system

Abstract

Provide a system. [Solution] A means for inputting mountain climbing plan information; A means for collecting weather data, topographical data, and past accident data based on inputted mountain climbing plan information; a means of analyzing the collected data and conducting a risk assessment; A means for generating an optimal climbing route based on the risk assessment results; A means for providing the generated climbing route and risk warning points to the user; a means for real-time monitoring and notification during the climb; A means to organize the climbing record and generate a report after the climbing is completed, A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In traditional mountain climbing, climbers must assess risks and take safety measures based on their own personal experience and limited information. However, this approach makes it difficult to fully predict weather changes and terrain risks, leading to accidents and troubles during the climb. Furthermore, there are few ways to grasp sudden weather changes and other risk information in real time while climbing, making it difficult to respond quickly. A new system is needed to solve these issues and ensure safe and efficient mountain climbing. [Means for solving the problem]

[0005] The present invention solves these problems by providing a system that includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information, a means for analyzing the collected data and conducting a risk assessment, a means for generating an optimal mountain climbing route based on the risk assessment results, a means for providing the generated mountain climbing route and risk warning points to the user, a means for real-time monitoring and notification during the mountain climbing, and a means for organizing the mountain climbing record and generating a report after the mountain climbing is completed.

[0006] Furthermore, by including a means to regularly update weather data and a means to monitor the user's location in real time and notify them of unexpected weather changes or risks, safety during mountain climbing can be further improved.

[0007] "Mountain climbing plan information" refers to detailed information about the mountain climber's planned climb (starting point, destination, planned date and time, climbing experience, etc.).

[0008] "Means" refers to an element such as a device, method, or software for achieving a specific function or purpose.

[0009] "Weather Data" refers to information regarding weather conditions such as temperature, wind speed, precipitation, and weather.

[0010] "Topographical data" refers to information about the terrain around a climbing route (elevation, slope, surface conditions, etc.).

[0011] "Past accident data" refers to information on past mountain climbing-related accidents and troubles (such as the location, cause, date and time of the accident).

[0012] "Risk assessment" refers to the process of evaluating the risk that may occur under specific conditions based on collected data and calculating the level of that risk.

[0013] A "mountain climbing route" refers to the specific path from the starting point to the destination.

[0014] "Risk warning points" refer to points on a particular climbing route that are assessed as being high risk.

[0015] "User" refers to a person who plans a mountain climb and uses this system.

[0016] "Real-time monitoring" refers to collecting and analyzing climber location information and surrounding environmental information in real time, and responding to changes in the situation.

[0017] "Notification" refers to the sending of messages from the system to users to inform them of the occurrence of risks or precautions.

[0018] "Mountain climbing records" refers to information that records the user's actions and situations during the mountain climbing process.

[0019] A "report" refers to a document that is generated after a climb is completed and includes a record of the climb and points for reflection. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a 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.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] System Overview

[0042] This invention is a system that uses AI to support safe mountain climbing planning. The system includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and conducting risk assessments, a means for generating optimal mountain climbing routes, a means for real-time monitoring and notification, and a means for organizing mountain climbing records and generating reports. Each of these means and specific examples are described below.

[0043] Program processing

[0044] 1. Enter your climbing plan information

[0045] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.). The user inputs the necessary information and presses the send button.

[0046] User: Enters a mountain climbing plan and sends it from the device to the server.

[0047] 2. Data collection

[0048] Server: Receives climbing plan information sent by users. Based on this information, it collects weather data, topography data, and past accident data for the target area. Weather data is obtained from weather services, topography data from a geographic information system (GIS), and past accident data from a climbing accident database.

[0049] 3. Risk assessment and route optimization

[0050] Server: Analyzes collected data and performs risk assessment using AI. Risk assessment includes factors such as weather, terrain, and past accident information. Based on the assessment results, multiple routes are generated and the risk points and risk levels for each are calculated. The route with the least risk is selected and detailed route information is generated.

[0051] 4. Providing optimal routes and important points to note

[0052] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0053] Terminal: Displays the optimal route and risk warning points on a map to provide users with information.

[0054] User: Create a safe mountain climbing plan based on the displayed information.

[0055] 5. Real-time monitoring and notifications

[0056] Server: Monitors the user's location in real time while they are climbing. If there are any changes in weather conditions or terrain information, it analyzes them promptly and sends a notification to the device.

[0057] Device: Display emergency alerts and warning notifications to the user.

[0058] 6. Organizing climbing records and generating reports

[0059] Server: After the climb is completed, the server receives feedback from users, organizes the climb record, and generates a detailed report including reflections and areas for improvement.

[0060] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[0061] Specific examples

[0062] Assume that the user is planning to climb Mountain A next Saturday.

[0063] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0064] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[0065] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[0066] 4. The server sends the optimal route information and risk warning information to the terminal.

[0067] 5. The device displays the optimal route and risk-related areas on a map for the user.

[0068] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[0069] 7. The device displays emergency alerts and warnings to the user.

[0070] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[0071] 9. The device can display this report to the user, helping them plan their next climb.

[0072] This will enable safe and secure mountain climbing using a system that makes full use of AI.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user inputs mountain climbing plan information into the input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[0076] Step 2:

[0077] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data for the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[0078] Step 3:

[0079] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[0080] Step 4:

[0081] The server then inputs the analysis results into an AI algorithm to perform a risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, the risk points and risk level for each route are calculated.

[0082] Step 5:

[0083] The server generates multiple climbing routes based on the risk assessment results. From the generated routes, it selects the route that is assessed as having the least risk and generates detailed route information. The detailed route information includes distance, elevation change, estimated time, etc.

[0084] Step 6:

[0085] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[0086] Step 7:

[0087] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[0088] Step 8:

[0089] During the climb, the user's location information is periodically updated by the device and sent to the server.

[0090] Step 9:

[0091] The server monitors the received location information in real time, and if there is a sudden change in weather conditions or other risk information, it analyzes it promptly and notifies the user of the risk information as necessary.

[0092] Step 10:

[0093] The device displays emergency alerts and warning notifications to the user in real time.

[0094] Step 11:

[0095] After the climb is completed, the server receives feedback from the user, organizes the climb record, analyzes the user's behavior and situation, and generates a detailed report that includes reflections and areas for improvement.

[0096] Step 12:

[0097] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] Mountaineering is an activity in nature, and it involves many risks, such as changes in weather and terrain, and unexpected events. It is particularly difficult for inexperienced climbers to plan a safe climb and manage the risks. There is also a lack of real-time risk notifications during the climb and feedback after the climb is complete. Furthermore, there is a need for systems that provide optimal routes based on collected data and support for enjoying mountaineering while ensuring safety.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for performing risk assessment using a generative AI model, a means for generating an optimal mountain climbing route based on the risk assessment results, a means for real-time monitoring and notification during mountain climbing, and a means for organizing mountain climbing records and generating a report after the mountain climbing is completed, thereby enabling support for users to climb mountains safely.

[0103] "Mountain climbing plan information" is information such as the starting point, destination, planned date and time, and mountain climbing experience that the user inputs in order to carry out mountain climbing activities.

[0104] "Weather data" is data that indicates the weather conditions in a specific area, and includes information on weather conditions such as temperature, precipitation, and wind speed.

[0105] "Topography data" refers to data that indicates the topography and geographical features of the target area, including information such as elevation, slope, and topography.

[0106] "Past accident data" is information about accidents and incidents that have occurred during mountain climbing in the past, including the location, cause, and detailed circumstances of the accident.

[0107] A "generative AI model" is a model that uses artificial intelligence technology to analyze large amounts of data, learn patterns, and make predictions and classifications.

[0108] "Risk assessment" is the process of assessing the risks during mountain climbing by analyzing the weather, terrain, past accident information, etc. based on the data obtained.

[0109] The "optimal climbing route" is the safest and most efficient climbing route selected based on risk assessment.

[0110] "Real-time monitoring" is the process of tracking the user's location and environmental information in real time while climbing, and constantly monitoring the latest situation.

[0111] A "notification" is a warning or caution message sent from the server to the user terminal, and is information necessary for the user to take appropriate action.

[0112] A "mountain climbing record" is a detailed record of mountain climbing activities, and includes information such as start time, finish time, walking distance, and elevation difference.

[0113] A "report" is a detailed report generated based on climbing records and feedback, and includes reflections and suggestions for improvement that will be useful in planning your next climb.

[0114] This invention relates to a system for safely supporting mountain climbing plans, and uses AI technology to perform risk assessment, propose optimal routes, perform real-time monitoring, and generate reports.

[0115] System Overview

[0116] The system includes a means for inputting climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and performing risk assessment using a generative AI model, a means for generating optimal climbing routes, a means for real-time monitoring and notification, and a means for organizing climbing records and generating reports.

[0117] Entering mountain climbing plan information

[0118] The terminal provides an interface for the user to input mountain climbing plan information. This interface includes items such as the starting point, destination, planned date and time, and climbing experience. As a specific example, if a user plans to depart from trailhead A to summit B at 6:00 a.m. on September 15th, the user can enter this information into the terminal and press the "send" button to send the information to the server.

[0119] Data collection

[0120] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data. Weather data is obtained through the weather service API (e.g., OpenWeatherMap), terrain data is obtained from a geographic information system (GIS), and past accident data is obtained from a mountain climbing accident database.

[0121] Risk assessment and route optimization

[0122] The server integrates the collected data and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated and the risk points and risk level of each route are calculated. The route with the least risk is selected and detailed route information is generated, including a route map, distance, elevation change, and risk warning points.

[0123] Providing optimal routes and points to note

[0124] The server organizes information on optimal routes and risk-related points, and sends it to the terminal. The terminal displays this information on a map for the user. The user can then create a safe mountain climbing plan based on the displayed information.

[0125] Real-time monitoring and notifications

[0126] The server periodically collects GPS data to monitor the user's location in real time while climbing. The server tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, the server promptly sends a notification to the device and displays an emergency alert or warning to the user.

[0127] Organize your climbing records and generate reports

[0128] After the climb is completed, the server receives feedback from the user and organizes the climb record. For example, a detailed report is generated by collecting data such as the start time, finish time, walking distance, and elevation gain. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via their device. The user can use this report to plan their next climb.

