system

Abstract

We provide the system. [Solution] A method for evaluating the safety of mountain climbing routes based on geographical information and past accident data, A method for obtaining weather information and proposing changes to the climbing route based on that information, A means of obtaining the user's location information and providing real-time route guidance, A means of transmitting a distress signal and supporting rescue operations in the event of an emergency, A means of identifying deteriorated sections of the route after the climb and notifying the climbers of improvement measures, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Mountain climbing in mountainous areas has the problem that the risk of getting lost or having an accident increases due to changes in the natural environment and lack of proper navigation. Especially in situations such as deteriorating weather conditions or getting lost on the way, failure to respond promptly may endanger life. However, currently, a system that can detect such risks in advance and respond in real time is not sufficient. Therefore, there is a need for a technology that can prevent accidents and support prompt rescue activities in case of an accident.

Means for Solving the Problems

[0005] This invention provides a means for evaluating the safety of mountain climbing routes based on geographical information and past accident data. It also includes a means for acquiring weather information in real time and proposing changes to the climbing route based on that information. Furthermore, it reduces the risk of getting lost by acquiring the user's location information and providing real-time route guidance. In the event of an emergency, it includes a means for immediately transmitting a rescue signal to support rescue operations. This makes it possible to identify deteriorated sections of the route after the climb and notify administrators of improvement measures. By integrating these means, it is possible to prevent mountain accidents and to enable a swift and effective response in the event of an emergency.

[0006] "Geographic information" refers to spatial data that includes topography, features, and land use, and represents the topography of a specific region.

[0007] "Past accident information" refers to records and data on accidents and incidents that have occurred in mountainous regions in the past, including information such as specific locations and conditions.

[0008] "Means for evaluating the safety of mountain climbing routes" refers to a function that has the ability to analyze the risks of a particular mountain climbing route based on geographical information and past accident data, and to judge its level of safety.

[0009] "Weather information" refers to real-time data on temperature, precipitation, wind speed, and weather conditions for each region, and is information that indicates changes in the environment.

[0010] The "means of suggesting changes to the climbing route" refers to a function that recommends route changes to help users climb more safely, based on acquired weather information and other factors.

[0011] "User location information" refers to data indicating the user's current location, obtained using technologies such as GPS, and is updated in real time.

[0012] "A means of providing real-time route guidance" refers to a function that continuously presents the user with the most suitable hiking route and provides navigation by comparing the user's current location with their target location.

[0013] "Means of transmitting a rescue signal" refers to a function that has the communication capability to quickly convey the user's current location and situation to external rescue organizations in an emergency.

[0014] "Means of supporting rescue operations" refers to a function that assists in rapid rescue operations by calculating the optimal rescue route after receiving a distress signal and providing necessary information to the rescue team.

[0015] "Means for identifying deteriorated areas and notifying improvement measures" refers to a function that identifies physical deterioration and dangerous areas based on the usage status of mountain climbing routes and changes in the natural environment, and informs relevant parties of suggestions for improvement. [Brief explanation of the drawing]

[0016] [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] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] 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.

[0021] 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] 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).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that prevents accidents in mountainous areas and enables a rapid response in emergencies. This system functions through the cooperation of three parties: a server, terminals, and users.

[0038] First, the server collects detailed geographical information and historical accident data for mountainous regions from a database. This data is analyzed using a generative AI model and used to assess the safety of climbing routes. Furthermore, it obtains current weather data from weather information providers and integrates it with the geographical information to perform a real-time risk assessment. The results of this assessment serve as an important guide for users when selecting safe climbing routes.

[0039] Next, the user enters their destination and climbing date into the device. Based on this, the server calculates the optimal climbing route and presents it to the device. The user can review the suggested route and make changes as needed. Once the user begins climbing, the device constantly acquires the user's location information using GPS and transmits it to the server in real time.

[0040] During the climb, the device continuously monitors the user's location and issues a warning if it detects a deviation from the route. Furthermore, it receives weather information updates from the server and, if new risks arise, notifies the user to change their route or descend.

[0041] In an emergency, the user operates the device to send an SOS signal. This signal, including the user's current location and situation, is transmitted to a server. Based on this information, the server notifies rescue teams and develops a rescue plan using the shortest route. This entire process enables rapid and accurate rescue operations.

[0042] After the climb is completed, the server analyzes the data collected along the route to identify areas of physical deterioration and those at risk. This information is then communicated to relevant administrators to encourage maintenance and improvements to the hiking trails.

[0043] The system of this invention allows climbers to enjoy a safer climbing experience and receive effective rescue in the event of an accident. Specifically, the server monitors the deterioration status of wooden bridges used on specific climbing routes, predicts when maintenance will be needed, and notifies the administrator, thereby improving safety.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects detailed geographical information and past accident data for mountainous areas from a database. This data is then passed as input to the analysis engine.

[0047] Step 2:

[0048] The server uses a generative AI model to analyze the received data and create a risk assessment score for the safety of each climbing route. This result is recorded in a separate database.

[0049] Step 3:

[0050] The server retrieves current weather data from weather information providers using APIs and integrates it with geographical information. This updates the risk assessment in real time.

[0051] Step 4:

[0052] The user enters the desired mountainous region and climbing date into the terminal. This input data is sent to the server, and the system begins suggesting appropriate climbing routes.

[0053] Step 5:

[0054] The server calculates the optimal climbing route based on the user's request and risk assessment score. The calculated route information is then sent to the user's device.

[0055] Step 6:

[0056] The terminal displays route information received from the server to the user. The user reviews this information and makes minor adjustments to the route as needed.

[0057] Step 7:

[0058] Once a user begins climbing, the device constantly acquires the user's location information via GPS and transmits it to the server in real time. This data is continuously processed by a monitoring system.

[0059] Step 8:

[0060] The device uses the user's location data to issue a warning if it detects a deviation from the route. This information provides the user with options for appropriate action.

[0061] Step 9:

[0062] The server periodically updates weather information and reassessss the risks along the route. If weather conditions worsen, it sends a notification to the device, prompting the user to change their route or descend the mountain.

[0063] Step 10:

[0064] If a user encounters an emergency, they press the emergency button on their device to send an SOS signal. This signal is then forwarded to the server as a report, including the user's location and situation.

[0065] Step 11:

[0066] Upon receiving an SOS signal, the server sends an emergency notification to the rescue team and calculates the optimal rescue route. It provides detailed information to the rescue team to support rapid rescue operations.

[0067] Step 12:

[0068] The server analyzes the data collected after the climb to identify areas of deterioration along the route used. Based on this, it notifies the administrator of the need for improvements.

[0069] (Example 1)

[0070] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0071] In mountainous regions and hazardous terrain, there is a need for rapid and appropriate safety assessments using readily available information, as well as quick responses to sudden weather changes. However, existing technologies do not adequately perform detailed risk assessments that reflect geographical and meteorological information, nor do they provide sufficient real-time user follow-up, leading to problems such as accidents and people getting lost. This invention aims to solve these problems and improve user safety.

[0072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0073] In this invention, the server includes means for evaluating the safety of a travel route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the travel route based on that information, and means for acquiring the user's location information and providing real-time route guidance. This enables a system in which the user always receives navigation based on the latest safety information and can flexibly respond to sudden dangers.

[0074] "Geographic information" refers to information about geographical characteristics such as topography, terrain, and land use.

[0075] "Accident information" refers to records and data concerning the details, circumstances, and causes of accidents that have occurred in the past.

[0076] A "travel route" is the path or route taken when traveling to reach a specific destination.

[0077] "Safety assessment" is a process for measuring and analyzing the probability of accidents or hazards occurring in specific situations or activities, and for determining the need for countermeasures.

[0078] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed.

[0079] "Means of suggesting change" refer to methods or functions that suggest alternative options or actions to users in response to changing circumstances.

[0080] "Location information" refers to data that indicates the geographical location of a specific place or object.

[0081] "Real-time route guidance" is a service that immediately displays the optimal travel route based on your current location and circumstances.

[0082] An "emergency situation" is a state of imminent danger that deviates from normal circumstances and requires a swift response.

[0083] A "rescue signal" is a signal or message transmitted to inform the outside world of an emergency.

[0084] A "deteriorated area" refers to a part whose performance or function has been reduced due to physical damage or wear.

[0085] "Improvement measures" are specific methods or actions to solve existing problems and make the situation better.

[0086] An "artificial intelligence model" is a set of algorithms that allow computers to analyze data, learn, and make inferences in the same way as humans.

[0087] "Analysis" is the act of consideration aimed at breaking things down into their constituent elements and clarifying their meaning and relationships.

[0088] "User" refers to an individual or group that uses a system or application.

[0089] This invention provides a system that enables the design of safe travel routes and emergency response in mountainous regions, and functions through the coordinated operation of a server, terminals, and users.

[0090] The server performs safety assessments using geographical information collected from terrain databases and accident recording systems, along with historical accident data. Using a generative AI model, it analyzes vast amounts of data to determine hazards along a route under specific conditions. This information is used for user risk management. The server also obtains the latest weather data through weather information service APIs and uses this data to suggest changes to travel routes. For example, the server can utilize the OpenWeatherMap API to collect information on temperature, wind speed, and rainfall, which can then be used for risk assessment.

[0091] The terminal's role is to transmit information to the server when the user enters their destination and planned travel date. The server then presents the user with the optimal route information and provides real-time route guidance. The terminal constantly monitors the user's location using a GPS module and transmits location data to the server. Based on the location information, it checks whether the user is deviating from the suggested route and issues a warning if a deviation is detected.

[0092] Users can carry the device while on the move and select a safe route according to suggestions from the server. In an emergency, they can use the device's emergency signal transmission function to send an SOS signal to the server, including their current location and situation. This procedure allows the server to quickly notify rescue organizations and take action to ensure the user's safety.

[0093] Example of a prompt message as a concrete example: "Please perform a real-time travel risk assessment under the following conditions: destination is point X, travel date is October 10, 2023, current weather conditions are cloudy, wind speed is 5 m / s. Please provide a safety assessment considering past accident cases."

[0094] As a result, the system utilizes advanced AI technology to provide users with safe and efficient travel information and is equipped to respond quickly in emergencies.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] The server collects geographical information and historical accident data for mountainous regions from a terrain database and accident record system. Inputs include map data and accident history, and data analysis is performed using a generative AI model. The output generates risk assessment results for travel routes in a specific region. Specifically, the server executes a Python script, uses database queries to retrieve the necessary datasets, and inputs them into the AI ​​model.

[0098] Step 2:

[0099] The server obtains current weather data through the API of a weather information service. Input includes API requests, and output provides the latest weather information regarding temperature, wind speed, and precipitation. The server integrates this weather data with geographical information to update the risk model and reassess the safety of travel routes. Specifically, the server retrieves data in JSON format from the OpenWeatherMap API, parses it, and incorporates it into the risk assessment.

[0100] Step 3:

[0101] The terminal sends the destination and planned travel date entered by the user to the server. The input includes the user's destination and date information, and the output is the server's suggested optimal travel route. Based on the risk assessment, the server evaluates multiple routes, selects the optimal route using a generating AI model, and sends it to the terminal. Specifically, the terminal uses a form screen to obtain information from the user and sends it to the server as an HTTP request.

[0102] Step 4:

[0103] The terminal presents the user with optimal travel route information obtained from the server. The input is route information sent from the server, and the output is route guidance displayed on the user interface. Furthermore, the terminal monitors the user's location in real time using GPS and transmits this data to the server. Specifically, the terminal uses the Google® Maps API to draw the route on a map and updates the location in real time.

[0104] Step 5:

[0105] In an emergency, the user presses the SOS button on their device to send their current GPS location and status to the server. The input includes the user's location information and emergency status, and the output is a signal to support rescue operations. Based on this information, the server quickly develops a rescue plan and arranges for assistance. Specifically, the device packages its location information and SOS status and sends it quickly to the server, enabling real-time rescue support.

[0106] (Application Example 1)

[0107] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0108] The present invention aims to provide a system that prevents accidents in mountainous areas and emergencies in urban areas, enabling a rapid and effective response. Conventional technologies are specialized for specific regions and situations, making comprehensive risk assessment and information provision difficult. Therefore, there is a need for broader disaster prevention measures and the provision of safety information.

[0109] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0110] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographic information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for integrating mountain climbing and disaster prevention information in urban areas, performing risk assessments, and providing safety information. This enables the provision of safe mountain climbing experiences in mountainous regions and rapid disaster response in urban areas.

[0111] "Geographic information" refers to information about the topography, terrain, and features of a specific area, and is important basic data for setting hiking routes and disaster prevention planning.

[0112] "Past accident information" refers to records of accidents and incidents that have occurred in the past, and is data used for safety assessments and risk prediction.

[0113] A "mountain climbing route" is a path established for climbers to travel within a mountainous region, and route selection must be based on safety and weather conditions.

[0114] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed, and plays an important role in mountaineering and urban disaster prevention planning.

[0115] "User location information" refers to data that indicates the current location of a specific user using technologies such as GPS, and is used for route guidance and safety checks.

[0116] A "rescue signal" is a signal transmitted in an emergency to request rescue, and is used to quickly convey the user's location and situation.

[0117] "Disaster prevention information" refers to information regarding safe actions to take during a disaster, evacuation locations, and the progression of the situation, and is a foundation that supports the safety of individuals and society.

[0118] "Risk assessment" is an analytical process that uses geographical information, meteorological information, and past accident data to measure the safety of a particular situation or area and predict potential hazards.

[0119] This system is configured to function through the collaboration of three parties: the server, the terminal, and the user. First, the server collects geographic information, past accident information, and current weather information via the network. Geographic information is obtained using a GIS (Geographic Information System), and accident information is acquired from a database. Weather information is obtained from a weather data provider via an API. This collected information is analyzed using a generative AI model to generate a numerical model for risk assessment.

[0120] Specifically, the server uses Python to build and operate a neural network model that assesses risk using deep learning frameworks such as TENSORFLOW® or PyTorch. This model will evaluate the risks of mountain climbing routes and urban areas in real time, providing users with safe information.

[0121] The device is envisioned as a smartphone or tablet, and will continuously acquire the user's location information using a GPS module. The device will also use a push notification service such as Firebase Cloud Messaging to receive push notifications from the server and present new risk information and safety measures to the user in real time.

[0122] When a user begins a climb or journey, they set their destination and itinerary on their device. Based on this, the server provides optimal route information. While climbing or traveling, the device monitors the user's location and route deviations through the output of a generated AI, and has the function to issue warnings as needed. It is also possible to send an SOS signal from the device, transmitting information to the server in case of an emergency, which can lead to rapid rescue operations.

[0123] For example, if a user is planning a weekend hike, the server can send a notification to their device saying, "The current weather is expected to change suddenly. For your safety, we recommend changing your route from Route A to Route B." Furthermore, in the event of an unexpected disaster in a city, evacuation advisories and safety information can be quickly delivered to the user.

[0124] An example of a prompt message would be, "Please tell me about safe hiking routes in the nearby mountainous area. Please also take current weather conditions into consideration."

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] The server retrieves geographic information from a geographic information system. This information is necessary for setting hiking routes and urban disaster prevention planning. The retrieved information is stored in a database and input into a generating AI model.

[0128] Step 2:

[0129] The server retrieves past accident information from a dedicated database. This information includes detailed data on past accidents and disasters. Based on the retrieved accident information, a generative AI model is used to analyze patterns that serve as criteria for risk assessment.

[0130] Step 3:

[0131] The server collects current weather information in real time through the API of a weather data provider. The collected weather information includes weather, temperature, wind speed, etc., and this data is integrated with geographic information and accident information and used as input for AI models.

