: Shelter X has availability, shelter Y is full. Medical facility information: Hospital A can accept patients, clinic B cannot. IoT sensor: River water level is rising. SNS information: Route A is congested, shelter X has availability. Analyze this information comprehensively and generate action guidelines including the optimal evacuation route, shelters, medical facilities, risk avoidance measures, and necessary preparations."

[0098] Step 4: Presenting optimal evacuation routes, shelters, medical facilities, and resource distribution. Based on the analysis results from the Generative AI Analysis Unit, the system generates action guidelines for each user, combining the optimal evacuation route, shelter, medical facilities, facilities to stop at along the way, necessary preparations, danger avoidance measures, resource allocation, urban function maintenance measures, and secondary disaster prevention measures, and presents them to the terminal through the user interface unit and notification / display unit. For example, it provides detailed evacuation and guidance suggestions that combine multiple means and facilities, such as "Walk north along main road A from your current location, wait temporarily at gymnasium B along the way, then take a special bus C to station D, and from station D, board train E as soon as it resumes operation and go to shelter F," "Road G is impassable due to flooding, underpass H is congested but passable, take a break at community center I along the way and wait for the congestion to ease," and "Use pedestrian overpass J with elevators as a barrier-free route, take a meal break at family restaurant K along the way, and go to medical facility M from the nearest taxi stand L."

[0099] Step 5: Notification and display on user devices and collection of feedback The user interface unit provides a variety of functions, including map display, route guidance, voice guidance, notification functions, chatbot, AI voice assistant, AR navigation, vibration notifications, emergency warnings, push notifications, customized displays based on user attributes, voice guidance for the visually impaired, vibration notifications for the hearing impaired, foreign language display, pictogram display, disaster FAQs, evacuation center congestion heatmaps, medical facility congestion graphs, real-time display of on-site SNS reports, and a feedback input screen, enabling users to easily understand the situation and take action. It collects on-site reports from users, route usage status, evacuation center arrival reports, facility usage status, SNS posts, image / video / audio posts, AI automatic translation feedback, medical facility visit reports, resource usage reports, on-site reports from government officials, volunteers, and NPOs, and conversation history with the AI ​​chatbot, and provides this information to the generative AI analysis unit in real time.

[0100] Step 6: Real-time reanalysis and revision of the situation in response to changing circumstances. During a disaster, the situation changes rapidly, so the urban information acquisition unit, infrastructure information acquisition unit, and generative AI analysis unit periodically or triggered by events to reacquire information and re-evaluate routes and means of transport. For example, if a road on the recommended route becomes newly closed, an alternative route is immediately proposed and notified to the user's terminal. If an evacuation center becomes full, other available facilities are suggested. If there is information on social media that "the road has reopened," it is cross-referenced with official information and, if reliable, reflected in the route. Specific examples of prompts to be fed into the generative AI include: "If a road on the recommended route becomes newly closed while the user is traveling from their current location to their destination, immediately propose alternative routes and means of transport based on the latest pedestrian flow data, transportation information, social media information, and facility information. Consider user attributes and preferences, and generate action guidelines that balance danger avoidance with efficient evacuation."

[0101] (Specific use cases)

[0102] For example, consider a scenario where a large-scale earthquake occurs in a metropolitan area, causing widespread suspension of public transportation such as trains and buses throughout the city, and making some major roads impassable due to collapses, flooding, landslides, fires, gas leaks, hazardous material leaks, etc. In this case, the urban information acquisition unit acquires real-time data on pedestrian and vehicle flow, dwell time, movement speed, congestion levels, and anomaly detection (falls, tumbles, crowd congestion, wrong-way driving, fire outbreaks, etc.) from cameras, infrared sensors, traffic counters, beacons, drones, smart streetlights, smart traffic lights, smart bus stops, smart trash cans, etc. installed in major roads, underpasses, bridges, squares, around stations, commercial facilities, residential areas, industrial zones, tourist spots, event venues, etc. It also acquires operational information APIs for trains, buses, trams, monorails, subways, shared bicycles, car sharing, taxis, ride sharing, and on-demand transportation, including operational status, suspended sections, delays, extra services, dispatch status, number of passengers, vehicle location, estimated recovery time, and priority transport targets. Furthermore, the system acquires information on road closures, alternating one-way traffic, restrictions for emergency vehicles only, vehicle entry bans, pedestrian-only areas, barrier-free routes, temporary bridge installations, temporary road openings, traffic signal malfunctions, traffic controller deployments, and road restoration progress in real time from road traffic information centers, local governments, police, fire departments, construction companies, resident reports, social media posts, and IoT sensors. It also acquires real-time information on the availability, capacity, and facilities (toilets, showers, power, Wi-Fi, drinking water, food stockpiles, nursing rooms, barrier-free access, pet-friendly options, foreign language support, on-site medical staff, infection control measures, privacy, heating and air conditioning, charging facilities, laundry facilities, children's spaces, women-only spaces, LGBTQ-friendly spaces, etc.) of evacuation centers and temporary accommodation facilities. Regarding the operational status of medical institutions, we will acquire information such as the acceptance status of hospitals, clinics, emergency hospitals, disaster base hospitals, temporary medical facilities, pharmacies, home nursing stations, dental clinics, and veterinary hospitals, the types of illnesses and injuries that can be treated, the availability of emergency response, the availability of medical staff, the inventory of pharmaceuticals and medical equipment, infectious disease control measures, foreign language support, accessibility features, means of transportation, medical collaboration systems, and the availability of telemedicine.IoT sensors will acquire real-time data on air quality, radiation, river levels, ground deformation, temperature, humidity, noise, vibration, PM2.5, pollen, volcanic ash, ultraviolet radiation, CO2 concentration, odor, rainfall, wind speed, wind direction, lightning, tsunami warnings, seismometer data, aftershock predictions, fire alarms, gas leak detection, hazardous material leak detection, animal intrusion detection, and drone surveillance footage. Social media will collect disaster-related posts, on-site reports, image / video / audio posts, posts with location information, disaster hashtags, foreign language posts, AI automatic translation, reliability evaluation, information freshness assessment, hoax detection, and cross-referencing with official information to extract firsthand accounts and image / video evidence from the field.