[0129] Specific examples of prompt sentences are as follows:

[0130] "Plan your next climb by analyzing past climb records and weather data to suggest the best route."

[0131] "I'd like to plan a climbing route to Summit A. The starting point is Trailhead B, and the planned time and date is 6:00 a.m. on September 15th. What is the best route and what are the risks and precautions I should take?"

[0132] This allows users to climb mountains safely and efficiently, and enjoy nature with peace of mind.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] The terminal displays a dedicated interface for the user to input mountain climbing plan information. The user inputs information such as the starting point, destination, planned date and time, and mountain climbing experience into this interface. For example, the starting point may be "Trailhead A," the destination may be "Summit B," the planned date and time may be "September 15th, 6:00 AM," and the climbing experience may be "Intermediate." Once this information is input, the user presses the "Send" button to send the information to the server. The input is the mountain climbing plan information input by the user, and the output is the mountain climbing plan information sent to the server.

[0136] Step 2:

[0137] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects the necessary data. Specifically, it obtains weather data from a weather service API (e.g., OpenWeatherMap), terrain data from a geographic information system (GIS), and past accident data from a mountain climbing accident database. The input is the received mountain climbing plan information, and the output is the collected weather data, terrain data, and past accident data.

[0138] Step 3:

[0139] The server integrates collected weather data, terrain data, and past accident data, and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated, and the risk points and risk levels for each route are calculated. The route with the least risk is selected, and detailed route information is generated. The input is the collected data, and the output is the risk assessment results and optimal route information.

[0140] Step 4:

[0141] The server organizes the selected optimal route information and information on risk-warning areas and sends it to the terminal. The terminal displays the received information on a map. This allows the user to visually confirm detailed information for creating a safe mountain climbing plan. The input is the optimal route information and information on risk-warning areas, and the output is the information sent to the terminal.

[0142] Step 5:

[0143] The server periodically collects GPS data to monitor the user's location in real time while climbing. It tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, it promptly sends a notification to the user's device, which then displays it to the user. The input is the GPS data collected in real time, and the output is notification information.

[0144] Step 6:

[0145] After the climb is completed, the server receives feedback from the user, organizes the climb record, and generates a detailed report. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via a terminal. The input is the user's feedback and the climb record, and the output is the generated detailed report.

[0146] This makes it possible to support the user in climbing safely and efficiently through each step.

[0147] (Application example 1)

[0148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0149] Conventional navigation systems and mountain climbing planning support systems lack the functionality to comprehensively consider real-time changing weather data, road conditions, past accident data, etc. to perform risk assessments and present optimal routes. As a result, users could face unexpected risks. In addition, they lacked the functionality to generate detailed reports after a hike or drive to evaluate safety and efficiency and incorporate them into future plans. This could compromise user safety and the accuracy of their plans.

[0150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0151] In this invention, the server includes a means for inputting mountain climbing plan information or driving plan information, a means for collecting weather data, topographical data, road data, and past accident data based on the input mountain climbing plan information or driving plan information, and a means for analyzing the collected data and performing risk assessment. This makes it possible to provide optimal routes that reflect a variety of information, such as weather, road conditions, and past accident data, in real time. It also provides real-time monitoring and emergency notifications during mountain climbing or driving, improving user safety. Furthermore, a detailed report can be generated after the end of the climb or drive, which can be used to plan the next trip.

[0152] "Mountain climbing plan information" is information entered by the mountain climber, such as the starting point, destination, planned date and time, and mountain climbing experience.

[0153] "Driving plan information" is information such as the departure point, destination, scheduled date and time entered by the driver.

[0154] "Weather data" refers to information about the weather, such as the current weather, temperature, precipitation, and wind speed.

[0155] "Topographic data" refers to geographical information such as mountain and terrain features, elevation, and slope.

[0156] "Road data" refers to geographical information about roads, such as road elevation, slope, and road width.

[0157] "Past accident data" refers to historical information on traffic accidents and mountain climbing accidents that have occurred in specific areas or on specific roads.

[0158] A "means" is a tool, method, or device used to achieve a particular end.

[0159] "Risk assessment" is the process of determining the existence and extent of potential risks based on weather conditions, road conditions, past accident data, etc.

[0160] The "optimal climbing route" is the climbing route that is analyzed to have the least risk, taking into account safety and efficiency.

[0161] An "optimal driving route" is a driving route that has been analyzed to have the least risk, taking into account safety and efficiency.

[0162] "Real-time monitoring" is the process of monitoring ongoing situations in real time and collecting and analyzing data instantly.

[0163] "Notification" is the act of conveying important information or warnings to a user.

[0164] A "report" is a document that organizes records after a hike or drive and provides information in a detailed report format.

[0165] "Location information" is information that indicates a specific location, such as the coordinates of the user's current location.

[0166] A "server" is a computer system that collects, analyzes, and provides data.

[0167] This invention is a system for supporting safe mountain climbing and driving plans. Based on the planning information entered by the user, the system collects and analyzes weather data, topographical data, road data, and past accident data, performs risk assessments, and provides optimal routes in real time. After the mountain climbing or driving is completed, a detailed report is generated and the safety and efficiency are evaluated.

[0168] The program proceeds as follows:

[0169] 1. Enter planning information

[0170] Users use their smartphones or in-car infotainment systems to input planning information such as starting point, destination, planned date and time, etc. This information is then sent from the device to a server.

[0171] 2. Data collection

[0172] Based on the planning information, the server collects weather data, topographical data, road data, and past accident data. These data are obtained from weather data APIs (e.g., OpenWeather), geographic information systems (GIS), and traffic information APIs.

[0173] 3. Risk assessment and route optimization

[0174] The server analyzes the collected data using AI analysis tools such as TENSORFLOW (registered trademark) and PyTorch to perform risk assessment. Evaluation items include weather, road conditions, topography, and past accident information. Based on the results of this analysis, the optimal route is generated.

[0175] 4. Providing optimal routes and risk warnings

[0176] The generated optimal route and risk information are sent from the server to the terminal, which then displays this information on a map and provides it to the user.

[0177] 5. Real-time monitoring and notifications

[0178] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, it immediately reanalyzes the data and sends warnings and alerts to the user's device, ensuring the user's safety.

[0179] 6. Record keeping and report generation

[0180] After the hike or drive is complete, the server compiles the records and generates a detailed report that provides useful information for planning your next trip.

[0181] Examples:

[0182] If the user enters "Tokyo" as the departure point, "Shizuoka" as the destination, and "October 20th 13:00" as the scheduled time and date, the following can be used as the prompt text:

[0183] "Use AI to evaluate the optimal route from Tokyo to Shizuoka and create a safe driving route."

[0184] The server collects weather data from OpenWeather, terrain data from GIS, and road and real-time traffic information from a traffic information API. Using TensorFlow, it performs risk assessments based on this data and generates optimal routes. The generated information is sent to the in-car navigation system and smartphone and displayed in real time. If there are sudden changes in weather or road conditions during hiking or driving, the server immediately reanalyzes the data and notifies the user. After completion, it organizes detailed route history and generates a report that can be used to plan the next trip.

[0185] In this way, by implementing the present invention, the user can make safe and efficient mountain climbing and driving plans.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] A user inputs planning information, such as a starting point, destination, and planned date and time, using a smartphone or in-car infotainment system. This information is sent from the device to a server. Inputs include the starting point, destination, date and time, and user-specific information. Outputs provide this planning information to the server.

[0189] Step 2:

[0190] The server collects weather data, topographical data (or road data), and past accident data based on the received plan information. Here, data is acquired using weather data APIs (such as OpenWeather), geographic information systems (GIS), and traffic information APIs. Plan information is used as input, and weather data, topographical data, road data, and past accident data are compiled as output.

[0191] Step 3:

[0192] The server analyzes the collected data and performs risk assessment using AI analysis tools (TensorFlow and PyTorch). This process evaluates the risk level based on weather conditions, road conditions, terrain, and past accident history. The input includes various collected data, and the output generates a risk assessment result.

[0193] Step 4:

[0194] The server generates the optimal route based on the risk assessment results. In this process, an algorithm for calculating the optimal route is applied to select a safe and efficient route for the user. The input is the risk assessment result, and the output is the optimal route information.

[0195] Step 5:

[0196] The server sends the generated information about the optimal route and risk warning points to the terminal. The terminal displays this information on a map and provides it to the user. The optimal route information is the input, and the route and risk warning points displayed on the map are provided to the user as the output.

[0197] Step 6:

[0198] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, the data is reanalyzed and alerts or warnings are sent to the user. In this step, data is processed and reevaluated in real time. The input includes real-time location information and new weather and road data, and the output is emergency alerts and notifications sent to the device.

[0199] Step 7:

[0200] After the climb or drive is completed, the server organizes the records and generates a detailed report, which provides useful information for planning the next climb or drive. The input includes the user's activity history and planning information, and the output is a detailed report, which is sent to the user's device and can be viewed.

[0201] The above processing flow enables safe and efficient mountain climbing and driving plans to be realized.

[0202] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0203] System Overview

[0204] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system includes functions for inputting mountain climbing plan information, collecting and analyzing weather data, topographical data, and past accident data, risk assessment, generating optimal mountain climbing routes, real-time monitoring and notifications, organizing mountain climbing records and generating reports, and an emotion engine that recognizes the user's emotions. It also includes a function that uses the emotion engine to provide appropriate advice and warnings based on the user's emotional state. Each method and specific examples are described in detail below.

[0205] Program processing

[0206] 1. Enter your climbing plan information

[0207] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.) When the user inputs the necessary information and presses the send button, the input information is sent to the server.

[0208] User: Enters a mountain climbing plan and sends it from the device to the server.

[0209] 2. Data collection

[0210] Server: Receives mountain climbing plan information sent by the user. Based on this information, it collects weather data, topographical data, and past accident data for the target area from the necessary data sources. Weather data is obtained from the weather service API, topographical data is collected from a geographic information system (GIS), and accident data is obtained from a mountain climbing accident database.