[0132] Step 4:

[0133] The AI-generated model allows the server to integrate and analyze geographical information, accident information, and weather information to perform a risk assessment. This analysis calculates safe routes and disaster prevention information for urban areas, and prepares this information as output for terminals.

[0134] Step 5:

[0135] The user enters their destination and itinerary into the device. The device then sends the entered data to the server. This data includes the user's location, destination, and desired activities.

[0136] Step 6:

[0137] The server calculates the optimal route based on the location and destination information received from the user. This calculation incorporates the results of an AI-based risk assessment, and the resulting optimal route information is sent to the terminal.

[0138] Step 7:

[0139] The terminal receives optimal route information from the server and notifies the user. The notified information includes details of a safe route and any necessary precautions. The user uses this information to confirm their itinerary and modify their plan as needed.

[0140] Step 8:

[0141] The device continuously tracks the user's location using GPS. This location information is also transmitted to the server, and a warning is issued if the user deviates from their route.

[0142] Step 9:

[0143] In an emergency, the user operates the device to send an SOS signal. This signal includes the user's current location and situation, and the server receives it and notifies rescue teams to support rapid rescue operations.

[0144] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0145] This invention is a system that prevents accidents in mountainous areas and provides support that takes into account the user's emotional state. This system achieves its functionality through the collaboration of a server, a terminal, and an emotion engine that analyzes the user's emotions.

[0146] First, the server collects geographical information and historical accident data for mountainous areas from a database. In parallel, it obtains real-time data such as temperature, precipitation, and wind speed from weather information providers and integrates it with the geographical information. The server then analyzes this information using a generative AI model to perform a safety risk assessment. Based on the risk assessment, it proposes the most suitable climbing route to the user.

[0147] Next, the user uses the device to enter their destination and hiking date. The user's input information is sent to the server, and an appropriate hiking route is calculated. Once the hike begins, the device acquires the user's location information in real time and sends it to the server. Based on this data, navigation and route change notifications are provided during the hike.

[0148] Furthermore, the device acquires the user's biometric information from sensors and transmits it to the emotion engine. The emotion engine uses this data to infer the user's emotional state. For example, it can detect stress and fatigue from heart rate and skin electrical activity. Based on the results, the user receives notifications recommending breaks or equipment checks.

[0149] In the event of an emergency, the user operates their device to send an SOS signal. This signal is sent to the server as a report including the user's current location and emotional state. The server quickly transmits this information to rescue teams and proposes the optimal rescue route. Furthermore, the emotion engine can generate psychological support messages that take the user's emotional state into consideration, providing a sense of security.

[0150] After the climb is completed, the server analyzes all data obtained from the route to identify deteriorated sections and risk areas. This allows administrators to be notified of improvement measures to enhance safety for future climbs.

[0151] For example, if a user begins to feel stressed during a long climb, the emotion engine will detect this, and the device will suggest a break and provide support such as playing relaxing music. In this way, the system aims to improve quality in both safety and user experience.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The server retrieves detailed geographical information and past accident data for mountainous areas from a database and inputs it into the analysis engine. This prepares the system for risk assessment of mountain climbing routes.

[0155] Step 2:

[0156] The server retrieves real-time weather data from weather information providers. This data includes temperature, precipitation, and wind speed, and when integrated with geographic information, it improves the accuracy of risk assessments.

[0157] Step 3:

[0158] The server analyzes geographic and weather data collected using a generative AI model to calculate a risk assessment score indicating the safety of each hiking route. This information is used for subsequent processing.

[0159] Step 4:

[0160] The user enters the desired mountainous region and climbing date into their device. This information is sent to the server, and the system begins suggesting appropriate climbing routes.

[0161] Step 5:

[0162] The server calculates the optimal climbing route based on a risk assessment score for the user's desired route. The route information is then sent to the user's device.

[0163] Step 6:

[0164] The device displays the received route information and prompts the user for confirmation. The user can review the suggested route and request changes if necessary.

[0165] Step 7:

[0166] Once the user begins climbing, the device continuously acquires the user's location information using GPS and transmits it to the server in real time.

[0167] Step 8:

[0168] The device uses built-in sensors to acquire the user's biometric information and transmits it to the emotion engine. The emotion engine then uses this information to analyze the user's emotional state.

[0169] Step 9:

[0170] The emotion engine detects stress and fatigue from the user's emotional data and generates appropriate responses based on that state. For example, it can generate suggestions to take a break or encouraging messages.

[0171] Step 10:

[0172] The server monitors weather information, which is updated in real time, and immediately sends a notification to the user's terminal if a risk occurs. The terminal then prompts the user to change their route or descend the mountain.

[0173] Step 11:

[0174] If a user encounters an emergency, they can use the device's SOS function to send a signal. This signal, which includes location information and emotional state, is sent to the server.

[0175] Step 12:

[0176] The server receives an SOS signal, quickly sends an emergency notification to rescue teams, and calculates the optimal rescue route. The emotion engine generates messages tailored to the user's emotions, providing reassurance and support.

[0177] Step 13:

[0178] After the climb is completed, the server analyzes route usage and emotional data to identify areas of physical deterioration and psychological risk factors. Administrators are then notified of any necessary improvements.

[0179] (Example 2)

[0180] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0181] Ensuring safety during mountain climbing in mountainous regions and improving user convenience are essential. Specifically, it is necessary to accurately assess risks using geographic and meteorological information, and to utilize climbers' location and biometric information in real time to provide early response to emergencies and appropriate support tailored to the user's emotional state. The challenge lies in providing highly accurate and rapid safety support in mountainous areas, which was difficult with conventional systems.

[0182] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0183] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for acquiring biometric information, analyzing emotional state, and providing appropriate notifications to the user. This enables flexible and rapid safety assurance and comfortable mountain climbing support tailored to the user's current situation.

[0184] "Geographic information" refers to data that includes location-related information such as topography, roads, and landmarks, and is used to understand the conditions of a specific region.

[0185] "Past accident information" refers to records of accidents that have occurred in a specific area in the past. This information is used to analyze the causes and circumstances of these accidents in order to improve safety.

[0186] A "mountain climbing route" is a set path that climbers take to reach their destination, and it is optimized considering the terrain and safety.

[0187] "Means of assessing safety" refer to processes and techniques for analyzing risk factors and indicating the safety of specific activities or locations using numerical values ​​or standards.

[0188] "Weather information" refers to meteorological data such as temperature, precipitation, and wind speed, and is used to understand weather conditions at a specific time and place.

[0189] "Means of proposing change" refer to systems or methods for indicating safer or more effective ways or routes depending on the situation.

[0190] "Location information" refers to information used to indicate a specific point on Earth, and is usually represented by data such as latitude and longitude.

[0191] "Route guidance" refers to a system or method that provides instructions to assist travel by showing the route and direction to a destination.

[0192] "Biometric information" refers to data that indicates an individual's physiological state, such as heart rate and skin electrical activity, and is usually measured through sensors.

[0193] "Means of analyzing emotional states" refer to systems and technologies that estimate and understand a person's emotions based on their biometric information and other data.

[0194] A "rescue signal" is a signal sent to request help in an emergency, and may include location information.

[0195] "Means of supporting rescue operations" refer to systems and technologies that provide the necessary information and procedures in the event of an emergency, and support the efficient carrying out of rescue operations.

[0196] A "deteriorated area" refers to a place or part that has deteriorated physically or functionally and requires improvement.

[0197] "Means of notifying corrective measures" refers to systems or methods for communicating suggestions or procedures for correcting specific problems to relevant parties.

[0198] The system used to implement this invention provides safe mountaineering support in mountainous regions based on communication between a server, a terminal, and a user.

[0199] The server first retrieves geographical information and historical accident data from a database. A relational database management system (relational database management system) is often used for this operation, such as PostgreSQL. Weather information is also obtained in real time through an external weather data provider (e.g., the OpenWeatherMap API). To integrate this diverse information, the server processes the data using the Python pandas library.

[0200] Next, the integrated data is analyzed using a generative AI model. Specifically, generative AI such as OpenAI's GPT series is utilized. This allows for risk assessment of the hiking routes and suggests the most suitable hiking route for the user.

[0201] The user enters their destination and climbing date using their device, and this information is sent to the server. The device is assumed to be a typical smartphone, and its GPS function tracks the user's location in real time. This location information is sent to the server, which uses it to provide route guidance and, if necessary, suggest route changes.

[0202] In addition, the device acquires biometric information from sensors such as wearable devices. This includes monitoring heart rate and skin electrical activity, for example, through an Apple Watch. This data is sent to the emotion engine and used as input to analyze the user's emotional state. The emotion engine detects whether the user is stressed or fatigued and, based on that, suggests taking a break or plays relaxing music.

[0203] For example, if a user's heart rate increases by 20% during a long hike, the emotion engine will detect stress, and the device will suggest a break to the user. Furthermore, it can play relaxing music using streaming services such as Spotify.

[0204] An example of a prompt message would be: "Explain what action this system should recommend when the user's heart rate increases by 20% above normal."

[0205] In the event of an emergency, users can operate their devices to send an SOS signal. This signal includes their current location and emotional state, which the server quickly relays to rescue teams. Furthermore, a psychological support message generated by the emotion engine is sent to the user to provide reassurance.

[0206] The system configuration described above aims to improve safety during mountain climbing and enrich the user experience.

[0207] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0208] Step 1:

[0209] The server acquires geographical information and historical accident data. This allows it to collect foundational data for safety assessments. The input is queries from a database, and the output is a dataset containing that information. The server retrieves the information via a relational database management system and prepares it for use in subsequent analysis processes.

[0210] Step 2:

[0211] The server retrieves real-time weather data from weather information providers. This is done via an API, and the output includes data such as temperature, precipitation, and wind speed. The server integrates this data with the geographic information obtained in the previous step to generate a comprehensive dataset.

[0212] Step 3:

[0213] The server analyzes the integrated data using a generative AI model. This process incorporates geographical information, historical accident data, and weather data. As a result of the analysis, a safety score for the hiking route is generated. Specifically, AI models such as OpenAI's GPT series are used for this analysis, performing scoring based on the type and location of risk.

[0214] Step 4:

[0215] The user enters their hiking destination and date into the device. This information is sent to the server and used as input. Based on the analysis results, the server calculates the optimal hiking route and sends a suggestion to the user's device as output. The user can then view the route, whose safety has been assessed, on the screen.

[0216] Step 5:

[0217] The device uses GPS to acquire the user's location information in real time. The acquired location information is sent to the server and used as input information. Based on this data, the server tracks the user's route and generates navigation information and route change suggestions as output. Route change notifications are sent in real time as needed.

[0218] Step 6:

[0219] The device collects biometric information from wearable devices, including heart rate and skin electrical activity. The acquired data is sent to an emotion engine, which analyzes it to estimate the user's emotional state. For example, it might detect stress levels and suggest the user take a break.

[0220] Step 7:

[0221] If an emergency occurs while mountaineering, the user can send an SOS signal from their device. The input includes the user's current location and emotional state. This information is sent to a server and quickly relayed to rescue teams. The server calculates the optimal rescue route and provides it to the rescue team. Additionally, a psychological support message generated by an emotion engine is sent to the device.

[0222] Step 8:

[0223] The server analyzes all data collected after the climb is completed. Input includes all data related to the climbing route, and output identifies deteriorated sections and risk areas along the route. This allows for the development of improvement measures for future climbs, which are then communicated to administrators. Through this process, continuous safety improvements are ensured.

[0224] (Application Example 2)

[0225] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0226] Conventional mountain climbing support systems primarily rely on geographical and meteorological information for safety assessments, lacking individualized support that considers the user's emotional state. This can lead to users continuing their climbs while experiencing significant psychological distress. Furthermore, while there is a need to monitor the user's health in real time and suggest appropriate actions, such implementations have not been common.

[0227] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0228] In this invention, the server includes means for evaluating the safety of a climbing route based on geographical information and past accident information, means for acquiring weather information and suggesting changes to the climbing route based on that information, and means for detecting the user's biometric information and inferring their emotional state. This enables the provision of appropriate support according to the user's psychological state, making a safe and comfortable climbing experience possible.

[0229] "Geographic information" refers to data that includes information about the location and topography of a specific region or place.

[0230] "Past accident information" refers to detailed records and data about accidents that have occurred to date.

[0231] "Means for evaluating the safety of mountain climbing routes" refers to a function that uses geographical information and past accident data to analyze the risks of mountain climbing routes and determine their level of safety.

[0232] "Weather information" refers to data that shows the weather conditions of a region with geographical characteristics, such as temperature, precipitation, and wind speed.

[0233] "Means of providing route guidance" refers to functions that provide users with the optimal route and instructions in real time.

[0234] "Means for transmitting rescue signals and supporting rescue operations" refers to technical measures that enable users to transmit rescue requests in emergencies and provide rapid assistance.

[0235] "A means of identifying deteriorated sections of a route and notifying of improvement measures" refers to a mechanism that identifies dangerous sections along a route after a climb and recommends improvements to ensure safety for future climbs.

[0236] "Biometric information" refers to data such as heart rate and skin electrical activity that indicate an individual's health status.

[0237] "Means for inferring emotional state" refer to algorithms and devices that analyze and determine a user's psychological state based on their biometric information.

[0238] "Means of providing appropriate notifications and suggestions to users based on their emotional state" refers to functions that take into account the user's psychological state and provide appropriate advice and information.

[0239] This invention is a system that provides the necessary support for users to enjoy mountain climbing with peace of mind. Specifically, a server, terminals, and an emotion analysis engine work together.

[0240] The server collects and combines geographical information, past accident data, and weather information to suggest the optimal hiking route to the user. This uses a database as hardware and software such as Python and generative AI models. Data analysis is used to assess safety risks.

[0241] The device acquires the user's location information in real time. This allows it to understand how the user is moving and provide safe route guidance. The device also uses biosensors to collect data such as the user's heart rate and skin's electrical activity, and sends this information to an emotion analysis engine to analyze their emotional state. For example, if it detects stress caused by prolonged mountain climbing, the system will provide support such as playing relaxing music.

[0242] Receiving appropriate support based on users' emotional state can reduce psychological burden and lead to a better climbing experience. This can alleviate anxiety and pressure felt during climbing, thereby increasing safety.

[0243] For example, if a user feels fatigued, the emotion engine can detect this and the device can send advice such as, "Perhaps it's time for a break?" An example of an input prompt for the generative AI model is, "Analyze heart rate data and skin potential data in real time and estimate the stress level."

[0244] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0245] Step 1:

[0246] The server collects geographical information, past accident data, and weather information from a database. This information is used as foundational data for evaluating the safety of mountain climbing routes. The input is a set of information from the database, and the output is integrated geographical and weather data. This integrated data is used in the next risk assessment step.

[0247] Step 2:

[0248] The server analyzes geographical and meteorological data collected using a generative AI model to assess the risks of hiking routes. The AI ​​model's input is integrated geographical and meteorological data. As part of the data processing, an algorithm is applied to score the degree of risk, and a safety assessment is performed. The output is the assessed risk score and the recommended optimal hiking route.

[0249] Step 3:

[0250] The user uses a terminal to enter their destination and hiking date. This information is sent to the server, and the optimal hiking route is suggested. The input in this step is the destination and date / time data entered by the user from the terminal, and the output is the optimal route returned by the server.

[0251] Step 4:

[0252] The device acquires the user's location information in real time and sends it to the server. This allows the server to monitor the user's progress and provide safe route guidance. The input is location data from the GPS sensor, and the output is current location information sent to the server. This location information is used for route guidance in the next step.

[0253] Step 5:

[0254] The device acquires the user's biometric information from sensors and transmits it to the emotion analysis engine. At this stage, the necessary inputs are heart rate and skin electrical activity data from biosensors, and the output is the estimated emotional state. The emotion analysis engine processes this data, analyzes the psychological state, and outputs the result.