[0103] The generation AI analysis unit comprehensively and dynamically analyzes all of this information, taking into account user attribute information (elderly, disabled, with children, pregnant, foreign tourist, with pet, LGBTQ, presence of pre-existing conditions / allergies, restrictions on means of transportation, language ability, IT literacy, disaster experience, evacuation behavior characteristics, etc.), individual preferences (desire for barrier-free routes, priority for medical facilities, desire for pet-friendly routes, desire for rest stops along the way, desire for meals / toilets / charging / Wi-Fi / privacy, emphasis on infectious disease control, desire for women-only spaces, desire for routes with nighttime lighting, desire for foreign language support, desire for religious considerations, desire for LGBTQ considerations, etc.), real-time conditions in the city during a disaster, big data and simulation results from past disasters, evacuation orders and advisories from the government, prospects for the restoration of urban infrastructure, resource distribution, secondary disaster risk, and optimization of evacuation guidance for the entire city. Based on this comprehensive analysis, it generates and presents to the terminal in real time individually optimized evacuation routes, shelters, medical facilities, necessary preparations, danger avoidance measures, resource distribution, urban function maintenance measures, and secondary disaster prevention measures for each user. For example, for elderly people using wheelchairs, we would propose a combination of services including barrier-free routes without steps, pedestrian overpasses with elevators, locations with temporary ramps, barrier-free shelters, facilities with resident medical staff, facilities equipped with toilets, showers, and charging facilities, routes with nighttime lighting, guidance on wheelchair-accessible taxis and on-demand transportation, dispatch of care staff, support for daily life after arriving at the shelter, and arrangements for transportation to medical facilities. For people with children or pregnant women, we would prioritize guiding them to nursing rooms and diaper changing stations, children's spaces, women-only spaces, facilities with enhanced infection control measures, food stockpiles, milk, stroller-friendly routes, routes with nighttime lighting, safe play areas, facilities with resident childcare workers, and medical facilities that accept maternal and child health handbooks. For foreign tourists, we would propose a combination of services including shelters with foreign language support, multilingual signage, AI automatic translation, facilities with religious considerations, placement of foreign volunteers, Wi-Fi access, collaboration with tourist information centers, embassy contact information, emergency interpretation services, and collaboration with NPOs that support foreigners during disasters. For those traveling with pets, we will provide information on pet-friendly shelters, veterinary clinics, pet food stockpiles, pet toilets, pet cage rentals, designated pet-friendly spaces, rules for evacuating with pets, and collaborations with animal volunteers.

[0104] In terms of resource distribution across the entire city, congestion predictions and dispersal guidance will be implemented to prevent overconcentration of people in specific shelters and routes. For example, "If everyone continues to use route A, congestion will worsen, so we recommend route B for some users," "Shelter C is expected to be full, so we will strengthen guidance to shelter D," "Medical facility E is full with severely ill patients, so those with mild symptoms will be dispersed to medical facility F," "Extra bus service G will prioritize the elderly and disabled, while those who can walk should use walking route H," and "Temporary housing I will prioritize the elderly and those requiring care, while tent village J will prioritize young people and healthy individuals." This will achieve dynamic optimization that maximizes the safety and efficiency of the entire city.

[0105] Specific examples of prompts to be fed into the generating AI include: "Based on the following city information, propose the optimal route, shelters, medical facilities, routes to avoid, and necessary preparations for elderly people in Area 1 to evacuate safely and efficiently. People flow data: Major road A is congested, underpass B has availability. Infrastructure information: Water supply partially shut off, power is normal, communication is congested. Transportation information: Train service suspended, some buses are running, some roads are closed. Shelter information: Shelter X has availability, shelter Y is full. Medical facility information: Hospital A can accept patients, clinic B cannot. IoT sensor: River water level is rising. SNS information: Route A is congested, shelter X has availability. Analyze this information comprehensively and generate action guidelines including the optimal evacuation route, shelters, medical facilities, risk avoidance measures, and necessary preparations."