[0211] 3. Risk assessment and route optimization

[0212] Server: Analyzes collected weather data, topographical data, and past accident data. Weather data is used to extract and analyze weather patterns and forecasts, topographical data is used to extract elevation changes and ground surface conditions along the route, and accident data is used to extract and analyze risk factors.

[0213] Server: The analysis results are fed into an AI algorithm to perform risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, risk points and risk levels for each route are calculated.

[0214] Server: Generates multiple climbing routes based on the risk assessment results. From the generated routes, selects the route assessed as having the least risk and generates detailed route information, including distance, elevation difference, estimated time, etc.

[0215] 4. Providing optimal routes and important points to note

[0216] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0217] Device: The optimal route and risk warning points are displayed on a map to provide users with information so they can plan their safe and optimal mountain climbing trip.

[0218] User: Create a safe mountain climbing plan based on the displayed information.

[0219] 5. Real-time monitoring and notifications

[0220] Server: Monitors the user's location in real time while they are climbing. If there is a sudden change in weather conditions or other risk information, it analyzes the situation promptly and notifies the user of the risk information as necessary.

[0221] Device: Display emergency alerts and warning notifications to users in real time.

[0222] 6. Organizing climbing records and generating reports

[0223] Server: After the climb is completed, the server receives feedback from the user and organizes the climb record. It analyzes the user's behavior and situation and generates a detailed report, which also includes reflections and suggestions for improvement.

[0224] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[0225] Introducing the Emotion Engine

[0226] 7. Emotion Engine Settings

[0227] Terminal: Provides an interface for the emotion engine to collect data such as the user's facial expressions, voice, and heart rate to measure the user's emotions.

[0228] User: Emotional data is provided to the device in real time via climbing equipment or wearable devices.

[0229] 8. Collecting and analyzing emotional data

[0230] Server: Receives collected emotion data and analyzes it in real time. The emotion engine evaluates the user's mental state and reflects it in risk assessment and route optimization as necessary.

[0231] 9. Emotion-based advice and notifications

[0232] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings if the user is under a high level of mental stress or feels stressed.

[0233] Device: Providing advice and reminders to the user, such as suggestions for resting places to calm down or simple breathing exercises to help them relax.

[0234] Specific examples

[0235] Assume that the user is planning to climb Mountain A next Saturday.

[0236] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0237] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[0238] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[0239] 4. The server sends the optimal route information and risk warning information to the terminal.

[0240] 5. The device displays the optimal route and risk-related areas on a map for the user.

[0241] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[0242] 7. The device displays emergency alerts and warnings to the user.

[0243] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[0244] 9. The device can display this report to the user, helping them plan their next climb.

[0245] The introduction of an emotion engine allows the system to monitor the user's mental state in real time and provide not only risk assessment but also psychological support. For example, if the user is feeling stressed, the system can suggest appropriate rest areas and notify them of ways to relax, providing a safe and supported climbing experience.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] The user inputs mountain climbing plan information into an input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[0249] Step 2:

[0250] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data related to the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[0251] Step 3:

[0252] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[0253] Step 4:

[0254] The server then uses an AI algorithm to perform a risk assessment based on the analysis results. This assessment process takes into consideration weather, terrain, and past accident information to calculate the risk level for each climbing route. The assessment results then confirm the risk points and their levels.

[0255] Step 5:

[0256] The server generates multiple climbing routes based on the risk assessment results, selects the route with the lowest risk from the generated routes, and generates detailed route information, including distance, elevation change, estimated time required, and other information.

[0257] Step 6:

[0258] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[0259] Step 7:

[0260] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[0261] Step 8:

[0262] Users use wearable devices or terminals to provide their own emotional data (heart rate, facial expressions, voice, etc.) to the terminals in real time.

[0263] Step 9:

[0264] The terminal collects emotion data and transmits it to the server.

[0265] Step 10:

[0266] The server analyzes the received emotional data and assesses the user's emotional state, adjusting the risk assessment accordingly if the user is experiencing high mental load or stress.

[0267] Step 11:

[0268] During the climb, the user's location information is also periodically sent from the device to the server.

[0269] Step 12:

[0270] The server monitors the user's situation based on real-time location and emotion data received, and immediately analyzes any sudden changes in weather or other risk factors and generates warnings as needed.

[0271] Step 13:

[0272] The device displays emergency alerts and reminder messages to users in real time, including advice based on their emotional state, such as "take a break to relax."

[0273] Step 14:

[0274] After the climb is complete, the server receives feedback from the user, organizes the climb record, and analyzes the user's behavior, situation, and emotional data to generate a detailed report.

[0275] Step 15:

[0276] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[0277] The above processing flow allows the user to climb mountains safely and efficiently, and by using the emotion engine, mental support is also provided at the same time.

[0278] Example 2

[0279] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0280] Mountaineering is a challenge to nature, but it also involves various risks, making it important to plan ahead and take into account weather changes, terrain conditions, and past accident data. However, comprehensively collecting and analyzing this information requires specialized knowledge and large amounts of data processing, making it difficult for average climbers. Furthermore, monitoring risks in real time during a climb and organizing records afterward are both cumbersome, and a system to address these issues is needed. Furthermore, supporting safe climbing by taking into account the climber's emotional state is also an important issue.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0282] In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and conducting risk assessments; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during the mountain climbing; means for compiling a mountain climbing record and generating a report after the mountain climbing is completed; means for collecting and analyzing the user's emotional data; and means for providing appropriate advice and warnings based on the analyzed emotional data. This increases the safety of mountain climbing plans. It also provides real-time risk monitoring during the mountain climbing and appropriate support based on the user's emotional state, allowing for efficient record organization after the mountain climbing is completed.

[0283] "Mountain climbing plan information" refers to information necessary when climbing a mountain, and specifically includes the starting point, destination, planned date and time, mountain climbing experience, and the like.

[0284] "Weather data" refers to data that indicates weather conditions related to the climbing route or climbing journey, and specifically includes temperature, humidity, wind speed, precipitation, weather forecast, and the like.

[0285] "Topographical data" refers to data that indicates the physical characteristics of a climbing route, and specifically includes the type of ground surface, elevation difference, gradient, geographical features, and the like.

[0286] "Past accident data" refers to data related to past accidents and troubles that have occurred while climbing mountains, and specifically includes the location of the accident, the cause, the date and time of the accident, and the extent of the damage.

[0287] "Risk assessment" refers to the process of analyzing collected data and evaluating the risks and dangers associated with a climbing route, taking into account factors such as weather fluctuations, the difficulty of the terrain, and past accident data.

[0288] A "climbing route" is a path from a starting point to a destination, usually depicted on a map. The optimal climbing route is selected based on a risk assessment.

[0289] "Real-time monitoring" refers to the process of monitoring a user's location and environmental conditions in real time while climbing, including the continuous collection and analysis of GPS and weather data.

[0290] "Notification" refers to the process of providing information to users during the climbing or planning stage, specifically including emergency alerts, risk warnings, advice, etc.

[0291] A "mountain climbing record" is a record of the progress and results of mountain climbing activities, and specifically includes the mountain climbing route, activity history, environmental conditions, user feedback, and the like.

[0292] A "report" is a document that compiles collected and organized climbing records, and specifically includes a review of the climb and areas for improvement for the next time.

[0293] "Emotion data" is data that indicates the user's psychological state and emotions, and specifically includes facial expressions, voice, heart rate, and the like.

[0294] "Advice" refers to advice and guidance provided to users during the climbing or planning stages, and specifically includes suggestions for safe climbing and ways to reduce stress.

[0295] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system inputs mountain climbing plan information, collects and analyzes weather data, terrain data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions.

[0296] Hardware and Software Used

[0297] The system uses the following hardware and software:

[0298] Terminal: A device that acts as a user interface, such as a smartphone or tablet.

[0299] Server: Data collection, analysis, route generation, monitoring, and report generation are performed using a high-performance cloud server.

[0300] Weather Service API: Used to retrieve weather data.

[0301] Geographic Information Systems (GIS): Used to collect topographical data.

[0302] Mountaineering accident database: Used to obtain past accident data.

[0303] Emotion engine: Analyzes the user's emotional data and provides appropriate advice and warnings.

[0304] Generative AI models: Used for risk assessment and optimal route generation.

[0305] Explanation of program processing

[0306] 1. Enter your climbing plan information

[0307] Terminal: Provides an interface for users to input their starting point, destination, planned date and time, climbing experience, etc. When the user inputs the information and presses the send button, the information is sent to the server.

[0308] User: Enters the climbing plan into the device interface and presses the send button.

[0309] 2. Data collection

[0310] Server: Based on the received mountain climbing plan information, it collects weather data, terrain data, and past accident data. Weather data is collected from the weather service API, terrain data from GIS, and accident data from the mountain climbing accident database.

[0311] 3. Risk assessment and route optimization

[0312] Server: Analyzes the collected data and performs risk assessments using AI algorithms based on weather data, topographical data, and past accident data.

[0313] Server: Generates optimal climbing routes based on risk assessment. Route information includes distance, elevation gain, estimated time, etc.

[0314] 4. Providing optimal routes and important points to note

[0315] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0316] Terminal: The received information is displayed on a map, allowing users to create a safe and optimal mountain climbing plan.

[0317] 5. Real-time monitoring and notifications

[0318] Server: Monitors the user's location in real time while climbing, and notifies them of sudden changes in weather and risks.

[0319] Terminal: Displays emergency alerts and warnings in real time.

[0320] 6. Organizing climbing records and generating reports

[0321] Server: Receives user feedback after the climb, organizes the climbing record, generates detailed reports, and provides them to users.

[0322] Terminal: The generated report can be displayed to the user to help them plan their next mountain climbing trip.

[0323] 7. Emotion Engine Configuration and Analysis

[0324] Terminal: Provides a data collection interface for the emotion engine, including a camera for recognizing the user's facial expressions, a microphone for analyzing voice, and a heart rate sensor.

[0325] User: Provides real-time emotional data through climbing equipment and wearable devices.