[0255] Step 6:

[0256] When a user experiences fatigue or stress, the emotion analysis engine detects their state, and the device provides the user with a notification prompting them to take a break or change their mood. The input for this step is emotional state data from the emotion analysis engine, and the output is a notification message to the user suggesting a break or a change of pace. Specific actions may include playing relaxing music.

[0257] Step 7:

[0258] In the event of an emergency, the user operates the device to send an SOS signal. The input for the SOS signal is a user-initiated button press or similar action, and the output is a rescue request signal that includes current location information and emotional state data. The server receives this signal and quickly transmits it to rescue teams.

[0259] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0260] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0261] 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.

[0262] [Second Embodiment]

[0263] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0264] 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.

[0265] 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).

[0266] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0267] 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.

[0268] 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).

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0275] This invention is a system that prevents accidents in mountainous areas and enables a rapid response in emergencies. This system functions through the cooperation of three parties: a server, terminals, and users.

[0276] First, the server collects detailed geographical information and historical accident data for mountainous regions from a database. This data is analyzed using a generative AI model and used to assess the safety of climbing routes. Furthermore, it obtains current weather data from weather information providers and integrates it with the geographical information to perform a real-time risk assessment. The results of this assessment serve as an important guide for users when selecting safe climbing routes.

[0277] Next, the user enters their destination and climbing date into the device. Based on this, the server calculates the optimal climbing route and presents it to the device. The user can review the suggested route and make changes as needed. Once the user begins climbing, the device constantly acquires the user's location information using GPS and transmits it to the server in real time.

[0278] During the climb, the device continuously monitors the user's location and issues a warning if it detects a deviation from the route. Furthermore, it receives weather information updates from the server and, if new risks arise, notifies the user to change their route or descend.

[0279] In an emergency, the user operates the device to send an SOS signal. This signal, including the user's current location and situation, is transmitted to a server. Based on this information, the server notifies rescue teams and develops a rescue plan using the shortest route. This entire process enables rapid and accurate rescue operations.

[0280] After the climb is completed, the server analyzes the data collected along the route to identify areas of physical deterioration and those at risk. This information is then communicated to relevant administrators to encourage maintenance and improvements to the hiking trails.

[0281] The system of this invention allows climbers to enjoy a safer climbing experience and receive effective rescue in the event of an accident. Specifically, the server monitors the deterioration status of wooden bridges used on specific climbing routes, predicts when maintenance will be needed, and notifies the administrator, thereby improving safety.

[0282] The following describes the processing flow.

[0283] Step 1:

[0284] The server collects detailed geographical information and past accident data for mountainous areas from a database. This data is then passed as input to the analysis engine.

[0285] Step 2:

[0286] The server analyzes the data received using the generative AI model and creates a risk assessment score regarding the safety of each hiking route. This result is recorded in another database.

[0287] Step 3:

[0288] The server uses an API to obtain current weather data from a weather information provider and integrates it with geographical information. Thereby, the real-time risk assessment is updated.

[0289] Step 4:

[0290] The user inputs the target mountainous area and the hiking date into the terminal. This input data is sent to the server, and the proposal of an appropriate hiking route is initiated.

[0291] Step 5:

[0292] Based on the user's request, the server calculates the optimal hiking route based on the risk assessment score. The calculated route information is sent to the user's terminal.

[0293] Step 6:

[0294] The terminal presents the route information received from the server to the user. The user checks this information and makes fine adjustments to the route if necessary.

[0295] Step 7:

[0296] When the user starts hiking, the terminal always obtains the user's location information via GPS and sends it to the server in real time. This data is continuously processed by the monitoring system.

[0297] Step 8:

[0298] The terminal issues a warning when it detects a deviation from the route based on the user's location data. This information indicates options for the user to respond appropriately.

[0299] Step 9:

[0300] The server periodically updates the weather information and re-evaluates the risk of the route. If the weather conditions deteriorate, it sends a notification to the terminal to prompt the user to change the route or descend.

[0301] Step 10:

[0302] If the user encounters an emergency, they press the emergency button on the terminal to send an SOS signal. This signal is transferred to the server as a report containing the user's location information and situation.

[0303] Step 11:

[0304] After receiving the SOS signal, the server sends an emergency notification to the rescue team and calculates the optimal rescue route. It provides detailed information to the rescue team to support rapid rescue operations.

[0305] Step 12:

[0306] After the mountain climbing is completed, the server analyzes the data collected and identifies the deteriorated parts of the route used. Based on this, it notifies the administrator of the improvement.

[0307] (Example 1)

[0308] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In mountainous regions and hazardous terrain, there is a need for rapid and appropriate safety assessments using readily available information, as well as quick responses to sudden weather changes. However, existing technologies do not adequately perform detailed risk assessments that reflect geographical and meteorological information, nor do they provide sufficient real-time user follow-up, leading to problems such as accidents and people getting lost. This invention aims to solve these problems and improve user safety.

[0310] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0311] In this invention, the server includes means for evaluating the safety of a travel route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the travel route based on that information, and means for acquiring the user's location information and providing real-time route guidance. This enables a system in which the user always receives navigation based on the latest safety information and can flexibly respond to sudden dangers.

[0312] "Geographic information" refers to information about geographical characteristics such as topography, terrain, and land use.

[0313] "Accident information" refers to records and data concerning the details, circumstances, and causes of accidents that have occurred in the past.

[0314] A "travel route" is the path or route taken when traveling to reach a specific destination.

[0315] "Safety assessment" is a process for measuring and analyzing the probability of accidents or hazards occurring in specific situations or activities, and for determining the need for countermeasures.

[0316] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed.

[0317] "Means of suggesting change" refer to methods or functions that suggest alternative options or actions to users in response to changing circumstances.

[0318] "Location information" refers to data that indicates the geographical location of a specific place or object.

[0319] "Real-time route guidance" is a service that immediately displays the optimal travel route based on your current location and circumstances.

[0320] An "emergency situation" is a state of imminent danger that deviates from normal circumstances and requires a swift response.

[0321] A "rescue signal" is a signal or message transmitted to inform the outside world of an emergency.

[0322] A "deteriorated area" refers to a part whose performance or function has been reduced due to physical damage or wear.

[0323] "Improvement measures" are specific methods or actions to solve existing problems and make the situation better.

[0324] An "artificial intelligence model" is a set of algorithms that allow computers to analyze data, learn, and make inferences in the same way as humans.

[0325] "Analysis" is the act of consideration aimed at breaking things down into their constituent elements and clarifying their meaning and relationships.

[0326] "User" refers to an individual or group that uses a system or application.

[0327] This invention provides a system that enables the design of safe travel routes and emergency response in mountainous regions, and functions through the coordinated operation of a server, terminals, and users.

[0328] The server performs safety assessments using geographical information collected from terrain databases and accident recording systems, along with historical accident data. Using a generative AI model, it analyzes vast amounts of data to determine hazards along a route under specific conditions. This information is used for user risk management. The server also obtains the latest weather data through weather information service APIs and uses this data to suggest changes to travel routes. For example, the server can utilize the OpenWeatherMap API to collect information on temperature, wind speed, and rainfall, which can then be used for risk assessment.

[0329] The terminal's role is to transmit information to the server when the user enters their destination and planned travel date. The server then presents the user with the optimal route information and provides real-time route guidance. The terminal constantly monitors the user's location using a GPS module and transmits location data to the server. Based on the location information, it checks whether the user is deviating from the suggested route and issues a warning if a deviation is detected.

[0330] Users can carry the device while on the move and select a safe route according to suggestions from the server. In an emergency, they can use the device's emergency signal transmission function to send an SOS signal to the server, including their current location and situation. This procedure allows the server to quickly notify rescue organizations and take action to ensure the user's safety.

[0331] Example of a prompt message as a concrete example: "Please perform a real-time travel risk assessment under the following conditions: destination is point X, travel date is October 10, 2023, current weather conditions are cloudy, wind speed is 5 m / s. Please provide a safety assessment considering past accident cases."

[0332] As a result, the system utilizes advanced AI technology to provide users with safe and efficient travel information and is equipped to respond quickly in emergencies.

[0333] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0334] Step 1:

[0335] The server collects geographical information and historical accident data for mountainous regions from a terrain database and accident record system. Inputs include map data and accident history, and data analysis is performed using a generative AI model. The output generates risk assessment results for travel routes in a specific region. Specifically, the server executes a Python script, uses database queries to retrieve the necessary datasets, and inputs them into the AI ​​model.

[0336] Step 2:

[0337] The server obtains current weather data through the API of a weather information service. Input includes API requests, and output provides the latest weather information regarding temperature, wind speed, and precipitation. The server integrates this weather data with geographical information to update the risk model and reassess the safety of travel routes. Specifically, the server retrieves data in JSON format from the OpenWeatherMap API, parses it, and incorporates it into the risk assessment.

[0338] Step 3:

[0339] The terminal sends the destination and planned travel date entered by the user to the server. The input includes the user's destination and date information, and the output is the server's suggested optimal travel route. Based on the risk assessment, the server evaluates multiple routes, selects the optimal route using a generating AI model, and sends it to the terminal. Specifically, the terminal uses a form screen to obtain information from the user and sends it to the server as an HTTP request.

[0340] Step 4:

[0341] The terminal presents the user with optimal travel route information obtained from the server. The input is route information sent from the server, and the output is route guidance displayed on the user interface. Furthermore, the terminal monitors the user's location in real time using GPS and sends this data to the server. Specifically, the terminal uses the Google Maps API to draw the route on a map and updates the location in real time.

[0342] Step 5:

[0343] In an emergency, the user presses the SOS button on their device to send their current GPS location and status to the server. The input includes the user's location information and emergency status, and the output is a signal to support rescue operations. Based on this information, the server quickly develops a rescue plan and arranges for assistance. Specifically, the device packages its location information and SOS status and sends it quickly to the server, enabling real-time rescue support.

[0344] (Application Example 1)

[0345] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] The present invention aims to provide a system that prevents accidents in mountainous areas and emergencies in urban areas, enabling a rapid and effective response. Conventional technologies are specialized for specific regions and situations, making comprehensive risk assessment and information provision difficult. Therefore, there is a need for broader disaster prevention measures and the provision of safety information.

[0347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0348] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographic information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for integrating mountain climbing and disaster prevention information in urban areas, performing risk assessments, and providing safety information. This enables the provision of safe mountain climbing experiences in mountainous regions and rapid disaster response in urban areas.

[0349] "Geographic information" refers to information about the topography, terrain, and features of a specific area, and is important basic data for setting hiking routes and disaster prevention planning.

[0350] "Past accident information" refers to records of accidents and incidents that have occurred in the past, and is data used for safety assessments and risk prediction.

[0351] A "mountain climbing route" is a path established for climbers to travel within a mountainous region, and route selection must be based on safety and weather conditions.

[0352] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed, and plays an important role in mountaineering and urban disaster prevention planning.

[0353] "User location information" refers to data that indicates the current location of a specific user using technologies such as GPS, and is used for route guidance and safety checks.

[0354] A "rescue signal" is a signal transmitted in an emergency to request rescue, and is used to quickly convey the user's location and situation.

[0355] "Disaster prevention information" refers to information regarding safe actions to take during a disaster, evacuation locations, and the progression of the situation, and is a foundation that supports the safety of individuals and society.

[0356] "Risk assessment" is an analytical process that uses geographical information, meteorological information, and past accident data to measure the safety of a particular situation or area and predict potential hazards.

[0357] This system is configured to function through the collaboration of three parties: the server, the terminal, and the user. First, the server collects geographic information, past accident information, and current weather information via the network. Geographic information is obtained using a GIS (Geographic Information System), and accident information is acquired from a database. Weather information is obtained from a weather data provider via an API. This collected information is analyzed using a generative AI model to generate a numerical model for risk assessment.

[0358] Specifically, the server uses Python to build and operate a neural network model that assesses risk using deep learning frameworks such as TensorFlow or PyTorch. This model will evaluate the risks of mountain climbing routes and urban areas in real time, providing users with safe information.

[0359] The device is envisioned as a smartphone or tablet, and will continuously acquire the user's location information using a GPS module. The device will also use a push notification service such as Firebase Cloud Messaging to receive push notifications from the server and present new risk information and safety measures to the user in real time.

[0360] When a user begins a climb or journey, they set their destination and itinerary on their device. Based on this, the server provides optimal route information. While climbing or traveling, the device monitors the user's location and route deviations through the output of a generated AI, and has the function to issue warnings as needed. It is also possible to send an SOS signal from the device, transmitting information to the server in case of an emergency, which can lead to rapid rescue operations.

[0361] For example, if a user is planning a weekend hike, the server can send a notification to their device saying, "The current weather is expected to change suddenly. For your safety, we recommend changing your route from Route A to Route B." Furthermore, in the event of an unexpected disaster in a city, evacuation advisories and safety information can be quickly delivered to the user.

[0362] An example of a prompt message would be, "Please tell me about safe hiking routes in the nearby mountainous area. Please also take current weather conditions into consideration."

[0363] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0364] Step 1:

[0365] The server retrieves geographic information from a geographic information system. This information is necessary for setting hiking routes and urban disaster prevention planning. The retrieved information is stored in a database and input into a generating AI model.

[0366] Step 2:

[0367] The server retrieves past accident information from a dedicated database. This information includes detailed data on past accidents and disasters. Based on the retrieved accident information, a generative AI model is used to analyze patterns that serve as criteria for risk assessment.

[0368] Step 3:

[0369] The server collects current weather information in real time through the API of a weather data provider. The collected weather information includes weather, temperature, wind speed, etc., and this data is integrated with geographic information and accident information and used as input for AI models.

[0370] Step 4:

[0371] The AI-generated model allows the server to integrate and analyze geographical information, accident information, and weather information to perform a risk assessment. This analysis calculates safe routes and disaster prevention information for urban areas, and prepares this information as output for terminals.

[0372] Step 5:

[0373] The user enters their destination and itinerary into the device. The device then sends the entered data to the server. This data includes the user's location, destination, and desired activities.

[0374] Step 6:

[0375] The server calculates the optimal route based on the location and destination information received from the user. This calculation incorporates the results of an AI-based risk assessment, and the resulting optimal route information is sent to the terminal.

[0376] Step 7:

[0377] The terminal receives optimal route information from the server and notifies the user. The notified information includes details of a safe route and any necessary precautions. The user uses this information to confirm their itinerary and modify their plan as needed.

[0378] Step 8:

[0379] The device continuously tracks the user's location using GPS. This location information is also transmitted to the server, and a warning is issued if the user deviates from their route.

[0380] Step 9:

[0381] In an emergency, the user operates the device to send an SOS signal. This signal includes the user's current location and situation, and the server receives it and notifies rescue teams to support rapid rescue operations.

[0382] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0383] This invention is a system that prevents accidents in mountainous areas and provides support that takes into account the user's emotional state. This system achieves its functionality through the collaboration of a server, a terminal, and an emotion engine that analyzes the user's emotions.

[0384] First, the server collects geographical information and historical accident data for mountainous areas from a database. In parallel, it obtains real-time data such as temperature, precipitation, and wind speed from weather information providers and integrates it with the geographical information. The server then analyzes this information using a generative AI model to perform a safety risk assessment. Based on the risk assessment, it proposes the most suitable climbing route to the user.

[0385] Next, the user uses the device to enter their destination and hiking date. The user's input information is sent to the server, and an appropriate hiking route is calculated. Once the hike begins, the device acquires the user's location information in real time and sends it to the server. Based on this data, navigation and route change notifications are provided during the hike.

[0386] Furthermore, the device acquires the user's biometric information from sensors and transmits it to the emotion engine. The emotion engine uses this data to infer the user's emotional state. For example, it can detect stress and fatigue from heart rate and skin electrical activity. Based on the results, the user receives notifications recommending breaks or equipment checks.