[0106] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart device 14 and processes it with the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0110] 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.

[0111] [Second Embodiment]

[0112] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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).

[0115] 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, camera 42, and communication interface 44 are also connected to the bus 52.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] 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. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the identification processing unit 290 using these models.

[0123] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is realized by the data processing device 12 and the smart glasses 214. 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".

[0124] (Example 1)

[0125] Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0126] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0127] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Furthermore, the processing by the data processing system 10 described above is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart glasses 214 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] 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.

[0131] [Third Embodiment]

[0132] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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).

[0135] 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, display 343, and communication interface 44 are also connected to the bus 52.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the headset terminal 314. 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".

[0144] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0145] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0146] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the headset terminal 314 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 and display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] 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.

[0150] [Fourth Embodiment]

[0151] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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).

[0154] 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, controlled object 443, and communication interface 44 are also connected to the bus 52.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0164] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0165] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0166] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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.

[0167] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0168] For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the robot 414 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using a generation AI. For example, the serving unit is implemented by the speaker 240 and controlled object 443 of the robot 414 and the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Furthermore, the following additional information is disclosed regarding the above explanation.

[0189] (Claim 1)

[0190] The system comprises an urban information acquisition unit, an infrastructure information acquisition unit, and a generative AI analysis unit, wherein the urban information acquisition unit collects diverse urban-related information in real time, such as pedestrian flow data within the city, public transportation operation status, road traffic information, availability of evacuation shelters and temporary accommodation facilities, operating status of medical institutions, environmental data from IoT sensors, and on-site reports extracted from social media; the infrastructure information acquisition unit acquires the operating status and recovery prospects of infrastructure such as electricity, water, gas, and communications within the city, as well as information on infrastructure failures during disasters; and the generative AI analysis unit dynamically and comprehensively analyzes the various information acquired from the urban information acquisition unit and the infrastructure information acquisition unit using generative AI, and generates and presents in real time the optimal evacuation route, evacuation shelter, medical institution, route to avoid, necessary preparations, action guidelines for maintaining urban functions, resource distribution, and measures to prevent secondary disasters for each user within the city. (Claim 2)

[0191] The system according to claim 1, characterized in that the generation AI analysis unit comprehensively evaluates human flow data, transportation operation information, infrastructure operating status, capacity status of evacuation centers and medical facilities, IoT sensor information, and SNS on-site reports from multiple areas within a city, and if it predicts excessive concentration at a specific evacuation center or route, it performs congestion prediction and dispersion guidance, thereby realizing dynamic evacuation guidance and resource optimization in real time to maximize the safety and efficiency of the entire city. (Claim 3)

[0192] The system according to claim 1, characterized in that the generation AI analysis unit considers user attribute information (elderly, disabled, with children, foreign tourist, etc.), individual preferences (desire for barrier-free routes, priority for medical facilities, desire to bring pets, desire for rest stops along the way, etc.), and real-time conditions in the city during a disaster to generate individually optimized evacuation routes, shelters, medical facilities, necessary preparations, and danger avoidance measures for each user, and further immediately re-evaluates and re-proposes when the situation changes. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system comprising a pedestrian flow information acquisition unit, a transportation information acquisition unit, and a route generation unit, wherein the pedestrian flow information acquisition unit acquires pedestrian flow data, congestion status, traffic history, and on-site report information extracted from SNS at the user's current location and surrounding area; the transportation information acquisition unit acquires the operating status of public transportation, information on service suspensions, availability of alternative transportation, road congestion information, traffic regulation information, and availability of temporary accommodation facilities and related facilities; and the route generation unit comprehensively analyzes the various information acquired from the pedestrian flow information acquisition unit and the transportation information acquisition unit using a generation-type AI to generate and present in real time action guidelines for each user, including the optimal return route and means of returning home, as well as suggestions for the use of temporary accommodation facilities.

2. The system according to claim 1, characterized in that the route generation unit comprehensively evaluates for each of the multiple route candidates from the user's current location to the destination using a generation AI, pedestrian flow data, public transport operation information, congestion information, traffic history, traffic regulation information, SNS information, and the availability of temporary accommodation facilities and related facilities, selects the optimal route and means, and further re-evaluates in real time if the situation changes along the way, and presents the user with the latest guidance for returning home.

3. The system according to claim 1, characterized in that the route generation unit analyzes the content of on-site reports extracted from SNS information using natural language processing, determines the passability and danger information of the site that cannot be grasped from official information using a generation AI, and reflects this information in the selection of a return route and the suggestion of temporary accommodation facilities.

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