[0326] Server: Analyzes the collected emotional data, evaluates the user's mental state, and reflects this in risk assessment and route optimization as necessary.

[0327] 8. Emotion-based advice and notifications

[0328] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings when the user is under high mental stress or feels stressed.

[0329] Device: Displays specific advice and reminders to the user, such as "Take a break" or "Breathing exercises to relax."

[0330] Specific examples

[0331] Assume that the user is planning to climb Mountain A next Saturday.

[0332] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0333] 2. The server collects surrounding weather data, topographical data, and past accident data, and organizes it specifically for Mountain A.

[0334] 3. The server performs a risk assessment, selects the safest route, and generates detailed information about it.

[0335] 4. The server sends the optimal route information and risk warning information to the terminal.

[0336] 5. The device displays the optimal route and risk warning points on a map for the user.

[0337] 6. During the climb, the server monitors the user's location in real time and notifies them of any sudden changes in the weather.

[0338] 7. The device displays emergency alerts and warnings.

[0339] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report that users can use to plan their next climb.

[0340] 9. The device collects emotional data using a facial recognition camera and heart rate sensor. The server analyzes the emotional data in real time and generates appropriate advice if stress is detected. The device then displays suggestions for reducing stress.

[0341] Prompt Sentence Examples

[0342] "Please tell me what climbing routes are safe."

[0343] "I'm an intermediate climber and I'm planning to climb Mountain A on September 15th. What's the best route?"

[0344] "What should I do if I feel stressed while climbing?"

[0345] This invention makes it possible to improve the safety of mountain climbing plans, monitor risks in real time during climbing, and provide appropriate support based on emotional states. In addition, by efficiently organizing records after climbing, it is possible to use the information to plan the next climbing trip.

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1:

[0348] Terminal: Provides an input interface for mountain climbing plan information. The user inputs the starting point, destination, planned date and time, and mountain climbing experience into the interface.

[0349] Input: User inputs starting point, destination, planned date and time, and climbing experience.

[0350] Specific behavior: The user enters information into the interface and presses the submit button.

[0351] Output: The entered mountain climbing plan information is sent to the server.

[0352] Step 2:

[0353] Server: Receives mountain climbing plan information and collects necessary data (weather data, terrain data, past accident data).

[0354] Input: Received climbing plan information.

[0355] Specific operation: The server sends a request to the weather service API to obtain weather data, collects topographical data from the GIS, and obtains past accident data from the mountaineering accident database.

[0356] Output: Acquired weather data, topographical data, and past accident data.

[0357] Step 3:

[0358] Server: Analyzes collected data and performs risk assessment.

[0359] Input: Weather data, terrain data, historical accident data.

[0360] Specific operation: The server analyzes meteorological data to identify weather patterns, analyzes topographical data to evaluate the elevation difference and ground surface condition of the route, and analyzes past accident data to extract risk factors. These are then integrated and used by an AI algorithm to perform a risk assessment.

[0361] Output: Risk assessment results (risk points, risk level).

[0362] Step 4:

[0363] Server: Generates the optimal climbing route based on the risk assessment results.

[0364] Input: Risk assessment results.

[0365] How it works: The server uses an AI algorithm to generate multiple climbing routes, evaluate the safety of each route, and select the safest one. Route information includes distance, elevation gain, estimated time, etc.

[0366] Output: Generated optimal climbing route information.

[0367] Step 5:

[0368] Server: Sends optimal route information and risk warning information to the device.

[0369] Input: Generated optimal climbing route information.

[0370] Specific operation: The server sends route information and details of risk warning areas to the terminal.

[0371] Output: Information on the best climbing route and risky areas is sent to the device.

[0372] Step 6:

[0373] Terminal: Displays optimal route information and risk warning areas on a map.

[0374] Input: Information on optimal climbing routes and risk warning points sent from the server.

[0375] Specific operation: The device displays route lines and markers for risk warning areas on a map, allowing users to plan safe mountain climbing.

[0376] Output: A visual representation of the climbing route and risk warnings to the user.

[0377] Step 7:

[0378] Server: Monitors the user's location in real time while climbing and notifies them of sudden changes in weather and other risks.

[0379] Input: User location, real-time weather data, and other risk information.

[0380] Specific operation: Regularly acquires GPS data and receives the latest weather information in real time from the weather service API. If a risk is detected, it prepares to notify the user immediately.

[0381] Output: Real-time notifications (emergency alerts and warnings).

[0382] Step 8:

[0383] Terminal: Displays emergency alerts and warnings in real time.

[0384] Input: Notification from the server.

[0385] Specific behavior: The device will immediately display the received notification on the screen and alert the user with sound and vibration.

[0386] Output: Visual and audible alerts and reminders provided to the user.

[0387] Step 9:

[0388] Server: After the climb is completed, the server receives feedback from the user and organizes the climbing record.

[0389] Input: User feedback, data collected during the climb.

[0390] Specific operation: The server analyzes the collected data and organizes the climbing route history, weather changes, risk warning points, etc. It then generates a detailed report including reviews and points for improvement.

[0391] Output: The generated climbing report.

[0392] Step 10:

[0393] Terminal: Displays the generated report to the user.

[0394] Input: Climbing report sent from the server.

[0395] What it does: The device displays a visual report to help users plan their next hike.

[0396] Output: A detailed climb report that is displayed to the user.

[0397] Step 11:

[0398] Terminal: Provides a data collection interface for the emotion engine.

[0399] Input: Data such as the user's facial expressions, voice, and heart rate.

[0400] How it works: The emotion engine uses a facial recognition camera, a voice analysis microphone, and a heart rate sensor to collect user emotional data.

[0401] Output: The collected emotion data is sent to the server.

[0402] Step 12:

[0403] Server: Analyzes the collected emotion data.

[0404] Input: Collected emotion data.

[0405] Specific operation: The server uses the emotion engine to evaluate the user's mental state in real time and reflects this in risk assessment and route optimization as necessary.

[0406] Output: Evaluation results and countermeasures based on the user's psychological state.

[0407] Step 13:

[0408] Server: Generates advice and warnings based on emotion data and sends them to the device.

[0409] Input: Evaluation results based on the user's mental state.

[0410] Specific behavior: The server generates appropriate advice and reminders based on the user's emotional state. For example, it creates notifications including "Take a break" and "Breathing exercises for relaxation."

[0411] Output: Advice and warnings based on emotion data are sent to the device.

[0412] Step 14:

[0413] Device: Displays advice and warnings to users based on emotional data.

[0414] Input: Notifications based on emotion data sent from the server.

[0415] Specific operation: The device displays the received advice or warning on the screen and notifies the user by sound or vibration.

[0416] Output: Specific advice or reminders provided to the user.

[0417] (Application example 2)

[0418] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0419] Existing mountain climbing planning systems perform risk assessments using weather data, topographical data, and past accident data, but do not provide real-time safety support that takes into account the climber's mental state. In particular, it is necessary to provide appropriate advice and warnings for safe driving by taking into account the driver's emotional state and stress during long-distance drives in autonomous vehicles. The present invention aims to solve this problem and improve the safety of autonomous vehicles by introducing an emotion engine.

[0420] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and performing risk assessment; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during mountain climbing; means for organizing the mountain climbing record and generating a report after the mountain climbing is completed; and means for collecting and analyzing emotional data in real time and providing appropriate advice and warnings based on the mental state. This makes it possible to realize a safe and comfortable driving plan not only for mountain climbers but also for drivers of autonomous vehicles.

[0421] "Mountain climbing plan information" is information that a mountain climber inputs detailed information such as the starting point, destination, planned date and time, and mountain climbing experience.

[0422] "Weather data" refers to data regarding weather conditions in a target area obtained using a weather service API.

[0423] "Topographic Data" means data about the surface conditions of a target area collected from a geographic information system (GIS).

[0424] "Past accident data" is information about accidents that have occurred in the past, obtained from a mountain climbing accident database.

[0425] "Risk assessment" is the process of analyzing collected weather data, topographical data, and past accident data to assess the risk level for each climbing route.

[0426] The "optimal climbing route" is the climbing route that is evaluated as the safest among multiple candidate routes based on the results of the risk assessment.

[0427] "Real-time monitoring" is a function that monitors the user's location and other data in real time while climbing, and notifies them as needed.

[0428] "Report generation" is the process of organizing the climbing record after the climb is completed and creating a detailed report that includes reflections and suggestions for improvement for the next climb.

[0429] The "Emotion Engine" is a system that collects and analyzes data such as the user's facial expressions, voice, and heart rate to evaluate their mental state.

[0430] "Emotion data" refers to data such as the user's facial expressions, voice, and heart rate collected through the emotion engine.

[0431] "Advice and warnings" refers to the provision of information based on emotional data to encourage the user to take appropriate action or take appropriate precautions.

[0432] System Overview

[0433] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing and in autonomous vehicles. This system inputs mountain climbing plan information, collects and analyzes weather data, topographical data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions. The emotion engine also has the function of providing appropriate advice and warnings based on the user's emotional state.

[0434] Hardware and software used

[0435] Hardware: In-vehicle GPS, cameras, microphones, heart rate monitors, autonomous driving control systems, wearable devices

[0436] Software: Emotion recognition engine, weather data API, terrain information API, traffic information API, AI analysis algorithm

[0437] Program processing explanation

[0438] 1. Enter operation plan information

[0439] The user inputs information about the mountain climbing or driving plan (starting point, destination, departure time, etc.) into the terminal, and the information is sent to the server.

[0440] 2. Data collection

[0441] The server collects weather data, terrain data, traffic information, and past accident data based on the input plan information. Weather data is obtained from a weather service API, terrain data from a geographic information system (GIS), and traffic information from a traffic information API. Accident data is obtained from a mountain climbing accident database and a traffic accident database.

[0442] 3. Risk assessment and route optimization

[0443] The server analyzes the collected data and performs risk assessment. In this process, an AI algorithm is used to analyze the data and calculate risk points and risk levels. The server then generates an optimal route and identifies risk areas.

[0444] 4. Notice to Users

[0445] The server sends information about the optimal route and points of caution to the device, which then displays this information on a map for the user.