[0387] In the event of an emergency, the user operates their device to send an SOS signal. This signal is sent to the server as a report including the user's current location and emotional state. The server quickly transmits this information to rescue teams and proposes the optimal rescue route. Furthermore, the emotion engine can generate psychological support messages that take the user's emotional state into consideration, providing a sense of security.

[0388] After the climb is completed, the server analyzes all data obtained from the route to identify deteriorated sections and risk areas. This allows administrators to be notified of improvement measures to enhance safety for future climbs.

[0389] For example, if a user begins to feel stressed during a long climb, the emotion engine will detect this, and the device will suggest a break and provide support such as playing relaxing music. In this way, the system aims to improve quality in both safety and user experience.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The server retrieves detailed geographical information and past accident data for mountainous areas from a database and inputs it into the analysis engine. This prepares the system for risk assessment of mountain climbing routes.

[0393] Step 2:

[0394] The server retrieves real-time weather data from weather information providers. This data includes temperature, precipitation, and wind speed, and when integrated with geographic information, it improves the accuracy of risk assessments.

[0395] Step 3:

[0396] The server analyzes geographic and weather data collected using a generative AI model to calculate a risk assessment score indicating the safety of each hiking route. This information is used for subsequent processing.

[0397] Step 4:

[0398] The user enters the desired mountainous region and climbing date into their device. This information is sent to the server, and the system begins suggesting appropriate climbing routes.

[0399] Step 5:

[0400] The server calculates the optimal climbing route based on a risk assessment score for the user's desired route. The route information is then sent to the user's device.

[0401] Step 6:

[0402] The device displays the received route information and prompts the user for confirmation. The user can review the suggested route and request changes if necessary.

[0403] Step 7:

[0404] Once the user begins climbing, the device continuously acquires the user's location information using GPS and transmits it to the server in real time.

[0405] Step 8:

[0406] The device uses built-in sensors to acquire the user's biometric information and transmits it to the emotion engine. The emotion engine then uses this information to analyze the user's emotional state.

[0407] Step 9:

[0408] The emotion engine detects stress and fatigue from the user's emotional data and generates appropriate responses based on that state. For example, it can generate suggestions to take a break or encouraging messages.

[0409] Step 10:

[0410] The server monitors weather information, which is updated in real time, and immediately sends a notification to the user's terminal if a risk occurs. The terminal then prompts the user to change their route or descend the mountain.

[0411] Step 11:

[0412] If a user encounters an emergency, they can use the device's SOS function to send a signal. This signal, which includes location information and emotional state, is sent to the server.

[0413] Step 12:

[0414] The server receives an SOS signal, quickly sends an emergency notification to rescue teams, and calculates the optimal rescue route. The emotion engine generates messages tailored to the user's emotions, providing reassurance and support.

[0415] Step 13:

[0416] After the climb is completed, the server analyzes route usage and emotional data to identify areas of physical deterioration and psychological risk factors. Administrators are then notified of any necessary improvements.

[0417] (Example 2)

[0418] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0419] Ensuring safety during mountain climbing in mountainous regions and improving user convenience are essential. Specifically, it is necessary to accurately assess risks using geographic and meteorological information, and to utilize climbers' location and biometric information in real time to provide early response to emergencies and appropriate support tailored to the user's emotional state. The challenge lies in providing highly accurate and rapid safety support in mountainous areas, which was difficult with conventional systems.

[0420] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0421] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for acquiring biometric information, analyzing emotional state, and providing appropriate notifications to the user. This enables flexible and rapid safety assurance and comfortable mountain climbing support tailored to the user's current situation.

[0422] "Geographic information" refers to data that includes location-related information such as topography, roads, and landmarks, and is used to understand the conditions of a specific region.

[0423] "Past accident information" refers to records of accidents that have occurred in a specific area in the past. This information is used to analyze the causes and circumstances of these accidents in order to improve safety.

[0424] A "mountain climbing route" is a set path that climbers take to reach their destination, and it is optimized considering the terrain and safety.

[0425] "Means of assessing safety" refer to processes and techniques for analyzing risk factors and indicating the safety of specific activities or locations using numerical values ​​or standards.

[0426] "Weather information" refers to meteorological data such as temperature, precipitation, and wind speed, and is used to understand weather conditions at a specific time and place.

[0427] "Means of proposing change" refer to systems or methods for indicating safer or more effective ways or routes depending on the situation.

[0428] "Location information" refers to information used to indicate a specific point on Earth, and is usually represented by data such as latitude and longitude.

[0429] "Route guidance" refers to a system or method that provides instructions to assist travel by showing the route and direction to a destination.

[0430] "Biometric information" refers to data that indicates an individual's physiological state, such as heart rate and skin electrical activity, and is usually measured through sensors.

[0431] "Means of analyzing emotional states" refer to systems and technologies that estimate and understand a person's emotions based on their biometric information and other data.

[0432] A "rescue signal" is a signal sent to request help in an emergency, and may include location information.

[0433] "Means of supporting rescue operations" refer to systems and technologies that provide the necessary information and procedures in the event of an emergency, and support the efficient carrying out of rescue operations.

[0434] A "deteriorated area" refers to a place or part that has deteriorated physically or functionally and requires improvement.

[0435] "Means of notifying corrective measures" refers to systems or methods for communicating suggestions or procedures for correcting specific problems to relevant parties.

[0436] The system used to implement this invention provides safe mountaineering support in mountainous regions based on communication between a server, a terminal, and a user.

[0437] The server first retrieves geographical information and historical accident data from a database. A relational database management system (relational database management system) is often used for this operation, such as PostgreSQL. Weather information is also obtained in real time through an external weather data provider (e.g., the OpenWeatherMap API). To integrate this diverse information, the server processes the data using the Python pandas library.

[0438] Next, the integrated data is analyzed using a generative AI model. Specifically, generative AI such as OpenAI's GPT series is utilized. This allows for risk assessment of the hiking routes and suggests the most suitable hiking route for the user.

[0439] The user enters their destination and climbing date using their device, and this information is sent to the server. The device is assumed to be a typical smartphone, and its GPS function tracks the user's location in real time. This location information is sent to the server, which uses it to provide route guidance and, if necessary, suggest route changes.

[0440] In addition, the device acquires biometric information from sensors such as wearable devices. This includes monitoring heart rate and skin electrical activity, for example, through an Apple Watch. This data is sent to the emotion engine and used as input to analyze the user's emotional state. The emotion engine detects whether the user is stressed or fatigued and, based on that, suggests taking a break or plays relaxing music.

[0441] For example, if a user's heart rate increases by 20% during a long hike, the emotion engine will detect stress, and the device will suggest a break to the user. Furthermore, it can play relaxing music using streaming services such as Spotify.

[0442] An example of a prompt message would be: "Explain what action this system should recommend when the user's heart rate increases by 20% above normal."

[0443] In the event of an emergency, users can operate their devices to send an SOS signal. This signal includes their current location and emotional state, which the server quickly relays to rescue teams. Furthermore, a psychological support message generated by the emotion engine is sent to the user to provide reassurance.

[0444] The system configuration described above aims to improve safety during mountain climbing and enrich the user experience.

[0445] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0446] Step 1:

[0447] The server acquires geographical information and historical accident data. This allows it to collect foundational data for safety assessments. The input is queries from a database, and the output is a dataset containing that information. The server retrieves the information via a relational database management system and prepares it for use in subsequent analysis processes.

[0448] Step 2:

[0449] The server retrieves real-time weather data from weather information providers. This is done via an API, and the output includes data such as temperature, precipitation, and wind speed. The server integrates this data with the geographic information obtained in the previous step to generate a comprehensive dataset.

[0450] Step 3:

[0451] The server analyzes the integrated data using a generative AI model. This process incorporates geographical information, historical accident data, and weather data. As a result of the analysis, a safety score for the hiking route is generated. Specifically, AI models such as OpenAI's GPT series are used for this analysis, performing scoring based on the type and location of risk.

[0452] Step 4:

[0453] The user enters their hiking destination and date into the device. This information is sent to the server and used as input. Based on the analysis results, the server calculates the optimal hiking route and sends a suggestion to the user's device as output. The user can then view the route, whose safety has been assessed, on the screen.

[0454] Step 5:

[0455] The device uses GPS to acquire the user's location information in real time. The acquired location information is sent to the server and used as input information. Based on this data, the server tracks the user's route and generates navigation information and route change suggestions as output. Route change notifications are sent in real time as needed.

[0456] Step 6:

[0457] The device collects biometric information from wearable devices, including heart rate and skin electrical activity. The acquired data is sent to an emotion engine, which analyzes it to estimate the user's emotional state. For example, it might detect stress levels and suggest the user take a break.

[0458] Step 7:

[0459] If an emergency occurs while mountaineering, the user can send an SOS signal from their device. The input includes the user's current location and emotional state. This information is sent to a server and quickly relayed to rescue teams. The server calculates the optimal rescue route and provides it to the rescue team. Additionally, a psychological support message generated by an emotion engine is sent to the device.

[0460] Step 8:

[0461] The server analyzes all data collected after the climb is completed. Input includes all data related to the climbing route, and output identifies deteriorated sections and risk areas along the route. This allows for the development of improvement measures for future climbs, which are then communicated to administrators. Through this process, continuous safety improvements are ensured.

[0462] (Application Example 2)

[0463] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0464] Conventional mountain climbing support systems primarily rely on geographical and meteorological information for safety assessments, lacking individualized support that considers the user's emotional state. This can lead to users continuing their climbs while experiencing significant psychological distress. Furthermore, while there is a need to monitor the user's health in real time and suggest appropriate actions, such implementations have not been common.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0466] In this invention, the server includes means for evaluating the safety of a climbing route based on geographical information and past accident information, means for acquiring weather information and suggesting changes to the climbing route based on that information, and means for detecting the user's biometric information and inferring their emotional state. This enables the provision of appropriate support according to the user's psychological state, making a safe and comfortable climbing experience possible.

[0467] "Geographic information" refers to data that includes information about the location and topography of a specific region or place.

[0468] "Past accident information" refers to detailed records and data about accidents that have occurred to date.

[0469] "Means for evaluating the safety of mountain climbing routes" refers to a function that uses geographical information and past accident data to analyze the risks of mountain climbing routes and determine their level of safety.

[0470] "Weather information" refers to data that shows the weather conditions of a region with geographical characteristics, such as temperature, precipitation, and wind speed.

[0471] "Means of providing route guidance" refers to functions that provide users with the optimal route and instructions in real time.

[0472] "Means for transmitting rescue signals and supporting rescue operations" refers to technical measures that enable users to transmit rescue requests in emergencies and provide rapid assistance.

[0473] "A means of identifying deteriorated sections of a route and notifying of improvement measures" refers to a mechanism that identifies dangerous sections along a route after a climb and recommends improvements to ensure safety for future climbs.

[0474] "Biometric information" refers to data such as heart rate and skin electrical activity that indicate an individual's health status.

[0475] "Means for inferring emotional state" refer to algorithms and devices that analyze and determine a user's psychological state based on their biometric information.

[0476] "Means of providing appropriate notifications and suggestions to users based on their emotional state" refers to functions that take into account the user's psychological state and provide appropriate advice and information.

[0477] This invention is a system that provides the necessary support for users to enjoy mountain climbing with peace of mind. Specifically, a server, terminals, and an emotion analysis engine work together.

[0478] The server collects and combines geographical information, past accident data, and weather information to suggest the optimal hiking route to the user. This uses a database as hardware and software such as Python and generative AI models. Data analysis is used to assess safety risks.

[0479] The device acquires the user's location information in real time. This allows it to understand how the user is moving and provide safe route guidance. The device also uses biosensors to collect data such as the user's heart rate and skin's electrical activity, and sends this information to an emotion analysis engine to analyze their emotional state. For example, if it detects stress caused by prolonged mountain climbing, the system will provide support such as playing relaxing music.

[0480] Receiving appropriate support based on users' emotional state can reduce psychological burden and lead to a better climbing experience. This can alleviate anxiety and pressure felt during climbing, thereby increasing safety.

[0481] For example, if a user feels fatigued, the emotion engine can detect this and the device can send advice such as, "Perhaps it's time for a break?" An example of an input prompt for the generative AI model is, "Analyze heart rate data and skin potential data in real time and estimate the stress level."

[0482] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0483] Step 1:

[0484] The server collects geographical information, past accident data, and weather information from a database. This information is used as foundational data for evaluating the safety of mountain climbing routes. The input is a set of information from the database, and the output is integrated geographical and weather data. This integrated data is used in the next risk assessment step.

[0485] Step 2:

[0486] The server analyzes geographical and meteorological data collected using a generative AI model to assess the risks of hiking routes. The AI ​​model's input is integrated geographical and meteorological data. As part of the data processing, an algorithm is applied to score the degree of risk, and a safety assessment is performed. The output is the assessed risk score and the recommended optimal hiking route.

[0487] Step 3:

[0488] The user uses a terminal to enter their destination and hiking date. This information is sent to the server, and the optimal hiking route is suggested. The input in this step is the destination and date / time data entered by the user from the terminal, and the output is the optimal route returned by the server.

[0489] Step 4:

[0490] The device acquires the user's location information in real time and sends it to the server. This allows the server to monitor the user's progress and provide safe route guidance. The input is location data from the GPS sensor, and the output is current location information sent to the server. This location information is used for route guidance in the next step.

[0491] Step 5:

[0492] The device acquires the user's biometric information from sensors and transmits it to the emotion analysis engine. At this stage, the necessary inputs are heart rate and skin electrical activity data from biosensors, and the output is the estimated emotional state. The emotion analysis engine processes this data, analyzes the psychological state, and outputs the result.

[0493] Step 6:

[0494] When a user experiences fatigue or stress, the emotion analysis engine detects their state, and the device provides the user with a notification prompting them to take a break or change their mood. The input for this step is emotional state data from the emotion analysis engine, and the output is a notification message to the user suggesting a break or a change of pace. Specific actions may include playing relaxing music.

[0495] Step 7:

[0496] In the event of an emergency, the user operates the device to send an SOS signal. The input for the SOS signal is a user-initiated button press or similar action, and the output is a rescue request signal that includes current location information and emotional state data. The server receives this signal and quickly transmits it to rescue teams.

[0497] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0498] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0499] 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.

[0500] [Third Embodiment]

[0501] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0502] 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.

[0503] 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).

[0504] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0505] 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.

[0506] 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).

[0507] 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.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] 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.

[0512] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0513] This invention is a system that prevents accidents in mountainous areas and enables a rapid response in emergencies. This system functions through the cooperation of three parties: a server, terminals, and users.

[0514] First, the server collects detailed geographical information and historical accident data for mountainous regions from a database. This data is analyzed using a generative AI model and used to assess the safety of climbing routes. Furthermore, it obtains current weather data from weather information providers and integrates it with the geographical information to perform a real-time risk assessment. The results of this assessment serve as an important guide for users when selecting safe climbing routes.

[0515] Next, the user enters their destination and climbing date into the device. Based on this, the server calculates the optimal climbing route and presents it to the device. The user can review the suggested route and make changes as needed. Once the user begins climbing, the device constantly acquires the user's location information using GPS and transmits it to the server in real time.

[0516] During the climb, the device continuously monitors the user's location and issues a warning if it detects a deviation from the route. Furthermore, it receives weather information updates from the server and, if new risks arise, notifies the user to change their route or descend.

[0517] In an emergency, the user operates the device to send an SOS signal. This signal, including the user's current location and situation, is transmitted to a server. Based on this information, the server notifies rescue teams and develops a rescue plan using the shortest route. This entire process enables rapid and accurate rescue operations.