[0446] 5. Real-time monitoring and notifications

[0447] The server monitors the user's location in real time while hiking or driving. If there is a sudden change in weather conditions or a traffic risk, it will quickly analyze the situation and notify the user as necessary. The emotion engine analyzes the user's heart rate, facial expressions, and voice data, and suggests taking a break if stress or fatigue increases.

[0448] 6. Organizing mountain climbing and driving records

[0449] After the hike or drive is over, the server analyzes the user's behavior and situation and generates a detailed report, which also includes a review and suggestions for improvement for the next time.

[0450] Examples and prompts

[0451] Specific examples

[0452] Suppose a user plans a drive from Tokyo to Osaka. The user inputs the starting point, destination, and departure time and sends them to the server. The server collects weather, terrain, and traffic information and performs a risk assessment. It calculates the optimal route and risk-related areas and displays this information on the in-car display. During the drive, the system monitors the driver's emotional state and suggests taking a break if stress levels are high. It also notifies the driver in real time of any sudden weather changes or traffic risks. After the drive is over, a detailed driving report is generated to help plan the next drive.

[0453] Prompt Sentence Examples

[0454] As an assistant AI, please provide appropriate advice to the driver based on the following information:

[0455] Driving plan: Tokyo to Osaka, Scheduled departure time: 8:00 AM

[0456] Driver's emotional state: High stress

[0457] advice:

[0458] "You are currently traveling from Tokyo to Osaka. You seem to be feeling stressed along the way, so we recommend you take a break at a nearby service area. Take a deep breath and relax."

[0459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0460] Step 1:

[0461] The user inputs the hiking or driving plan information (starting point, destination, departure time, etc.) into the terminal, and the information is sent to the server. Based on this input, the server saves the plan information and prepares for the necessary data collection.

[0462] Step 2:

[0463] Based on the input plan information, the server collects weather data, terrain data, traffic information, and past accident data. The server calls the weather service API to obtain weather data, obtains terrain data using a geographic information system (GIS), and collects traffic information using a traffic information API. In addition, it obtains past accident data from a mountain climbing accident database and a traffic accident database. This allows the server to obtain sufficient auxiliary data.

[0464] Step 3:

[0465] The server performs risk assessment based on collected weather data, terrain data, traffic information, and past accident data. It analyzes the data using AI analysis algorithms. Specifically, it calculates risk points and risk levels by integrating factors such as weather patterns, terrain complexity, traffic congestion, and past accident frequency. This generates basic data for safety assessment.

[0466] Step 4:

[0467] The server generates the optimal climbing or driving route based on the risk assessment results. Using an AI algorithm, it calculates the least risky route from the collected and analyzed data and generates detailed information about it (distance, elevation difference, estimated time, etc.). This provides safe route information that users can choose from.

[0468] Step 5:

[0469] The server sends the generated optimal route and information on risk and caution areas to the device. The device displays this information on a map for the user to use. Based on this information, the user can create a safe and optimal plan.

[0470] Step 6:

[0471] The server monitors the user's location information in real time while hiking or driving. It acquires GPS data to confirm the user's current location, and performs necessary analysis and promptly notifies the user in the event of a sudden change in weather conditions or traffic risks. This allows the user to respond to risks in real time.

[0472] Step 7:

[0473] The emotion engine analyzes the user's heart rate, facial expressions, and voice data, and provides appropriate advice or suggests rest if stress or fatigue increases. The server receives this data in real time and sends a notification to the user based on the emotion engine's evaluation results. This enables safety measures that take the user's mental state into consideration.

[0474] Step 8:

[0475] After the hike or drive is over, the server analyzes the user's behavior and situation and generates a detailed report. The report provides feedback to the user, including reflections and suggestions for improvement for the next trip. The device displays this report to the user, allowing them to use it to plan their next trip.

[0476] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0477] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0478] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0479] [Second embodiment]

[0480] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0481] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0482] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0483] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0484] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0485] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0486] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0487] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0488] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0489] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0490] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0491] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0492] System Overview

[0493] This invention is a system that uses AI to support safe mountain climbing planning. The system includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and conducting risk assessments, a means for generating optimal mountain climbing routes, a means for real-time monitoring and notification, and a means for organizing mountain climbing records and generating reports. Each of these means and specific examples are described below.

[0494] Program processing

[0495] 1. Enter your climbing plan information

[0496] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.). The user inputs the necessary information and presses the send button.

[0497] User: Enters a mountain climbing plan and sends it from the device to the server.

[0498] 2. Data collection

[0499] Server: Receives climbing plan information sent by users. Based on this information, it collects weather data, topography data, and past accident data for the target area. Weather data is obtained from weather services, topography data from a geographic information system (GIS), and past accident data from a climbing accident database.

[0500] 3. Risk assessment and route optimization

[0501] Server: Analyzes collected data and performs risk assessment using AI. Risk assessment includes factors such as weather, terrain, and past accident information. Based on the assessment results, multiple routes are generated and the risk points and risk levels for each are calculated. The route with the least risk is selected and detailed route information is generated.

[0502] 4. Providing optimal routes and important points to note

[0503] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0504] Terminal: Displays the optimal route and risk warning points on a map to provide users with information.

[0505] User: Create a safe mountain climbing plan based on the displayed information.

[0506] 5. Real-time monitoring and notifications

[0507] Server: Monitors the user's location in real time while they are climbing. If there are any changes in weather conditions or terrain information, it analyzes them promptly and sends a notification to the device.

[0508] Device: Display emergency alerts and warning notifications to the user.

[0509] 6. Organizing climbing records and generating reports

[0510] Server: After the climb is completed, the server receives feedback from users, organizes the climb record, and generates a detailed report including reflections and areas for improvement.

[0511] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[0512] Specific examples

[0513] Assume that the user is planning to climb Mountain A next Saturday.

[0514] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0515] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[0516] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[0517] 4. The server sends the optimal route information and risk warning information to the terminal.

[0518] 5. The device displays the optimal route and risk-related areas on a map for the user.

[0519] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[0520] 7. The device displays emergency alerts and warnings to the user.

[0521] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[0522] 9. The device can display this report to the user, helping them plan their next climb.

[0523] This will enable safe and secure mountain climbing using a system that makes full use of AI.

[0524] The processing flow will be explained below.

[0525] Step 1:

[0526] The user inputs mountain climbing plan information into the input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[0527] Step 2:

[0528] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data for the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[0529] Step 3:

[0530] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[0531] Step 4:

[0532] The server then inputs the analysis results into an AI algorithm to perform a risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, the risk points and risk level for each route are calculated.

[0533] Step 5:

[0534] The server generates multiple climbing routes based on the risk assessment results. From the generated routes, it selects the route that is assessed as having the least risk and generates detailed route information. The detailed route information includes distance, elevation change, estimated time, etc.

[0535] Step 6:

[0536] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[0537] Step 7:

[0538] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[0539] Step 8:

[0540] During the climb, the user's location information is periodically updated by the device and sent to the server.

[0541] Step 9:

[0542] The server monitors the received location information in real time, and if there is a sudden change in weather conditions or other risk information, it analyzes it promptly and notifies the user of the risk information as necessary.

[0543] Step 10:

[0544] The device displays emergency alerts and warning notifications to the user in real time.

[0545] Step 11:

[0546] After the climb is completed, the server receives feedback from the user, organizes the climb record, analyzes the user's behavior and situation, and generates a detailed report that includes reflections and areas for improvement.

[0547] Step 12:

[0548] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[0549] Example 1

[0550] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0551] Mountaineering is an activity in nature, and it involves many risks, such as changes in weather and terrain, and unexpected events. It is particularly difficult for inexperienced climbers to plan a safe climb and manage the risks. There is also a lack of real-time risk notifications during the climb and feedback after the climb is complete. Furthermore, there is a need for systems that provide optimal routes based on collected data and support for enjoying mountaineering while ensuring safety.

[0552] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0553] In this invention, the server includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for performing risk assessment using a generative AI model, a means for generating an optimal mountain climbing route based on the risk assessment results, a means for real-time monitoring and notification during mountain climbing, and a means for organizing mountain climbing records and generating a report after the mountain climbing is completed, thereby enabling support for users to climb mountains safely.

[0554] "Mountain climbing plan information" is information such as the starting point, destination, planned date and time, and mountain climbing experience that the user inputs in order to carry out mountain climbing activities.

[0555] "Weather data" is data that indicates the weather conditions in a specific area, and includes information on weather conditions such as temperature, precipitation, and wind speed.

[0556] "Topography data" refers to data that indicates the topography and geographical features of the target area, including information such as elevation, slope, and topography.

[0557] "Past accident data" is information about accidents and incidents that have occurred during mountain climbing in the past, including the location, cause, and detailed circumstances of the accident.

[0558] A "generative AI model" is a model that uses artificial intelligence technology to analyze large amounts of data, learn patterns, and make predictions and classifications.

[0559] "Risk assessment" is the process of assessing the risks during mountain climbing by analyzing the weather, terrain, past accident information, etc. based on the data obtained.

[0560] The "optimal climbing route" is the safest and most efficient climbing route selected based on risk assessment.

[0561] "Real-time monitoring" is the process of tracking the user's location and environmental information in real time while climbing, and constantly monitoring the latest situation.

[0562] A "notification" is a warning or caution message sent from the server to the user terminal, and is information necessary for the user to take appropriate action.

[0563] A "mountain climbing record" is a detailed record of mountain climbing activities, and includes information such as start time, finish time, walking distance, and elevation difference.

[0564] A "report" is a detailed report generated based on climbing records and feedback, and includes reflections and suggestions for improvement that will be useful in planning your next climb.

[0565] This invention relates to a system for safely supporting mountain climbing plans, and uses AI technology to perform risk assessment, propose optimal routes, perform real-time monitoring, and generate reports.

[0566] System Overview

[0567] The system includes a means for inputting climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and performing risk assessment using a generative AI model, a means for generating optimal climbing routes, a means for real-time monitoring and notification, and a means for organizing climbing records and generating reports.

[0568] Entering mountain climbing plan information

[0569] The terminal provides an interface for the user to input mountain climbing plan information. This interface includes items such as the starting point, destination, planned date and time, and climbing experience. As a specific example, if a user plans to depart from trailhead A to summit B at 6:00 a.m. on September 15th, the user can enter this information into the terminal and press the "send" button to send the information to the server.

[0570] Data collection

[0571] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data. Weather data is obtained through the weather service API (e.g., OpenWeatherMap), terrain data is obtained from a geographic information system (GIS), and past accident data is obtained from a mountain climbing accident database.

[0572] Risk assessment and route optimization

[0573] The server integrates the collected data and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated and the risk points and risk level of each route are calculated. The route with the least risk is selected and detailed route information is generated, including a route map, distance, elevation change, and risk warning points.

[0574] Providing optimal routes and points to note

[0575] The server organizes information on optimal routes and risk-related points, and sends it to the terminal. The terminal displays this information on a map for the user. The user can then create a safe mountain climbing plan based on the displayed information.

[0576] Real-time monitoring and notifications

[0577] The server periodically collects GPS data to monitor the user's location in real time while climbing. The server tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, the server promptly sends a notification to the device and displays an emergency alert or warning to the user.

[0578] Organize your climbing records and generate reports

[0579] After the climb is completed, the server receives feedback from the user and organizes the climb record. For example, a detailed report is generated by collecting data such as the start time, finish time, walking distance, and elevation gain. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via their device. The user can use this report to plan their next climb.

[0580] Specific examples of prompt sentences are as follows:

[0581] "Plan your next climb by analyzing past climb records and weather data to suggest the best route."

[0582] "I'd like to plan a climbing route to Summit A. The starting point is Trailhead B, and the planned time and date is 6:00 a.m. on September 15th. What is the best route and what are the risks and precautions I should take?"

[0583] This allows users to climb mountains safely and efficiently, and enjoy nature with peace of mind.

[0584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0585] Step 1:

[0586] The terminal displays a dedicated interface for the user to input mountain climbing plan information. The user inputs information such as the starting point, destination, planned date and time, and mountain climbing experience into this interface. For example, the starting point may be "Trailhead A," the destination may be "Summit B," the planned date and time may be "September 15th, 6:00 AM," and the climbing experience may be "Intermediate." Once this information is input, the user presses the "Send" button to send the information to the server. The input is the mountain climbing plan information input by the user, and the output is the mountain climbing plan information sent to the server.

[0587] Step 2:

[0588] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects the necessary data. Specifically, it obtains weather data from a weather service API (e.g., OpenWeatherMap), terrain data from a geographic information system (GIS), and past accident data from a mountain climbing accident database. The input is the received mountain climbing plan information, and the output is the collected weather data, terrain data, and past accident data.

[0589] Step 3:

[0590] The server integrates collected weather data, terrain data, and past accident data, and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated, and the risk points and risk levels for each route are calculated. The route with the least risk is selected, and detailed route information is generated. The input is the collected data, and the output is the risk assessment results and optimal route information.

[0591] Step 4:

[0592] The server organizes the selected optimal route information and information on risk-warning areas and sends it to the terminal. The terminal displays the received information on a map. This allows the user to visually confirm detailed information for creating a safe mountain climbing plan. The input is the optimal route information and information on risk-warning areas, and the output is the information sent to the terminal.

[0593] Step 5:

[0594] The server periodically collects GPS data to monitor the user's location in real time while climbing. It tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, it promptly sends a notification to the user's device, which then displays it to the user. The input is the GPS data collected in real time, and the output is notification information.

[0595] Step 6:

[0596] After the climb is completed, the server receives feedback from the user, organizes the climb record, and generates a detailed report. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via a terminal. The input is the user's feedback and the climb record, and the output is the generated detailed report.

[0597] This makes it possible to support the user in climbing safely and efficiently through each step.

[0598] (Application example 1)

[0599] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0600] Conventional navigation systems and mountain climbing planning support systems lack the functionality to comprehensively consider real-time changing weather data, road conditions, past accident data, etc. to perform risk assessments and present optimal routes. As a result, users could face unexpected risks. In addition, they lacked the functionality to generate detailed reports after a hike or drive to evaluate safety and efficiency and incorporate them into future plans. This could compromise user safety and the accuracy of their plans.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0602] In this invention, the server includes a means for inputting mountain climbing plan information or driving plan information, a means for collecting weather data, topographical data, road data, and past accident data based on the input mountain climbing plan information or driving plan information, and a means for analyzing the collected data and performing risk assessment. This makes it possible to provide optimal routes that reflect a variety of information, such as weather, road conditions, and past accident data, in real time. It also provides real-time monitoring and emergency notifications during mountain climbing or driving, improving user safety. Furthermore, a detailed report can be generated after the end of the climb or drive, which can be used to plan the next trip.

[0603] "Mountain climbing plan information" is information entered by the mountain climber, such as the starting point, destination, planned date and time, and mountain climbing experience.

[0604] "Driving plan information" is information such as the departure point, destination, scheduled date and time entered by the driver.

[0605] "Weather data" refers to information about the weather, such as the current weather, temperature, precipitation, and wind speed.

[0606] "Topographic data" refers to geographical information such as mountain and terrain features, elevation, and slope.

[0607] "Road data" refers to geographical information about roads, such as road elevation, slope, and road width.

[0608] "Past accident data" refers to historical information on traffic accidents and mountain climbing accidents that have occurred in specific areas or on specific roads.

[0609] A "means" is a tool, method, or device used to achieve a particular end.

[0610] "Risk assessment" is the process of determining the existence and extent of potential risks based on weather conditions, road conditions, past accident data, etc.

[0611] The "optimal climbing route" is the climbing route that is analyzed to have the least risk, taking into account safety and efficiency.

[0612] An "optimal driving route" is a driving route that has been analyzed to have the least risk, taking into account safety and efficiency.

[0613] "Real-time monitoring" is the process of monitoring ongoing situations in real time and collecting and analyzing data instantly.

[0614] "Notification" is the act of conveying important information or warnings to a user.

[0615] A "report" is a document that organizes records after a hike or drive and provides information in a detailed report format.

[0616] "Location information" is information that indicates a specific location, such as the coordinates of the user's current location.

[0617] A "server" is a computer system that collects, analyzes, and provides data.

[0618] This invention is a system for supporting safe mountain climbing and driving plans. Based on the planning information entered by the user, the system collects and analyzes weather data, topographical data, road data, and past accident data, performs risk assessments, and provides optimal routes in real time. After the mountain climbing or driving is completed, a detailed report is generated and the safety and efficiency are evaluated.

[0619] The program proceeds as follows:

[0620] 1. Enter planning information

[0621] Users use their smartphones or in-car infotainment systems to input planning information such as starting point, destination, planned date and time, etc. This information is then sent from the device to a server.

[0622] 2. Data collection

[0623] Based on the planning information, the server collects weather data, topographical data, road data, and past accident data. These data are obtained from weather data APIs (e.g., OpenWeather), geographic information systems (GIS), and traffic information APIs.

[0624] 3. Risk assessment and route optimization

[0625] The server analyzes the collected data using AI analysis tools such as TensorFlow and PyTorch to perform risk assessments, including weather, road conditions, terrain, and past accident information. Based on the results of this analysis, the system generates the optimal route.

[0626] 4. Providing optimal routes and risk warnings

[0627] The generated optimal route and risk information are sent from the server to the terminal, which then displays this information on a map and provides it to the user.

[0628] 5. Real-time monitoring and notifications

[0629] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, it immediately reanalyzes the data and sends warnings and alerts to the user's device, ensuring the user's safety.

[0630] 6. Record keeping and report generation

[0631] After the hike or drive is complete, the server compiles the records and generates a detailed report that provides useful information for planning your next trip.

[0632] Examples:

[0633] If the user enters "Tokyo" as the departure point, "Shizuoka" as the destination, and "October 20th 13:00" as the scheduled time and date, the following can be used as the prompt text:

[0634] "Use AI to evaluate the optimal route from Tokyo to Shizuoka and create a safe driving route."

[0635] The server collects weather data from OpenWeather, terrain data from GIS, and road and real-time traffic information from a traffic information API. Using TensorFlow, it performs risk assessments based on this data and generates optimal routes. The generated information is sent to the in-car navigation system and smartphone and displayed in real time. If there are sudden changes in weather or road conditions during hiking or driving, the server immediately reanalyzes the data and notifies the user. After completion, it organizes detailed route history and generates a report that can be used to plan the next trip.

[0636] In this way, by implementing the present invention, the user can make safe and efficient mountain climbing and driving plans.

[0637] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0638] Step 1:

[0639] A user inputs planning information, such as a starting point, destination, and planned date and time, using a smartphone or in-car infotainment system. This information is sent from the device to a server. Inputs include the starting point, destination, date and time, and user-specific information. Outputs provide this planning information to the server.

[0640] Step 2:

[0641] The server collects weather data, topographical data (or road data), and past accident data based on the received plan information. Here, data is acquired using weather data APIs (such as OpenWeather), geographic information systems (GIS), and traffic information APIs. Plan information is used as input, and weather data, topographical data, road data, and past accident data are compiled as output.

[0642] Step 3:

[0643] The server analyzes the collected data and performs risk assessment using AI analysis tools (TensorFlow and PyTorch). This process evaluates the risk level based on weather conditions, road conditions, terrain, and past accident history. The input includes various collected data, and the output generates a risk assessment result.

[0644] Step 4:

[0645] The server generates the optimal route based on the risk assessment results. In this process, an algorithm for calculating the optimal route is applied to select a safe and efficient route for the user. The input is the risk assessment result, and the output is the optimal route information.

[0646] Step 5:

[0647] The server sends the generated information about the optimal route and risk warning points to the terminal. The terminal displays this information on a map and provides it to the user. The optimal route information is the input, and the route and risk warning points displayed on the map are provided to the user as the output.

[0648] Step 6:

[0649] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, the data is reanalyzed and alerts or warnings are sent to the user. In this step, data is processed and reevaluated in real time. The input includes real-time location information and new weather and road data, and the output is emergency alerts and notifications sent to the device.