[0518] After the climb is completed, the server analyzes the data collected along the route to identify areas of physical deterioration and those at risk. This information is then communicated to relevant administrators to encourage maintenance and improvements to the hiking trails.

[0519] The system of this invention allows climbers to enjoy a safer climbing experience and receive effective rescue in the event of an accident. Specifically, the server monitors the deterioration status of wooden bridges used on specific climbing routes, predicts when maintenance will be needed, and notifies the administrator, thereby improving safety.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The server collects detailed geographical information and past accident data for mountainous areas from a database. This data is then passed as input to the analysis engine.

[0523] Step 2:

[0524] The server uses a generative AI model to analyze the received data and create a risk assessment score for the safety of each climbing route. This result is recorded in a separate database.

[0525] Step 3:

[0526] The server retrieves current weather data from weather information providers using APIs and integrates it with geographical information. This updates the risk assessment in real time.

[0527] Step 4:

[0528] The user enters the desired mountainous region and climbing date into the terminal. This input data is sent to the server, and the system begins suggesting appropriate climbing routes.

[0529] Step 5:

[0530] The server calculates the optimal climbing route based on the user's request and risk assessment score. The calculated route information is then sent to the user's device.

[0531] Step 6:

[0532] The terminal displays route information received from the server to the user. The user reviews this information and makes minor adjustments to the route as needed.

[0533] Step 7:

[0534] Once a user begins climbing, the device constantly acquires the user's location information via GPS and transmits it to the server in real time. This data is continuously processed by a monitoring system.

[0535] Step 8:

[0536] The device uses the user's location data to issue a warning if it detects a deviation from the route. This information provides the user with options for appropriate action.

[0537] Step 9:

[0538] The server periodically updates weather information and reassessss the risks along the route. If weather conditions worsen, it sends a notification to the device, prompting the user to change their route or descend the mountain.

[0539] Step 10:

[0540] If a user encounters an emergency, they press the emergency button on their device to send an SOS signal. This signal is then forwarded to the server as a report, including the user's location and situation.

[0541] Step 11:

[0542] Upon receiving an SOS signal, the server sends an emergency notification to the rescue team and calculates the optimal rescue route. It provides detailed information to the rescue team to support rapid rescue operations.

[0543] Step 12:

[0544] The server analyzes the data collected after the climb to identify areas of deterioration along the route used. Based on this, it notifies the administrator of the need for improvements.

[0545] (Example 1)

[0546] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] In mountainous regions and hazardous terrain, there is a need for rapid and appropriate safety assessments using readily available information, as well as quick responses to sudden weather changes. However, existing technologies do not adequately perform detailed risk assessments that reflect geographical and meteorological information, nor do they provide sufficient real-time user follow-up, leading to problems such as accidents and people getting lost. This invention aims to solve these problems and improve user safety.

[0548] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0549] In this invention, the server includes means for evaluating the safety of a travel route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the travel route based on that information, and means for acquiring the user's location information and providing real-time route guidance. This enables a system in which the user always receives navigation based on the latest safety information and can flexibly respond to sudden dangers.

[0550] "Geographic information" refers to information about geographical characteristics such as topography, terrain, and land use.

[0551] "Accident information" refers to records and data concerning the details, circumstances, and causes of accidents that have occurred in the past.

[0552] A "travel route" is the path or route taken when traveling to reach a specific destination.

[0553] "Safety assessment" is a process for measuring and analyzing the probability of accidents or hazards occurring in specific situations or activities, and for determining the need for countermeasures.

[0554] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed.

[0555] "Means of suggesting change" refer to methods or functions that suggest alternative options or actions to users in response to changing circumstances.

[0556] "Location information" refers to data that indicates the geographical location of a specific place or object.

[0557] "Real-time route guidance" is a service that immediately displays the optimal travel route based on your current location and circumstances.

[0558] An "emergency situation" is a state of imminent danger that deviates from normal circumstances and requires a swift response.

[0559] A "rescue signal" is a signal or message transmitted to inform the outside world of an emergency.

[0560] A "deteriorated area" refers to a part whose performance or function has been reduced due to physical damage or wear.

[0561] "Improvement measures" are specific methods or actions to solve existing problems and make the situation better.

[0562] An "artificial intelligence model" is a set of algorithms that allow computers to analyze data, learn, and make inferences in the same way as humans.

[0563] "Analysis" is the act of consideration aimed at breaking things down into their constituent elements and clarifying their meaning and relationships.

[0564] "User" refers to an individual or group that uses a system or application.

[0565] This invention provides a system that enables the design of safe travel routes and emergency response in mountainous regions, and functions through the coordinated operation of a server, terminals, and users.

[0566] The server performs safety assessments using geographical information collected from terrain databases and accident recording systems, along with historical accident data. Using a generative AI model, it analyzes vast amounts of data to determine hazards along a route under specific conditions. This information is used for user risk management. The server also obtains the latest weather data through weather information service APIs and uses this data to suggest changes to travel routes. For example, the server can utilize the OpenWeatherMap API to collect information on temperature, wind speed, and rainfall, which can then be used for risk assessment.

[0567] The terminal's role is to transmit information to the server when the user enters their destination and planned travel date. The server then presents the user with the optimal route information and provides real-time route guidance. The terminal constantly monitors the user's location using a GPS module and transmits location data to the server. Based on the location information, it checks whether the user is deviating from the suggested route and issues a warning if a deviation is detected.

[0568] Users can carry the device while on the move and select a safe route according to suggestions from the server. In an emergency, they can use the device's emergency signal transmission function to send an SOS signal to the server, including their current location and situation. This procedure allows the server to quickly notify rescue organizations and take action to ensure the user's safety.

[0569] Example of a prompt message as a concrete example: "Please perform a real-time travel risk assessment under the following conditions: destination is point X, travel date is October 10, 2023, current weather conditions are cloudy, wind speed is 5 m / s. Please provide a safety assessment considering past accident cases."

[0570] As a result, the system utilizes advanced AI technology to provide users with safe and efficient travel information and is equipped to respond quickly in emergencies.

[0571] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0572] Step 1:

[0573] The server collects geographical information and historical accident data for mountainous regions from a terrain database and accident record system. Inputs include map data and accident history, and data analysis is performed using a generative AI model. The output generates risk assessment results for travel routes in a specific region. Specifically, the server executes a Python script, uses database queries to retrieve the necessary datasets, and inputs them into the AI ​​model.

[0574] Step 2:

[0575] The server obtains current weather data through the API of a weather information service. Input includes API requests, and output provides the latest weather information regarding temperature, wind speed, and precipitation. The server integrates this weather data with geographical information to update the risk model and reassess the safety of travel routes. Specifically, the server retrieves data in JSON format from the OpenWeatherMap API, parses it, and incorporates it into the risk assessment.

[0576] Step 3:

[0577] The terminal sends the destination and planned travel date entered by the user to the server. The input includes the user's destination and date information, and the output is the server's suggested optimal travel route. Based on the risk assessment, the server evaluates multiple routes, selects the optimal route using a generating AI model, and sends it to the terminal. Specifically, the terminal uses a form screen to obtain information from the user and sends it to the server as an HTTP request.

[0578] Step 4:

[0579] The terminal presents the user with optimal travel route information obtained from the server. The input is route information sent from the server, and the output is route guidance displayed on the user interface. Furthermore, the terminal monitors the user's location in real time using GPS and sends this data to the server. Specifically, the terminal uses the Google Maps API to draw the route on a map and updates the location in real time.

[0580] Step 5:

[0581] In an emergency, the user presses the SOS button on their device to send their current GPS location and status to the server. The input includes the user's location information and emergency status, and the output is a signal to support rescue operations. Based on this information, the server quickly develops a rescue plan and arranges for assistance. Specifically, the device packages its location information and SOS status and sends it quickly to the server, enabling real-time rescue support.

[0582] (Application Example 1)

[0583] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] The present invention aims to provide a system that prevents accidents in mountainous areas and emergencies in urban areas, enabling a rapid and effective response. Conventional technologies are specialized for specific regions and situations, making comprehensive risk assessment and information provision difficult. Therefore, there is a need for broader disaster prevention measures and the provision of safety information.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0586] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographic information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for integrating mountain climbing and disaster prevention information in urban areas, performing risk assessments, and providing safety information. This enables the provision of safe mountain climbing experiences in mountainous regions and rapid disaster response in urban areas.

[0587] "Geographic information" refers to information about the topography, terrain, and features of a specific area, and is important basic data for setting hiking routes and disaster prevention planning.

[0588] "Past accident information" refers to records of accidents and incidents that have occurred in the past, and is data used for safety assessments and risk prediction.

[0589] A "mountain climbing route" is a path established for climbers to travel within a mountainous region, and route selection must be based on safety and weather conditions.

[0590] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed, and plays an important role in mountaineering and urban disaster prevention planning.

[0591] "User location information" refers to data that indicates the current location of a specific user using technologies such as GPS, and is used for route guidance and safety checks.

[0592] A "rescue signal" is a signal transmitted in an emergency to request rescue, and is used to quickly convey the user's location and situation.

[0593] "Disaster prevention information" refers to information regarding safe actions to take during a disaster, evacuation locations, and the progression of the situation, and is a foundation that supports the safety of individuals and society.

[0594] "Risk assessment" is an analytical process that uses geographical information, meteorological information, and past accident data to measure the safety of a particular situation or area and predict potential hazards.

[0595] This system is configured to function through the collaboration of three parties: the server, the terminal, and the user. First, the server collects geographic information, past accident information, and current weather information via the network. Geographic information is obtained using a GIS (Geographic Information System), and accident information is acquired from a database. Weather information is obtained from a weather data provider via an API. This collected information is analyzed using a generative AI model to generate a numerical model for risk assessment.

[0596] Specifically, the server uses Python to build and operate a neural network model that assesses risk using deep learning frameworks such as TensorFlow or PyTorch. This model will evaluate the risks of mountain climbing routes and urban areas in real time, providing users with safe information.

[0597] The device is envisioned as a smartphone or tablet, and will continuously acquire the user's location information using a GPS module. The device will also use a push notification service such as Firebase Cloud Messaging to receive push notifications from the server and present new risk information and safety measures to the user in real time.

[0598] When a user begins a climb or journey, they set their destination and itinerary on their device. Based on this, the server provides optimal route information. While climbing or traveling, the device monitors the user's location and route deviations through the output of a generated AI, and has the function to issue warnings as needed. It is also possible to send an SOS signal from the device, transmitting information to the server in case of an emergency, which can lead to rapid rescue operations.

[0599] For example, if a user is planning a weekend hike, the server can send a notification to their device saying, "The current weather is expected to change suddenly. For your safety, we recommend changing your route from Route A to Route B." Furthermore, in the event of an unexpected disaster in a city, evacuation advisories and safety information can be quickly delivered to the user.

[0600] An example of a prompt message would be, "Please tell me about safe hiking routes in the nearby mountainous area. Please also take current weather conditions into consideration."

[0601] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0602] Step 1:

[0603] The server retrieves geographic information from a geographic information system. This information is necessary for setting hiking routes and urban disaster prevention planning. The retrieved information is stored in a database and input into a generating AI model.

[0604] Step 2:

[0605] The server retrieves past accident information from a dedicated database. This information includes detailed data on past accidents and disasters. Based on the retrieved accident information, a generative AI model is used to analyze patterns that serve as criteria for risk assessment.

[0606] Step 3:

[0607] The server collects current weather information in real time through the API of a weather data provider. The collected weather information includes weather, temperature, wind speed, etc., and this data is integrated with geographic information and accident information and used as input for AI models.

[0608] Step 4:

[0609] The AI-generated model allows the server to integrate and analyze geographical information, accident information, and weather information to perform a risk assessment. This analysis calculates safe routes and disaster prevention information for urban areas, and prepares this information as output for terminals.

[0610] Step 5:

[0611] The user enters their destination and itinerary into the device. The device then sends the entered data to the server. This data includes the user's location, destination, and desired activities.

[0612] Step 6:

[0613] The server calculates the optimal route based on the location and destination information received from the user. This calculation incorporates the results of an AI-based risk assessment, and the resulting optimal route information is sent to the terminal.

[0614] Step 7:

[0615] The terminal receives optimal route information from the server and notifies the user. The notified information includes details of a safe route and any necessary precautions. The user uses this information to confirm their itinerary and modify their plan as needed.

[0616] Step 8:

[0617] The device continuously tracks the user's location using GPS. This location information is also transmitted to the server, and a warning is issued if the user deviates from their route.

[0618] Step 9:

[0619] In an emergency, the user operates the device to send an SOS signal. This signal includes the user's current location and situation, and the server receives it and notifies rescue teams to support rapid rescue operations.

[0620] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0621] This invention is a system that prevents accidents in mountainous areas and provides support that takes into account the user's emotional state. This system achieves its functionality through the collaboration of a server, a terminal, and an emotion engine that analyzes the user's emotions.

[0622] First, the server collects geographical information and historical accident data for mountainous areas from a database. In parallel, it obtains real-time data such as temperature, precipitation, and wind speed from weather information providers and integrates it with the geographical information. The server then analyzes this information using a generative AI model to perform a safety risk assessment. Based on the risk assessment, it proposes the most suitable climbing route to the user.

[0623] Next, the user uses the device to enter their destination and hiking date. The user's input information is sent to the server, and an appropriate hiking route is calculated. Once the hike begins, the device acquires the user's location information in real time and sends it to the server. Based on this data, navigation and route change notifications are provided during the hike.

[0624] Furthermore, the device acquires the user's biometric information from sensors and transmits it to the emotion engine. The emotion engine uses this data to infer the user's emotional state. For example, it can detect stress and fatigue from heart rate and skin electrical activity. Based on the results, the user receives notifications recommending breaks or equipment checks.

[0625] In the event of an emergency, the user operates their device to send an SOS signal. This signal is sent to the server as a report including the user's current location and emotional state. The server quickly transmits this information to rescue teams and proposes the optimal rescue route. Furthermore, the emotion engine can generate psychological support messages that take the user's emotional state into consideration, providing a sense of security.

[0626] After the climb is completed, the server analyzes all data obtained from the route to identify deteriorated sections and risk areas. This allows administrators to be notified of improvement measures to enhance safety for future climbs.

[0627] For example, if a user begins to feel stressed during a long climb, the emotion engine will detect this, and the device will suggest a break and provide support such as playing relaxing music. In this way, the system aims to improve quality in both safety and user experience.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server retrieves detailed geographical information and past accident data for mountainous areas from a database and inputs it into the analysis engine. This prepares the system for risk assessment of mountain climbing routes.

[0631] Step 2:

[0632] The server retrieves real-time weather data from weather information providers. This data includes temperature, precipitation, and wind speed, and when integrated with geographic information, it improves the accuracy of risk assessments.

[0633] Step 3:

[0634] The server analyzes geographic and weather data collected using a generative AI model to calculate a risk assessment score indicating the safety of each hiking route. This information is used for subsequent processing.

[0635] Step 4:

[0636] The user enters the desired mountainous region and climbing date into their device. This information is sent to the server, and the system begins suggesting appropriate climbing routes.

[0637] Step 5:

[0638] The server calculates the optimal climbing route based on a risk assessment score for the user's desired route. The route information is then sent to the user's device.

[0639] Step 6:

[0640] The device displays the received route information and prompts the user for confirmation. The user can review the suggested route and request changes if necessary.

[0641] Step 7:

[0642] Once the user begins climbing, the device continuously acquires the user's location information using GPS and transmits it to the server in real time.

[0643] Step 8:

[0644] The device uses built-in sensors to acquire the user's biometric information and transmits it to the emotion engine. The emotion engine then uses this information to analyze the user's emotional state.

[0645] Step 9:

[0646] The emotion engine detects stress and fatigue from the user's emotional data and generates appropriate responses based on that state. For example, it can generate suggestions to take a break or encouraging messages.

[0647] Step 10:

[0648] The server monitors weather information, which is updated in real time, and immediately sends a notification to the user's terminal if a risk occurs. The terminal then prompts the user to change their route or descend the mountain.

[0649] Step 11:

[0650] If a user encounters an emergency, they can use the device's SOS function to send a signal. This signal, which includes location information and emotional state, is sent to the server.

[0651] Step 12:

[0652] The server receives an SOS signal, quickly sends an emergency notification to rescue teams, and calculates the optimal rescue route. The emotion engine generates messages tailored to the user's emotions, providing reassurance and support.

[0653] Step 13:

[0654] After the climb is completed, the server analyzes route usage and emotional data to identify areas of physical deterioration and psychological risk factors. Administrators are then notified of any necessary improvements.

[0655] (Example 2)

[0656] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0657] Ensuring safety during mountain climbing in mountainous regions and improving user convenience are essential. Specifically, it is necessary to accurately assess risks using geographic and meteorological information, and to utilize climbers' location and biometric information in real time to provide early response to emergencies and appropriate support tailored to the user's emotional state. The challenge lies in providing highly accurate and rapid safety support in mountainous areas, which was difficult with conventional systems.

[0658] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0659] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for acquiring biometric information, analyzing emotional state, and providing appropriate notifications to the user. This enables flexible and rapid safety assurance and comfortable mountain climbing support tailored to the user's current situation.

[0660] "Geographic information" refers to data that includes location-related information such as topography, roads, and landmarks, and is used to understand the conditions of a specific region.

[0661] "Past accident information" refers to records of accidents that have occurred in a specific area in the past. This information is used to analyze the causes and circumstances of these accidents in order to improve safety.

[0662] A "mountain climbing route" is a set path that climbers take to reach their destination, and it is optimized considering the terrain and safety.

[0663] "Means of assessing safety" refer to processes and techniques for analyzing risk factors and indicating the safety of specific activities or locations using numerical values ​​or standards.

[0664] "Weather information" refers to meteorological data such as temperature, precipitation, and wind speed, and is used to understand weather conditions at a specific time and place.

[0665] "Means of proposing change" refer to systems or methods for indicating safer or more effective ways or routes depending on the situation.

[0666] "Location information" refers to information used to indicate a specific point on Earth, and is usually represented by data such as latitude and longitude.

[0667] "Route guidance" refers to a system or method that provides instructions to assist travel by showing the route and direction to a destination.

[0668] "Biometric information" refers to data that indicates an individual's physiological state, such as heart rate and skin electrical activity, and is usually measured through sensors.

[0669] "Means of analyzing emotional states" refer to systems and technologies that estimate and understand a person's emotions based on their biometric information and other data.

[0670] A "rescue signal" is a signal sent to request help in an emergency, and may include location information.

[0671] "Means of supporting rescue operations" refer to systems and technologies that provide the necessary information and procedures in the event of an emergency, and support the efficient carrying out of rescue operations.

[0672] A "deteriorated area" refers to a place or part that has deteriorated physically or functionally and requires improvement.

[0673] "Means of notifying corrective measures" refers to systems or methods for communicating suggestions or procedures for correcting specific problems to relevant parties.

[0674] The system used to implement this invention provides safe mountaineering support in mountainous regions based on communication between a server, a terminal, and a user.

[0675] The server first retrieves geographical information and historical accident data from a database. A relational database management system (relational database management system) is often used for this operation, such as PostgreSQL. Weather information is also obtained in real time through an external weather data provider (e.g., the OpenWeatherMap API). To integrate this diverse information, the server processes the data using the Python pandas library.

[0676] Next, the integrated data is analyzed using a generative AI model. Specifically, generative AI such as OpenAI's GPT series is utilized. This allows for risk assessment of the hiking routes and suggests the most suitable hiking route for the user.

[0677] The user enters their destination and climbing date using their device, and this information is sent to the server. The device is assumed to be a typical smartphone, and its GPS function tracks the user's location in real time. This location information is sent to the server, which uses it to provide route guidance and, if necessary, suggest route changes.

[0678] In addition, the device acquires biometric information from sensors such as wearable devices. This includes monitoring heart rate and skin electrical activity, for example, through an Apple Watch. This data is sent to the emotion engine and used as input to analyze the user's emotional state. The emotion engine detects whether the user is stressed or fatigued and, based on that, suggests taking a break or plays relaxing music.

[0679] For example, if a user's heart rate increases by 20% during a long hike, the emotion engine will detect stress, and the device will suggest a break to the user. Furthermore, it can play relaxing music using streaming services such as Spotify.

[0680] An example of a prompt message would be: "Explain what action this system should recommend when the user's heart rate increases by 20% above normal."

[0681] In the event of an emergency, users can operate their devices to send an SOS signal. This signal includes their current location and emotional state, which the server quickly relays to rescue teams. Furthermore, a psychological support message generated by the emotion engine is sent to the user to provide reassurance.

[0682] The system configuration described above aims to improve safety during mountain climbing and enrich the user experience.

[0683] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0684] Step 1:

[0685] The server acquires geographical information and historical accident data. This allows it to collect foundational data for safety assessments. The input is queries from a database, and the output is a dataset containing that information. The server retrieves the information via a relational database management system and prepares it for use in subsequent analysis processes.

[0686] Step 2:

[0687] The server retrieves real-time weather data from weather information providers. This is done via an API, and the output includes data such as temperature, precipitation, and wind speed. The server integrates this data with the geographic information obtained in the previous step to generate a comprehensive dataset.

[0688] Step 3:

[0689] The server analyzes the integrated data using a generative AI model. This process incorporates geographical information, historical accident data, and weather data. As a result of the analysis, a safety score for the hiking route is generated. Specifically, AI models such as OpenAI's GPT series are used for this analysis, performing scoring based on the type and location of risk.

[0690] Step 4:

[0691] The user enters their hiking destination and date into the device. This information is sent to the server and used as input. Based on the analysis results, the server calculates the optimal hiking route and sends a suggestion to the user's device as output. The user can then view the route, whose safety has been assessed, on the screen.

[0692] Step 5:

[0693] The device uses GPS to acquire the user's location information in real time. The acquired location information is sent to the server and used as input information. Based on this data, the server tracks the user's route and generates navigation information and route change suggestions as output. Route change notifications are sent in real time as needed.

[0694] Step 6:

[0695] The device collects biometric information from wearable devices, including heart rate and skin electrical activity. The acquired data is sent to an emotion engine, which analyzes it to estimate the user's emotional state. For example, it might detect stress levels and suggest the user take a break.

[0696] Step 7:

[0697] If an emergency occurs while mountaineering, the user can send an SOS signal from their device. The input includes the user's current location and emotional state. This information is sent to a server and quickly relayed to rescue teams. The server calculates the optimal rescue route and provides it to the rescue team. Additionally, a psychological support message generated by an emotion engine is sent to the device.

[0698] Step 8:

[0699] The server analyzes all data collected after the climb is completed. Input includes all data related to the climbing route, and output identifies deteriorated sections and risk areas along the route. This allows for the development of improvement measures for future climbs, which are then communicated to administrators. Through this process, continuous safety improvements are ensured.

[0700] (Application Example 2)

[0701] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0702] Conventional mountain climbing support systems primarily rely on geographical and meteorological information for safety assessments, lacking individualized support that considers the user's emotional state. This can lead to users continuing their climbs while experiencing significant psychological distress. Furthermore, while there is a need to monitor the user's health in real time and suggest appropriate actions, such implementations have not been common.

[0703] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0704] In this invention, the server includes means for evaluating the safety of a climbing route based on geographical information and past accident information, means for acquiring weather information and suggesting changes to the climbing route based on that information, and means for detecting the user's biometric information and inferring their emotional state. This enables the provision of appropriate support according to the user's psychological state, making a safe and comfortable climbing experience possible.

[0705] "Geographic information" refers to data that includes information about the location and topography of a specific region or place.

[0706] "Past accident information" refers to detailed records and data about accidents that have occurred to date.

[0707] "Means for evaluating the safety of mountain climbing routes" refers to a function that uses geographical information and past accident data to analyze the risks of mountain climbing routes and determine their level of safety.

[0708] "Weather information" refers to data that shows the weather conditions of a region with geographical characteristics, such as temperature, precipitation, and wind speed.

[0709] "Means of providing route guidance" refers to functions that provide users with the optimal route and instructions in real time.

[0710] "Means for transmitting rescue signals and supporting rescue operations" refers to technical measures that enable users to transmit rescue requests in emergencies and provide rapid assistance.

[0711] "A means of identifying deteriorated sections of a route and notifying of improvement measures" refers to a mechanism that identifies dangerous sections along a route after a climb and recommends improvements to ensure safety for future climbs.

[0712] "Biometric information" refers to data such as heart rate and skin electrical activity that indicate an individual's health status.

[0713] "Means for inferring emotional state" refer to algorithms and devices that analyze and determine a user's psychological state based on their biometric information.

[0714] "Means of providing appropriate notifications and suggestions to users based on their emotional state" refers to functions that take into account the user's psychological state and provide appropriate advice and information.

[0715] This invention is a system that provides the necessary support for users to enjoy mountain climbing with peace of mind. Specifically, a server, terminals, and an emotion analysis engine work together.

[0716] The server collects and combines geographical information, past accident data, and weather information to suggest the optimal hiking route to the user. This uses a database as hardware and software such as Python and generative AI models. Data analysis is used to assess safety risks.

[0717] The device acquires the user's location information in real time. This allows it to understand how the user is moving and provide safe route guidance. The device also uses biosensors to collect data such as the user's heart rate and skin's electrical activity, and sends this information to an emotion analysis engine to analyze their emotional state. For example, if it detects stress caused by prolonged mountain climbing, the system will provide support such as playing relaxing music.

[0718] Receiving appropriate support based on users' emotional state can reduce psychological burden and lead to a better climbing experience. This can alleviate anxiety and pressure felt during climbing, thereby increasing safety.

[0719] For example, if a user feels fatigued, the emotion engine can detect this and the device can send advice such as, "Perhaps it's time for a break?" An example of an input prompt for the generative AI model is, "Analyze heart rate data and skin potential data in real time and estimate the stress level."

[0720] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0721] Step 1:

[0722] The server collects geographical information, past accident data, and weather information from a database. This information is used as foundational data for evaluating the safety of mountain climbing routes. The input is a set of information from the database, and the output is integrated geographical and weather data. This integrated data is used in the next risk assessment step.

[0723] Step 2:

[0724] The server analyzes geographical and meteorological data collected using a generative AI model to assess the risks of hiking routes. The AI ​​model's input is integrated geographical and meteorological data. As part of the data processing, an algorithm is applied to score the degree of risk, and a safety assessment is performed. The output is the assessed risk score and the recommended optimal hiking route.

[0725] Step 3:

[0726] The user uses a terminal to enter their destination and hiking date. This information is sent to the server, and the optimal hiking route is suggested. The input in this step is the destination and date / time data entered by the user from the terminal, and the output is the optimal route returned by the server.

[0727] Step 4:

[0728] The device acquires the user's location information in real time and sends it to the server. This allows the server to monitor the user's progress and provide safe route guidance. The input is location data from the GPS sensor, and the output is current location information sent to the server. This location information is used for route guidance in the next step.

[0729] Step 5:

[0730] The device acquires the user's biometric information from sensors and transmits it to the emotion analysis engine. At this stage, the necessary inputs are heart rate and skin electrical activity data from biosensors, and the output is the estimated emotional state. The emotion analysis engine processes this data, analyzes the psychological state, and outputs the result.

[0731] Step 6:

[0732] When a user experiences fatigue or stress, the emotion analysis engine detects their state, and the device provides the user with a notification prompting them to take a break or change their mood. The input for this step is emotional state data from the emotion analysis engine, and the output is a notification message to the user suggesting a break or a change of pace. Specific actions may include playing relaxing music.

[0733] Step 7:

[0734] In the event of an emergency, the user operates the device to send an SOS signal. The input for the SOS signal is a user-initiated button press or similar action, and the output is a rescue request signal that includes current location information and emotional state data. The server receives this signal and quickly transmits it to rescue teams.

[0735] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0736] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0737] 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.

[0738] [Fourth Embodiment]

[0739] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0740] 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.

[0741] 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).

[0742] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0743] 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.

[0744] 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).

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] This invention is a system that prevents accidents in mountainous areas and enables a rapid response in emergencies. This system functions through the cooperation of three parties: a server, terminals, and users.

[0753] First, the server collects detailed geographical information and historical accident data for mountainous regions from a database. This data is analyzed using a generative AI model and used to assess the safety of climbing routes. Furthermore, it obtains current weather data from weather information providers and integrates it with the geographical information to perform a real-time risk assessment. The results of this assessment serve as an important guide for users when selecting safe climbing routes.

[0754] Next, the user enters their destination and climbing date into the device. Based on this, the server calculates the optimal climbing route and presents it to the device. The user can review the suggested route and make changes as needed. Once the user begins climbing, the device constantly acquires the user's location information using GPS and transmits it to the server in real time.

[0755] During the climb, the device continuously monitors the user's location and issues a warning if it detects a deviation from the route. Furthermore, it receives weather information updates from the server and, if new risks arise, notifies the user to change their route or descend.

[0756] In an emergency, the user operates the device to send an SOS signal. This signal, including the user's current location and situation, is transmitted to a server. Based on this information, the server notifies rescue teams and develops a rescue plan using the shortest route. This entire process enables rapid and accurate rescue operations.

[0757] After the climb is completed, the server analyzes the data collected along the route to identify areas of physical deterioration and those at risk. This information is then communicated to relevant administrators to encourage maintenance and improvements to the hiking trails.

[0758] The system of this invention allows climbers to enjoy a safer climbing experience and receive effective rescue in the event of an accident. Specifically, the server monitors the deterioration status of wooden bridges used on specific climbing routes, predicts when maintenance will be needed, and notifies the administrator, thereby improving safety.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The server collects detailed geographical information and past accident data for mountainous areas from a database. This data is then passed as input to the analysis engine.

[0762] Step 2:

[0763] The server uses a generative AI model to analyze the received data and create a risk assessment score for the safety of each climbing route. This result is recorded in a separate database.

[0764] Step 3:

[0765] The server retrieves current weather data from weather information providers using APIs and integrates it with geographical information. This updates the risk assessment in real time.

[0766] Step 4:

[0767] The user enters the desired mountainous region and climbing date into the terminal. This input data is sent to the server, and the system begins suggesting appropriate climbing routes.

[0768] Step 5:

[0769] The server calculates the optimal climbing route based on the user's request and risk assessment score. The calculated route information is then sent to the user's device.

[0770] Step 6:

[0771] The terminal displays route information received from the server to the user. The user reviews this information and makes minor adjustments to the route as needed.

[0772] Step 7:

[0773] Once a user begins climbing, the device constantly acquires the user's location information via GPS and transmits it to the server in real time. This data is continuously processed by a monitoring system.

[0774] Step 8:

[0775] The device uses the user's location data to issue a warning if it detects a deviation from the route. This information provides the user with options for appropriate action.

[0776] Step 9:

[0777] The server periodically updates weather information and reassessss the risks along the route. If weather conditions worsen, it sends a notification to the device, prompting the user to change their route or descend the mountain.

[0778] Step 10:

[0779] If a user encounters an emergency, they press the emergency button on their device to send an SOS signal. This signal is then forwarded to the server as a report, including the user's location and situation.

[0780] Step 11:

[0781] Upon receiving an SOS signal, the server sends an emergency notification to the rescue team and calculates the optimal rescue route. It provides detailed information to the rescue team to support rapid rescue operations.