[0650] Step 7:

[0651] After the climb or drive is completed, the server organizes the records and generates a detailed report, which provides useful information for planning the next climb or drive. The input includes the user's activity history and planning information, and the output is a detailed report, which is sent to the user's device and can be viewed.

[0652] The above processing flow enables safe and efficient mountain climbing and driving plans to be realized.

[0653] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0654] System Overview

[0655] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system includes functions for inputting mountain climbing plan information, collecting and analyzing weather data, topographical data, and past accident data, risk assessment, generating optimal mountain climbing routes, real-time monitoring and notifications, organizing mountain climbing records and generating reports, and an emotion engine that recognizes the user's emotions. It also includes a function that uses the emotion engine to provide appropriate advice and warnings based on the user's emotional state. Each method and specific examples are described in detail below.

[0656] Program processing

[0657] 1. Enter your climbing plan information

[0658] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.) When the user inputs the necessary information and presses the send button, the input information is sent to the server.

[0659] User: Enters a mountain climbing plan and sends it from the device to the server.

[0660] 2. Data collection

[0661] Server: Receives mountain climbing plan information sent by the user. Based on this information, it collects weather data, topographical data, and past accident data for the target area from the necessary data sources. Weather data is obtained from the weather service API, topographical data is collected from a geographic information system (GIS), and accident data is obtained from a mountain climbing accident database.

[0662] 3. Risk assessment and route optimization

[0663] Server: Analyzes collected weather data, topographical data, and past accident data. Weather data is used to extract and analyze weather patterns and forecasts, topographical data is used to extract elevation changes and ground surface conditions along the route, and accident data is used to extract and analyze risk factors.

[0664] Server: The analysis results are fed into an AI algorithm to perform risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, risk points and risk levels for each route are calculated.

[0665] Server: Generates multiple climbing routes based on the risk assessment results. From the generated routes, selects the route assessed as having the least risk and generates detailed route information, including distance, elevation difference, estimated time, etc.

[0666] 4. Providing optimal routes and important points to note

[0667] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0668] Device: The optimal route and risk warning points are displayed on a map to provide users with information so they can plan their safe and optimal mountain climbing trip.

[0669] User: Create a safe mountain climbing plan based on the displayed information.

[0670] 5. Real-time monitoring and notifications

[0671] Server: Monitors the user's location in real time while they are climbing. If there is a sudden change in weather conditions or other risk information, it analyzes the situation promptly and notifies the user of the risk information as necessary.

[0672] Device: Display emergency alerts and warning notifications to users in real time.

[0673] 6. Organizing climbing records and generating reports

[0674] Server: After the climb is completed, the server receives feedback from the user and organizes the climb record. It analyzes the user's behavior and situation and generates a detailed report, which also includes reflections and suggestions for improvement.

[0675] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[0676] Introducing the Emotion Engine

[0677] 7. Emotion Engine Settings

[0678] Terminal: Provides an interface for the emotion engine to collect data such as the user's facial expressions, voice, and heart rate to measure the user's emotions.

[0679] User: Emotional data is provided to the device in real time via climbing equipment or wearable devices.

[0680] 8. Collecting and analyzing emotional data

[0681] Server: Receives collected emotion data and analyzes it in real time. The emotion engine evaluates the user's mental state and reflects it in risk assessment and route optimization as necessary.

[0682] 9. Emotion-based advice and notifications

[0683] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings if the user is under a high level of mental stress or feels stressed.

[0684] Device: Providing advice and reminders to the user, such as suggestions for resting places to calm down or simple breathing exercises to help them relax.

[0685] Specific examples

[0686] Assume that the user is planning to climb Mountain A next Saturday.

[0687] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0688] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[0689] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[0690] 4. The server sends the optimal route information and risk warning information to the terminal.

[0691] 5. The device displays the optimal route and risk-related areas on a map for the user.

[0692] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[0693] 7. The device displays emergency alerts and warnings to the user.

[0694] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[0695] 9. The device can display this report to the user, helping them plan their next climb.

[0696] The introduction of an emotion engine allows the system to monitor the user's mental state in real time and provide not only risk assessment but also psychological support. For example, if the user is feeling stressed, the system can suggest appropriate rest areas and notify them of ways to relax, providing a safe and supported climbing experience.

[0697] The processing flow will be explained below.

[0698] Step 1:

[0699] The user inputs mountain climbing plan information into an input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[0700] Step 2:

[0701] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data related to the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[0702] Step 3:

[0703] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[0704] Step 4:

[0705] The server then uses an AI algorithm to perform a risk assessment based on the analysis results. This assessment process takes into consideration weather, terrain, and past accident information to calculate the risk level for each climbing route. The assessment results then confirm the risk points and their levels.

[0706] Step 5:

[0707] The server generates multiple climbing routes based on the risk assessment results, selects the route with the lowest risk from the generated routes, and generates detailed route information, including distance, elevation change, estimated time required, and other information.

[0708] Step 6:

[0709] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[0710] Step 7:

[0711] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[0712] Step 8:

[0713] Users use wearable devices or terminals to provide their own emotional data (heart rate, facial expressions, voice, etc.) to the terminals in real time.

[0714] Step 9:

[0715] The terminal collects emotion data and transmits it to the server.

[0716] Step 10:

[0717] The server analyzes the received emotional data and assesses the user's emotional state, adjusting the risk assessment accordingly if the user is experiencing high mental load or stress.

[0718] Step 11:

[0719] During the climb, the user's location information is also periodically sent from the device to the server.

[0720] Step 12:

[0721] The server monitors the user's situation based on real-time location and emotion data received, and immediately analyzes any sudden changes in weather or other risk factors and generates warnings as needed.

[0722] Step 13:

[0723] The device displays emergency alerts and reminder messages to users in real time, including advice based on their emotional state, such as "take a break to relax."

[0724] Step 14:

[0725] After the climb is complete, the server receives feedback from the user, organizes the climb record, and analyzes the user's behavior, situation, and emotional data to generate a detailed report.

[0726] Step 15:

[0727] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[0728] The above processing flow allows the user to climb mountains safely and efficiently, and by using the emotion engine, mental support is also provided at the same time.

[0729] Example 2

[0730] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0731] Mountaineering is a challenge to nature, but it also involves various risks, making it important to plan ahead and take into account weather changes, terrain conditions, and past accident data. However, comprehensively collecting and analyzing this information requires specialized knowledge and large amounts of data processing, making it difficult for average climbers. Furthermore, monitoring risks in real time during a climb and organizing records afterward are both cumbersome, and a system to address these issues is needed. Furthermore, supporting safe climbing by taking into account the climber's emotional state is also an important issue.

[0732] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0733] In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and conducting risk assessments; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during the mountain climbing; means for compiling a mountain climbing record and generating a report after the mountain climbing is completed; means for collecting and analyzing the user's emotional data; and means for providing appropriate advice and warnings based on the analyzed emotional data. This increases the safety of mountain climbing plans. It also provides real-time risk monitoring during the mountain climbing and appropriate support based on the user's emotional state, allowing for efficient record organization after the mountain climbing is completed.

[0734] "Mountain climbing plan information" refers to information necessary when climbing a mountain, and specifically includes the starting point, destination, planned date and time, mountain climbing experience, and the like.

[0735] "Weather data" refers to data that indicates weather conditions related to the climbing route or climbing journey, and specifically includes temperature, humidity, wind speed, precipitation, weather forecast, and the like.

[0736] "Topographical data" refers to data that indicates the physical characteristics of a climbing route, and specifically includes the type of ground surface, elevation difference, gradient, geographical features, and the like.

[0737] "Past accident data" refers to data related to past accidents and troubles that have occurred while climbing mountains, and specifically includes the location of the accident, the cause, the date and time of the accident, and the extent of the damage.

[0738] "Risk assessment" refers to the process of analyzing collected data and evaluating the risks and dangers associated with a climbing route, taking into account factors such as weather fluctuations, the difficulty of the terrain, and past accident data.

[0739] A "climbing route" is a path from a starting point to a destination, usually depicted on a map. The optimal climbing route is selected based on a risk assessment.

[0740] "Real-time monitoring" refers to the process of monitoring a user's location and environmental conditions in real time while climbing, including the continuous collection and analysis of GPS and weather data.

[0741] "Notification" refers to the process of providing information to users during the climbing or planning stage, specifically including emergency alerts, risk warnings, advice, etc.

[0742] A "mountain climbing record" is a record of the progress and results of mountain climbing activities, and specifically includes the mountain climbing route, activity history, environmental conditions, user feedback, and the like.

[0743] A "report" is a document that compiles collected and organized climbing records, and specifically includes a review of the climb and areas for improvement for the next time.

[0744] "Emotion data" is data that indicates the user's psychological state and emotions, and specifically includes facial expressions, voice, heart rate, and the like.

[0745] "Advice" refers to advice and guidance provided to users during the climbing or planning stages, and specifically includes suggestions for safe climbing and ways to reduce stress.

[0746] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system inputs mountain climbing plan information, collects and analyzes weather data, terrain data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions.

[0747] Hardware and Software Used

[0748] The system uses the following hardware and software:

[0749] Terminal: A device that acts as a user interface, such as a smartphone or tablet.

[0750] Server: Data collection, analysis, route generation, monitoring, and report generation are performed using a high-performance cloud server.

[0751] Weather Service API: Used to retrieve weather data.

[0752] Geographic Information Systems (GIS): Used to collect topographical data.

[0753] Mountaineering accident database: Used to obtain past accident data.

[0754] Emotion engine: Analyzes the user's emotional data and provides appropriate advice and warnings.

[0755] Generative AI models: Used for risk assessment and optimal route generation.

[0756] Explanation of program processing

[0757] 1. Enter your climbing plan information

[0758] Terminal: Provides an interface for users to input their starting point, destination, planned date and time, climbing experience, etc. When the user inputs the information and presses the send button, the information is sent to the server.

[0759] User: Enters the climbing plan into the device interface and presses the send button.

[0760] 2. Data collection

[0761] Server: Based on the received mountain climbing plan information, it collects weather data, terrain data, and past accident data. Weather data is collected from the weather service API, terrain data from GIS, and accident data from the mountain climbing accident database.