[0782] Step 12:

[0783] The server analyzes the data collected after the climb to identify areas of deterioration along the route used. Based on this, it notifies the administrator of the need for improvements.

[0784] (Example 1)

[0785] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0786] In mountainous regions and hazardous terrain, there is a need for rapid and appropriate safety assessments using readily available information, as well as quick responses to sudden weather changes. However, existing technologies do not adequately perform detailed risk assessments that reflect geographical and meteorological information, nor do they provide sufficient real-time user follow-up, leading to problems such as accidents and people getting lost. This invention aims to solve these problems and improve user safety.

[0787] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0788] In this invention, the server includes means for evaluating the safety of a travel route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the travel route based on that information, and means for acquiring the user's location information and providing real-time route guidance. This enables a system in which the user always receives navigation based on the latest safety information and can flexibly respond to sudden dangers.

[0789] "Geographic information" refers to information about geographical characteristics such as topography, terrain, and land use.

[0790] "Accident information" refers to records and data concerning the details, circumstances, and causes of accidents that have occurred in the past.

[0791] A "travel route" is the path or route taken when traveling to reach a specific destination.

[0792] "Safety assessment" is a process for measuring and analyzing the probability of accidents or hazards occurring in specific situations or activities, and for determining the need for countermeasures.

[0793] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed.

[0794] "Means of suggesting change" refer to methods or functions that suggest alternative options or actions to users in response to changing circumstances.

[0795] "Location information" refers to data that indicates the geographical location of a specific place or object.

[0796] "Real-time route guidance" is a service that immediately displays the optimal travel route based on your current location and circumstances.

[0797] An "emergency situation" is a state of imminent danger that deviates from normal circumstances and requires a swift response.

[0798] A "rescue signal" is a signal or message transmitted to inform the outside world of an emergency.

[0799] A "deteriorated area" refers to a part whose performance or function has been reduced due to physical damage or wear.

[0800] "Improvement measures" are specific methods or actions to solve existing problems and make the situation better.

[0801] An "artificial intelligence model" is a set of algorithms that allow computers to analyze data, learn, and make inferences in the same way as humans.

[0802] "Analysis" is the act of consideration aimed at breaking things down into their constituent elements and clarifying their meaning and relationships.

[0803] "User" refers to an individual or group that uses a system or application.

[0804] This invention provides a system that enables the design of safe travel routes and emergency response in mountainous regions, and functions through the coordinated operation of a server, terminals, and users.

[0805] The server performs safety assessments using geographical information collected from terrain databases and accident recording systems, along with historical accident data. Using a generative AI model, it analyzes vast amounts of data to determine hazards along a route under specific conditions. This information is used for user risk management. The server also obtains the latest weather data through weather information service APIs and uses this data to suggest changes to travel routes. For example, the server can utilize the OpenWeatherMap API to collect information on temperature, wind speed, and rainfall, which can then be used for risk assessment.

[0806] The terminal's role is to transmit information to the server when the user enters their destination and planned travel date. The server then presents the user with the optimal route information and provides real-time route guidance. The terminal constantly monitors the user's location using a GPS module and transmits location data to the server. Based on the location information, it checks whether the user is deviating from the suggested route and issues a warning if a deviation is detected.

[0807] Users can carry the device while on the move and select a safe route according to suggestions from the server. In an emergency, they can use the device's emergency signal transmission function to send an SOS signal to the server, including their current location and situation. This procedure allows the server to quickly notify rescue organizations and take action to ensure the user's safety.

[0808] Example of a prompt message as a concrete example: "Please perform a real-time travel risk assessment under the following conditions: destination is point X, travel date is October 10, 2023, current weather conditions are cloudy, wind speed is 5 m / s. Please provide a safety assessment considering past accident cases."

[0809] As a result, the system utilizes advanced AI technology to provide users with safe and efficient travel information and is equipped to respond quickly in emergencies.

[0810] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0811] Step 1:

[0812] The server collects geographical information and historical accident data for mountainous regions from a terrain database and accident record system. Inputs include map data and accident history, and data analysis is performed using a generative AI model. The output generates risk assessment results for travel routes in a specific region. Specifically, the server executes a Python script, uses database queries to retrieve the necessary datasets, and inputs them into the AI ​​model.

[0813] Step 2:

[0814] The server obtains current weather data through the API of a weather information service. Input includes API requests, and output provides the latest weather information regarding temperature, wind speed, and precipitation. The server integrates this weather data with geographical information to update the risk model and reassess the safety of travel routes. Specifically, the server retrieves data in JSON format from the OpenWeatherMap API, parses it, and incorporates it into the risk assessment.

[0815] Step 3:

[0816] The terminal sends the destination and planned travel date entered by the user to the server. The input includes the user's destination and date information, and the output is the server's suggested optimal travel route. Based on the risk assessment, the server evaluates multiple routes, selects the optimal route using a generating AI model, and sends it to the terminal. Specifically, the terminal uses a form screen to obtain information from the user and sends it to the server as an HTTP request.

[0817] Step 4:

[0818] The terminal presents the user with optimal travel route information obtained from the server. The input is route information sent from the server, and the output is route guidance displayed on the user interface. Furthermore, the terminal monitors the user's location in real time using GPS and sends this data to the server. Specifically, the terminal uses the Google Maps API to draw the route on a map and updates the location in real time.

[0819] Step 5:

[0820] In an emergency, the user presses the SOS button on their device to send their current GPS location and status to the server. The input includes the user's location information and emergency status, and the output is a signal to support rescue operations. Based on this information, the server quickly develops a rescue plan and arranges for assistance. Specifically, the device packages its location information and SOS status and sends it quickly to the server, enabling real-time rescue support.

[0821] (Application Example 1)

[0822] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0823] The present invention aims to provide a system that prevents accidents in mountainous areas and emergencies in urban areas, enabling a rapid and effective response. Conventional technologies are specialized for specific regions and situations, making comprehensive risk assessment and information provision difficult. Therefore, there is a need for broader disaster prevention measures and the provision of safety information.

[0824] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0825] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographic information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for integrating mountain climbing and disaster prevention information in urban areas, performing risk assessments, and providing safety information. This enables the provision of safe mountain climbing experiences in mountainous regions and rapid disaster response in urban areas.

[0826] "Geographic information" refers to information about the topography, terrain, and features of a specific area, and is important basic data for setting hiking routes and disaster prevention planning.

[0827] "Past accident information" refers to records of accidents and incidents that have occurred in the past, and is data used for safety assessments and risk prediction.

[0828] A "mountain climbing route" is a path established for climbers to travel within a mountainous region, and route selection must be based on safety and weather conditions.

[0829] "Weather information" refers to data on meteorological conditions such as weather, temperature, precipitation, and wind speed, and plays an important role in mountaineering and urban disaster prevention planning.

[0830] "User location information" refers to data that indicates the current location of a specific user using technologies such as GPS, and is used for route guidance and safety checks.

[0831] A "rescue signal" is a signal transmitted in an emergency to request rescue, and is used to quickly convey the user's location and situation.

[0832] "Disaster prevention information" refers to information regarding safe actions to take during a disaster, evacuation locations, and the progression of the situation, and is a foundation that supports the safety of individuals and society.

[0833] "Risk assessment" is an analytical process that uses geographical information, meteorological information, and past accident data to measure the safety of a particular situation or area and predict potential hazards.

[0834] This system is configured to function through the collaboration of three parties: the server, the terminal, and the user. First, the server collects geographic information, past accident information, and current weather information via the network. Geographic information is obtained using a GIS (Geographic Information System), and accident information is acquired from a database. Weather information is obtained from a weather data provider via an API. This collected information is analyzed using a generative AI model to generate a numerical model for risk assessment.

[0835] Specifically, the server uses Python to build and operate a neural network model that assesses risk using deep learning frameworks such as TensorFlow or PyTorch. This model will evaluate the risks of mountain climbing routes and urban areas in real time, providing users with safe information.

[0836] The device is envisioned as a smartphone or tablet, and will continuously acquire the user's location information using a GPS module. The device will also use a push notification service such as Firebase Cloud Messaging to receive push notifications from the server and present new risk information and safety measures to the user in real time.

[0837] When a user begins a climb or journey, they set their destination and itinerary on their device. Based on this, the server provides optimal route information. While climbing or traveling, the device monitors the user's location and route deviations through the output of a generated AI, and has the function to issue warnings as needed. It is also possible to send an SOS signal from the device, transmitting information to the server in case of an emergency, which can lead to rapid rescue operations.

[0838] For example, if a user is planning a weekend hike, the server can send a notification to their device saying, "The current weather is expected to change suddenly. For your safety, we recommend changing your route from Route A to Route B." Furthermore, in the event of an unexpected disaster in a city, evacuation advisories and safety information can be quickly delivered to the user.

[0839] An example of a prompt message would be, "Please tell me about safe hiking routes in the nearby mountainous area. Please also take current weather conditions into consideration."

[0840] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0841] Step 1:

[0842] The server retrieves geographic information from a geographic information system. This information is necessary for setting hiking routes and urban disaster prevention planning. The retrieved information is stored in a database and input into a generating AI model.

[0843] Step 2:

[0844] The server retrieves past accident information from a dedicated database. This information includes detailed data on past accidents and disasters. Based on the retrieved accident information, a generative AI model is used to analyze patterns that serve as criteria for risk assessment.

[0845] Step 3:

[0846] The server collects current weather information in real time through the API of a weather data provider. The collected weather information includes weather, temperature, wind speed, etc., and this data is integrated with geographic information and accident information and used as input for AI models.

[0847] Step 4:

[0848] The AI-generated model allows the server to integrate and analyze geographical information, accident information, and weather information to perform a risk assessment. This analysis calculates safe routes and disaster prevention information for urban areas, and prepares this information as output for terminals.

[0849] Step 5:

[0850] The user enters their destination and itinerary into the device. The device then sends the entered data to the server. This data includes the user's location, destination, and desired activities.

[0851] Step 6:

[0852] The server calculates the optimal route based on the location and destination information received from the user. This calculation incorporates the results of an AI-based risk assessment, and the resulting optimal route information is sent to the terminal.

[0853] Step 7:

[0854] The terminal receives optimal route information from the server and notifies the user. The notified information includes details of a safe route and any necessary precautions. The user uses this information to confirm their itinerary and modify their plan as needed.

[0855] Step 8:

[0856] The device continuously tracks the user's location using GPS. This location information is also transmitted to the server, and a warning is issued if the user deviates from their route.

[0857] Step 9:

[0858] In an emergency, the user operates the device to send an SOS signal. This signal includes the user's current location and situation, and the server receives it and notifies rescue teams to support rapid rescue operations.

[0859] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0860] This invention is a system that prevents accidents in mountainous areas and provides support that takes into account the user's emotional state. This system achieves its functionality through the collaboration of a server, a terminal, and an emotion engine that analyzes the user's emotions.

[0861] First, the server collects geographical information and historical accident data for mountainous areas from a database. In parallel, it obtains real-time data such as temperature, precipitation, and wind speed from weather information providers and integrates it with the geographical information. The server then analyzes this information using a generative AI model to perform a safety risk assessment. Based on the risk assessment, it proposes the most suitable climbing route to the user.

[0862] Next, the user uses the device to enter their destination and hiking date. The user's input information is sent to the server, and an appropriate hiking route is calculated. Once the hike begins, the device acquires the user's location information in real time and sends it to the server. Based on this data, navigation and route change notifications are provided during the hike.

[0863] Furthermore, the device acquires the user's biometric information from sensors and transmits it to the emotion engine. The emotion engine uses this data to infer the user's emotional state. For example, it can detect stress and fatigue from heart rate and skin electrical activity. Based on the results, the user receives notifications recommending breaks or equipment checks.

[0864] In the event of an emergency, the user operates their device to send an SOS signal. This signal is sent to the server as a report including the user's current location and emotional state. The server quickly transmits this information to rescue teams and proposes the optimal rescue route. Furthermore, the emotion engine can generate psychological support messages that take the user's emotional state into consideration, providing a sense of security.

[0865] After the climb is completed, the server analyzes all data obtained from the route to identify deteriorated sections and risk areas. This allows administrators to be notified of improvement measures to enhance safety for future climbs.

[0866] For example, if a user begins to feel stressed during a long climb, the emotion engine will detect this, and the device will suggest a break and provide support such as playing relaxing music. In this way, the system aims to improve quality in both safety and user experience.

[0867] The following describes the processing flow.

[0868] Step 1:

[0869] The server retrieves detailed geographical information and past accident data for mountainous areas from a database and inputs it into the analysis engine. This prepares the system for risk assessment of mountain climbing routes.

[0870] Step 2:

[0871] The server retrieves real-time weather data from weather information providers. This data includes temperature, precipitation, and wind speed, and when integrated with geographic information, it improves the accuracy of risk assessments.

[0872] Step 3:

[0873] The server analyzes geographic and weather data collected using a generative AI model to calculate a risk assessment score indicating the safety of each hiking route. This information is used for subsequent processing.

[0874] Step 4:

[0875] The user enters the desired mountainous region and climbing date into their device. This information is sent to the server, and the system begins suggesting appropriate climbing routes.

[0876] Step 5:

[0877] The server calculates the optimal climbing route based on a risk assessment score for the user's desired route. The route information is then sent to the user's device.

[0878] Step 6:

[0879] The device displays the received route information and prompts the user for confirmation. The user can review the suggested route and request changes if necessary.

[0880] Step 7:

[0881] Once the user begins climbing, the device continuously acquires the user's location information using GPS and transmits it to the server in real time.

[0882] Step 8:

[0883] The device uses built-in sensors to acquire the user's biometric information and transmits it to the emotion engine. The emotion engine then uses this information to analyze the user's emotional state.

[0884] Step 9:

[0885] The emotion engine detects stress and fatigue from the user's emotional data and generates appropriate responses based on that state. For example, it can generate suggestions to take a break or encouraging messages.

[0886] Step 10:

[0887] The server monitors weather information, which is updated in real time, and immediately sends a notification to the user's terminal if a risk occurs. The terminal then prompts the user to change their route or descend the mountain.

[0888] Step 11:

[0889] If a user encounters an emergency, they can use the device's SOS function to send a signal. This signal, which includes location information and emotional state, is sent to the server.

[0890] Step 12:

[0891] The server receives an SOS signal, quickly sends an emergency notification to rescue teams, and calculates the optimal rescue route. The emotion engine generates messages tailored to the user's emotions, providing reassurance and support.

[0892] Step 13:

[0893] After the climb is completed, the server analyzes route usage and emotional data to identify areas of physical deterioration and psychological risk factors. Administrators are then notified of any necessary improvements.

[0894] (Example 2)

[0895] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0896] Ensuring safety during mountain climbing in mountainous regions and improving user convenience are essential. Specifically, it is necessary to accurately assess risks using geographic and meteorological information, and to utilize climbers' location and biometric information in real time to provide early response to emergencies and appropriate support tailored to the user's emotional state. The challenge lies in providing highly accurate and rapid safety support in mountainous areas, which was difficult with conventional systems.

[0897] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0898] In this invention, the server includes means for evaluating the safety of a mountain climbing route based on geographical information and past accident information, means for acquiring weather information and proposing changes to the mountain climbing route based on that information, and means for acquiring biometric information, analyzing emotional state, and providing appropriate notifications to the user. This enables flexible and rapid safety assurance and comfortable mountain climbing support tailored to the user's current situation.

[0899] "Geographic information" refers to data that includes location-related information such as topography, roads, and landmarks, and is used to understand the conditions of a specific region.

[0900] "Past accident information" refers to records of accidents that have occurred in a specific area in the past. This information is used to analyze the causes and circumstances of these accidents in order to improve safety.

[0901] A "mountain climbing route" is a set path that climbers take to reach their destination, and it is optimized considering the terrain and safety.

[0902] "Means of assessing safety" refer to processes and techniques for analyzing risk factors and indicating the safety of specific activities or locations using numerical values ​​or standards.

[0903] "Weather information" refers to meteorological data such as temperature, precipitation, and wind speed, and is used to understand weather conditions at a specific time and place.

[0904] "Means of proposing change" refer to systems or methods for indicating safer or more effective ways or routes depending on the situation.

[0905] "Location information" refers to information used to indicate a specific point on Earth, and is usually represented by data such as latitude and longitude.

[0906] "Route guidance" refers to a system or method that provides instructions to assist travel by showing the route and direction to a destination.

[0907] "Biometric information" refers to data that indicates an individual's physiological state, such as heart rate and skin electrical activity, and is usually measured through sensors.

[0908] "Means of analyzing emotional states" refer to systems and technologies that estimate and understand a person's emotions based on their biometric information and other data.

[0909] A "rescue signal" is a signal sent to request help in an emergency, and may include location information.

[0910] "Means of supporting rescue operations" refer to systems and technologies that provide the necessary information and procedures in the event of an emergency, and support the efficient carrying out of rescue operations.

[0911] A "deteriorated area" refers to a place or part that has deteriorated physically or functionally and requires improvement.

[0912] "Means of notifying corrective measures" refers to systems or methods for communicating suggestions or procedures for correcting specific problems to relevant parties.

[0913] The system used to implement this invention provides safe mountaineering support in mountainous regions based on communication between a server, a terminal, and a user.

[0914] The server first retrieves geographical information and historical accident data from a database. A relational database management system (relational database management system) is often used for this operation, such as PostgreSQL. Weather information is also obtained in real time through an external weather data provider (e.g., the OpenWeatherMap API). To integrate this diverse information, the server processes the data using the Python pandas library.

[0915] Next, the integrated data is analyzed using a generative AI model. Specifically, generative AI such as OpenAI's GPT series is utilized. This allows for risk assessment of the hiking routes and suggests the most suitable hiking route for the user.

[0916] The user enters their destination and climbing date using their device, and this information is sent to the server. The device is assumed to be a typical smartphone, and its GPS function tracks the user's location in real time. This location information is sent to the server, which uses it to provide route guidance and, if necessary, suggest route changes.

[0917] In addition, the device acquires biometric information from sensors such as wearable devices. This includes monitoring heart rate and skin electrical activity, for example, through an Apple Watch. This data is sent to the emotion engine and used as input to analyze the user's emotional state. The emotion engine detects whether the user is stressed or fatigued and, based on that, suggests taking a break or plays relaxing music.

[0918] For example, if a user's heart rate increases by 20% during a long hike, the emotion engine will detect stress, and the device will suggest a break to the user. Furthermore, it can play relaxing music using streaming services such as Spotify.

[0919] An example of a prompt message would be: "Explain what action this system should recommend when the user's heart rate increases by 20% above normal."

[0920] In the event of an emergency, users can operate their devices to send an SOS signal. This signal includes their current location and emotional state, which the server quickly relays to rescue teams. Furthermore, a psychological support message generated by the emotion engine is sent to the user to provide reassurance.

[0921] The system configuration described above aims to improve safety during mountain climbing and enrich the user experience.

[0922] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0923] Step 1:

[0924] The server acquires geographical information and historical accident data. This allows it to collect foundational data for safety assessments. The input is queries from a database, and the output is a dataset containing that information. The server retrieves the information via a relational database management system and prepares it for use in subsequent analysis processes.

[0925] Step 2:

[0926] The server retrieves real-time weather data from weather information providers. This is done via an API, and the output includes data such as temperature, precipitation, and wind speed. The server integrates this data with the geographic information obtained in the previous step to generate a comprehensive dataset.

[0927] Step 3:

[0928] The server analyzes the integrated data using a generative AI model. This process incorporates geographical information, historical accident data, and weather data. As a result of the analysis, a safety score for the hiking route is generated. Specifically, AI models such as OpenAI's GPT series are used for this analysis, performing scoring based on the type and location of risk.

[0929] Step 4:

[0930] The user enters their hiking destination and date into the device. This information is sent to the server and used as input. Based on the analysis results, the server calculates the optimal hiking route and sends a suggestion to the user's device as output. The user can then view the route, whose safety has been assessed, on the screen.

[0931] Step 5:

[0932] The device uses GPS to acquire the user's location information in real time. The acquired location information is sent to the server and used as input information. Based on this data, the server tracks the user's route and generates navigation information and route change suggestions as output. Route change notifications are sent in real time as needed.

[0933] Step 6:

[0934] The device collects biometric information from wearable devices, including heart rate and skin electrical activity. The acquired data is sent to an emotion engine, which analyzes it to estimate the user's emotional state. For example, it might detect stress levels and suggest the user take a break.

[0935] Step 7:

[0936] If an emergency occurs while mountaineering, the user can send an SOS signal from their device. The input includes the user's current location and emotional state. This information is sent to a server and quickly relayed to rescue teams. The server calculates the optimal rescue route and provides it to the rescue team. Additionally, a psychological support message generated by an emotion engine is sent to the device.

[0937] Step 8:

[0938] The server analyzes all data collected after the climb is completed. Input includes all data related to the climbing route, and output identifies deteriorated sections and risk areas along the route. This allows for the development of improvement measures for future climbs, which are then communicated to administrators. Through this process, continuous safety improvements are ensured.

[0939] (Application Example 2)

[0940] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0941] Conventional mountain climbing support systems primarily rely on geographical and meteorological information for safety assessments, lacking individualized support that considers the user's emotional state. This can lead to users continuing their climbs while experiencing significant psychological distress. Furthermore, while there is a need to monitor the user's health in real time and suggest appropriate actions, such implementations have not been common.

[0942] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0943] In this invention, the server includes means for evaluating the safety of a climbing route based on geographical information and past accident information, means for acquiring weather information and suggesting changes to the climbing route based on that information, and means for detecting the user's biometric information and inferring their emotional state. This enables the provision of appropriate support according to the user's psychological state, making a safe and comfortable climbing experience possible.

[0944] "Geographic information" refers to data that includes information about the location and topography of a specific region or place.

[0945] "Past accident information" refers to detailed records and data about accidents that have occurred to date.

[0946] "Means for evaluating the safety of mountain climbing routes" refers to a function that uses geographical information and past accident data to analyze the risks of mountain climbing routes and determine their level of safety.

[0947] "Weather information" refers to data that shows the weather conditions of a region with geographical characteristics, such as temperature, precipitation, and wind speed.

[0948] "Means of providing route guidance" refers to functions that provide users with the optimal route and instructions in real time.

[0949] "Means for transmitting rescue signals and supporting rescue operations" refers to technical measures that enable users to transmit rescue requests in emergencies and provide rapid assistance.

[0950] "A means of identifying deteriorated sections of a route and notifying of improvement measures" refers to a mechanism that identifies dangerous sections along a route after a climb and recommends improvements to ensure safety for future climbs.

[0951] "Biometric information" refers to data such as heart rate and skin electrical activity that indicate an individual's health status.

[0952] "Means for inferring emotional state" refer to algorithms and devices that analyze and determine a user's psychological state based on their biometric information.

[0953] "Means of providing appropriate notifications and suggestions to users based on their emotional state" refers to functions that take into account the user's psychological state and provide appropriate advice and information.

[0954] This invention is a system that provides the necessary support for users to enjoy mountain climbing with peace of mind. Specifically, a server, terminals, and an emotion analysis engine work together.

[0955] The server collects and combines geographical information, past accident data, and weather information to suggest the optimal hiking route to the user. This uses a database as hardware and software such as Python and generative AI models. Data analysis is used to assess safety risks.

[0956] The device acquires the user's location information in real time. This allows it to understand how the user is moving and provide safe route guidance. The device also uses biosensors to collect data such as the user's heart rate and skin's electrical activity, and sends this information to an emotion analysis engine to analyze their emotional state. For example, if it detects stress caused by prolonged mountain climbing, the system will provide support such as playing relaxing music.

[0957] Receiving appropriate support based on users' emotional state can reduce psychological burden and lead to a better climbing experience. This can alleviate anxiety and pressure felt during climbing, thereby increasing safety.

[0958] For example, if a user feels fatigued, the emotion engine can detect this and the device can send advice such as, "Perhaps it's time for a break?" An example of an input prompt for the generative AI model is, "Analyze heart rate data and skin potential data in real time and estimate the stress level."

[0959] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0960] Step 1:

[0961] The server collects geographical information, past accident data, and weather information from a database. This information is used as foundational data for evaluating the safety of mountain climbing routes. The input is a set of information from the database, and the output is integrated geographical and weather data. This integrated data is used in the next risk assessment step.

[0962] Step 2:

[0963] The server analyzes geographical and meteorological data collected using a generative AI model to assess the risks of hiking routes. The AI ​​model's input is integrated geographical and meteorological data. As part of the data processing, an algorithm is applied to score the degree of risk, and a safety assessment is performed. The output is the assessed risk score and the recommended optimal hiking route.

[0964] Step 3:

[0965] The user uses a terminal to enter their destination and hiking date. This information is sent to the server, and the optimal hiking route is suggested. The input in this step is the destination and date / time data entered by the user from the terminal, and the output is the optimal route returned by the server.

[0966] Step 4:

[0967] The device acquires the user's location information in real time and sends it to the server. This allows the server to monitor the user's progress and provide safe route guidance. The input is location data from the GPS sensor, and the output is current location information sent to the server. This location information is used for route guidance in the next step.

[0968] Step 5:

[0969] The device acquires the user's biometric information from sensors and transmits it to the emotion analysis engine. At this stage, the necessary inputs are heart rate and skin electrical activity data from biosensors, and the output is the estimated emotional state. The emotion analysis engine processes this data, analyzes the psychological state, and outputs the result.

[0970] Step 6:

[0971] When a user experiences fatigue or stress, the emotion analysis engine detects their state, and the device provides the user with a notification prompting them to take a break or change their mood. The input for this step is emotional state data from the emotion analysis engine, and the output is a notification message to the user suggesting a break or a change of pace. Specific actions may include playing relaxing music.

[0972] Step 7:

[0973] In the event of an emergency, the user operates the device to send an SOS signal. The input for the SOS signal is a user-initiated button press or similar action, and the output is a rescue request signal that includes current location information and emotional state data. The server receives this signal and quickly transmits it to rescue teams.

[0974] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0975] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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."

[0983] 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.

[0984] 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.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] 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.

[0990] 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.

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] The following is further disclosed regarding the embodiments described above.

[0996] (Claim 1)

[0997] A method for evaluating the safety of mountain climbing routes based on geographical information and past accident data,

[0998] A method for obtaining weather information and proposing changes to the climbing route based on that information,

[0999] A means of obtaining the user's location information and providing real-time route guidance,

[1000] A means of transmitting a distress signal and supporting rescue operations in the event of an emergency,

[1001] A means of identifying deteriorated sections of the route after the climb and notifying the climbers of improvement measures,

[1002] A system that includes this.

[1003] (Claim 2)

[1004] The system according to claim 1, which analyzes geographic and meteorological information using a generated AI model and performs risk assessment.

[1005] (Claim 3)

[1006] The system according to claim 1, which detects deviations from a hiking route based on acquired user location information and issues a warning.

[1007] "Example 1"

[1008] (Claim 1)

[1009] A method for evaluating the safety of travel routes based on geographical information and past accident data,

[1010] A means of obtaining weather information and proposing changes to travel routes based on that information,

[1011] A means of obtaining the user's location information and providing real-time route guidance,

[1012] A means of transmitting a distress signal and supporting rescue operations in the event of an emergency,

[1013] A means of identifying deteriorated areas along the route after the move is completed and notifying the customer of corrective measures,

[1014] A means of analyzing geographic and meteorological information using a generated artificial intelligence model to perform a risk assessment,

[1015] A means to detect deviations from the travel route based on acquired user location information and issue a warning,

[1016] A system that includes this.

[1017] (Claim 2)

[1018] The system according to claim 1, which uses a generated artificial intelligence model to analyze geographic and meteorological information and perform a risk assessment.

[1019] (Claim 3)

[1020] The system according to claim 1, which detects deviations from a travel path based on acquired user location information and issues a warning.

[1021] "Application Example 1"

[1022] (Claim 1)

[1023] A method for evaluating the safety of mountain climbing routes based on geographical information and past accident data,

[1024] A method for obtaining weather information and proposing changes to the climbing route based on that information,

[1025] A means of obtaining the user's location information and providing real-time route guidance,

[1026] A means of transmitting a distress signal and supporting rescue operations in the event of an emergency,

[1027] A means of integrating disaster prevention information for mountaineering and urban areas, conducting risk assessments, and providing information to ensure safety.

[1028] A means of providing guidelines for emergency response in tourist areas or urban areas for the safety of users,

[1029] A system that includes this.

[1030] (Claim 2)

[1031] The system according to claim 1, which analyzes geographic and meteorological information using a generated AI model and performs risk assessment.

[1032] (Claim 3)

[1033] The system according to claim 1, which detects deviations from a hiking route based on acquired user location information and issues a warning.

[1034] "Example 2 of combining an emotion engine"

[1035] (Claim 1)

[1036] A method for evaluating the safety of mountain climbing routes based on geographical information and past accident data,

[1037] A means of obtaining weather information and proposing changes to the climbing route based on that information,

[1038] A means of obtaining the user's location information and providing real-time route guidance,

[1039] A means of acquiring biometric information, analyzing emotional state, and providing appropriate notifications to the user,

[1040] A means of transmitting a distress signal and supporting rescue operations in the event of an emergency,

[1041] A means of identifying deteriorated sections of the trail after the climb and notifying the climbers of improvement measures,

[1042] A system that includes this.

[1043] (Claim 2)

[1044] The system according to claim 1, which analyzes geographic and meteorological information using a generated artificial intelligence model and performs risk assessment.

[1045] (Claim 3)

[1046] The system according to claim 1, which detects deviations from a mountain climbing route based on acquired user location information and issues a warning.

[1047] "Application example 2 when combining with an emotional engine"

[1048] (Claim 1)

[1049] A method for evaluating the safety of mountain climbing routes based on geographical information and past accident data,

[1050] A method for obtaining weather information and proposing changes to the climbing route based on that information,

[1051] A means of obtaining the user's location information and providing real-time route guidance,

[1052] A means of transmitting a distress signal and supporting rescue operations in the event of an emergency,

[1053] A means of identifying deteriorated sections of the route after the climb and notifying the climbers of improvement measures,

[1054] A means of detecting the user's biometric information and inferring their emotional state,

[1055] A means of providing users with appropriate notifications and suggestions based on their emotional state,

[1056] A system that includes this.

[1057] (Claim 2)

[1058] The system according to claim 1, which analyzes geographic and meteorological information using a generated AI model and performs risk assessment.

[1059] (Claim 3)

[1060] The system according to claim 1, which detects deviations from a hiking route based on acquired user location information and issues a warning. [Explanation of symbols]

[1061] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for evaluating the safety of mountain climbing routes based on geographical information and past accident data, A method for obtaining weather information and proposing changes to the climbing route based on that information, A means of obtaining the user's location information and providing real-time route guidance, A means of transmitting a distress signal and supporting rescue operations in the event of an emergency, A means of identifying deteriorated sections of the route after the climb and notifying the climbers of improvement measures, A system that includes this.

2. The system according to claim 1, which uses a generated AI model to analyze geographic and meteorological information and perform risk assessment.

3. The system according to claim 1, which detects deviations from a hiking route based on acquired user location information and issues a warning.

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