[0762] 3. Risk assessment and route optimization

[0763] Server: Analyzes the collected data and performs risk assessments using AI algorithms based on weather data, topographical data, and past accident data.

[0764] Server: Generates optimal climbing routes based on risk assessment. Route information includes distance, elevation gain, estimated time, etc.

[0765] 4. Providing optimal routes and important points to note

[0766] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0767] Terminal: The received information is displayed on a map, allowing users to create a safe and optimal mountain climbing plan.

[0768] 5. Real-time monitoring and notifications

[0769] Server: Monitors the user's location in real time while climbing, and notifies them of sudden changes in weather and risks.

[0770] Terminal: Displays emergency alerts and warnings in real time.

[0771] 6. Organizing climbing records and generating reports

[0772] Server: Receives user feedback after the climb, organizes the climbing record, generates detailed reports, and provides them to users.

[0773] Terminal: The generated report can be displayed to the user to help them plan their next mountain climbing trip.

[0774] 7. Emotion Engine Configuration and Analysis

[0775] Terminal: Provides a data collection interface for the emotion engine, including a camera for recognizing the user's facial expressions, a microphone for analyzing voice, and a heart rate sensor.

[0776] User: Provides real-time emotional data through climbing equipment and wearable devices.

[0777] Server: Analyzes the collected emotional data, evaluates the user's mental state, and reflects this in risk assessment and route optimization as necessary.

[0778] 8. Emotion-based advice and notifications

[0779] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings when the user is under high mental stress or feels stressed.

[0780] Device: Displays specific advice and reminders to the user, such as "Take a break" or "Breathing exercises to relax."

[0781] Specific examples

[0782] Assume that the user is planning to climb Mountain A next Saturday.

[0783] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0784] 2. The server collects surrounding weather data, topographical data, and past accident data, and organizes it specifically for Mountain A.

[0785] 3. The server performs a risk assessment, selects the safest route, and generates detailed information about it.

[0786] 4. The server sends the optimal route information and risk warning information to the terminal.

[0787] 5. The device displays the optimal route and risk warning points on a map for the user.

[0788] 6. During the climb, the server monitors the user's location in real time and notifies them of any sudden changes in the weather.

[0789] 7. The device displays emergency alerts and warnings.

[0790] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report that users can use to plan their next climb.

[0791] 9. The device collects emotional data using a facial recognition camera and heart rate sensor. The server analyzes the emotional data in real time and generates appropriate advice if stress is detected. The device then displays suggestions for reducing stress.

[0792] Prompt Sentence Examples

[0793] "Please tell me what climbing routes are safe."

[0794] "I'm an intermediate climber and I'm planning to climb Mountain A on September 15th. What's the best route?"

[0795] "What should I do if I feel stressed while climbing?"

[0796] This invention makes it possible to improve the safety of mountain climbing plans, monitor risks in real time during climbing, and provide appropriate support based on emotional states. In addition, by efficiently organizing records after climbing, it is possible to use the information to plan the next climbing trip.

[0797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0798] Step 1:

[0799] Terminal: Provides an input interface for mountain climbing plan information. The user inputs the starting point, destination, planned date and time, and mountain climbing experience into the interface.

[0800] Input: User inputs starting point, destination, planned date and time, and climbing experience.

[0801] Specific behavior: The user enters information into the interface and presses the submit button.

[0802] Output: The entered mountain climbing plan information is sent to the server.

[0803] Step 2:

[0804] Server: Receives mountain climbing plan information and collects necessary data (weather data, terrain data, past accident data).

[0805] Input: Received climbing plan information.

[0806] Specific operation: The server sends a request to the weather service API to obtain weather data, collects topographical data from the GIS, and obtains past accident data from the mountaineering accident database.

[0807] Output: Acquired weather data, topographical data, and past accident data.

[0808] Step 3:

[0809] Server: Analyzes collected data and performs risk assessment.

[0810] Input: Weather data, terrain data, historical accident data.

[0811] Specific operation: The server analyzes meteorological data to identify weather patterns, analyzes topographical data to evaluate the elevation difference and ground surface condition of the route, and analyzes past accident data to extract risk factors. These are then integrated and used by an AI algorithm to perform a risk assessment.

[0812] Output: Risk assessment results (risk points, risk level).

[0813] Step 4:

[0814] Server: Generates the optimal climbing route based on the risk assessment results.

[0815] Input: Risk assessment results.

[0816] How it works: The server uses an AI algorithm to generate multiple climbing routes, evaluate the safety of each route, and select the safest one. Route information includes distance, elevation gain, estimated time, etc.

[0817] Output: Generated optimal climbing route information.

[0818] Step 5:

[0819] Server: Sends optimal route information and risk warning information to the device.

[0820] Input: Generated optimal climbing route information.

[0821] Specific operation: The server sends route information and details of risk warning areas to the terminal.

[0822] Output: Information on the best climbing route and risky areas is sent to the device.

[0823] Step 6:

[0824] Terminal: Displays optimal route information and risk warning areas on a map.

[0825] Input: Information on optimal climbing routes and risk warning points sent from the server.

[0826] Specific operation: The device displays route lines and markers for risk warning areas on a map, allowing users to plan safe mountain climbing.

[0827] Output: A visual representation of the climbing route and risk warnings to the user.

[0828] Step 7:

[0829] Server: Monitors the user's location in real time while climbing and notifies them of sudden changes in weather and other risks.

[0830] Input: User location, real-time weather data, and other risk information.

[0831] Specific operation: Regularly acquires GPS data and receives the latest weather information in real time from the weather service API. If a risk is detected, it prepares to notify the user immediately.

[0832] Output: Real-time notifications (emergency alerts and warnings).

[0833] Step 8:

[0834] Terminal: Displays emergency alerts and warnings in real time.

[0835] Input: Notification from the server.

[0836] Specific behavior: The device will immediately display the received notification on the screen and alert the user with sound and vibration.

[0837] Output: Visual and audible alerts and reminders provided to the user.

[0838] Step 9:

[0839] Server: After the climb is completed, the server receives feedback from the user and organizes the climbing record.

[0840] Input: User feedback, data collected during the climb.

[0841] Specific operation: The server analyzes the collected data and organizes the climbing route history, weather changes, risk warning points, etc. It then generates a detailed report including reviews and points for improvement.

[0842] Output: The generated climbing report.

[0843] Step 10:

[0844] Terminal: Displays the generated report to the user.

[0845] Input: Climbing report sent from the server.

[0846] What it does: The device displays a visual report to help users plan their next hike.

[0847] Output: A detailed climb report that is displayed to the user.

[0848] Step 11:

[0849] Terminal: Provides a data collection interface for the emotion engine.

[0850] Input: Data such as the user's facial expressions, voice, and heart rate.

[0851] How it works: The emotion engine uses a facial recognition camera, a voice analysis microphone, and a heart rate sensor to collect user emotional data.

[0852] Output: The collected emotion data is sent to the server.

[0853] Step 12:

[0854] Server: Analyzes the collected emotion data.

[0855] Input: Collected emotion data.

[0856] Specific operation: The server uses the emotion engine to evaluate the user's mental state in real time and reflects this in risk assessment and route optimization as necessary.

[0857] Output: Evaluation results and countermeasures based on the user's psychological state.

[0858] Step 13:

[0859] Server: Generates advice and warnings based on emotion data and sends them to the device.

[0860] Input: Evaluation results based on the user's mental state.

[0861] Specific behavior: The server generates appropriate advice and reminders based on the user's emotional state. For example, it creates notifications including "Take a break" and "Breathing exercises for relaxation."

[0862] Output: Advice and warnings based on emotion data are sent to the device.

[0863] Step 14:

[0864] Device: Displays advice and warnings to users based on emotional data.

[0865] Input: Notifications based on emotion data sent from the server.

[0866] Specific operation: The device displays the received advice or warning on the screen and notifies the user by sound or vibration.

[0867] Output: Specific advice or reminders provided to the user.

[0868] (Application example 2)

[0869] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0870] Existing mountain climbing planning systems perform risk assessments using weather data, topographical data, and past accident data, but do not provide real-time safety support that takes into account the climber's mental state. In particular, it is necessary to provide appropriate advice and warnings for safe driving by taking into account the driver's emotional state and stress during long-distance drives in autonomous vehicles. The present invention aims to solve this problem and improve the safety of autonomous vehicles by introducing an emotion engine.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and performing risk assessment; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during mountain climbing; means for organizing the mountain climbing record and generating a report after the mountain climbing is completed; and means for collecting and analyzing emotional data in real time and providing appropriate advice and warnings based on the mental state. This makes it possible to realize a safe and comfortable driving plan not only for mountain climbers but also for drivers of autonomous vehicles.

[0872] "Mountain climbing plan information" is information that a mountain climber inputs detailed information such as the starting point, destination, planned date and time, and mountain climbing experience.

[0873] "Weather data" refers to data regarding weather conditions in a target area obtained using a weather service API.

[0874] "Topographic Data" means data about the surface conditions of a target area collected from a geographic information system (GIS).

[0875] "Past accident data" is information about accidents that have occurred in the past, obtained from a mountain climbing accident database.

[0876] "Risk assessment" is the process of analyzing collected weather data, topographical data, and past accident data to assess the risk level for each climbing route.

[0877] The "optimal climbing route" is the climbing route that is evaluated as the safest among multiple candidate routes based on the results of the risk assessment.

[0878] "Real-time monitoring" is a function that monitors the user's location and other data in real time while climbing, and notifies them as needed.

[0879] "Report generation" is the process of organizing the climbing record after the climb is completed and creating a detailed report that includes reflections and suggestions for improvement for the next climb.

[0880] The "Emotion Engine" is a system that collects and analyzes data such as the user's facial expressions, voice, and heart rate to evaluate their mental state.

[0881] "Emotion data" refers to data such as the user's facial expressions, voice, and heart rate collected through the emotion engine.

[0882] "Advice and warnings" refers to the provision of information based on emotional data to encourage the user to take appropriate action or take appropriate precautions.

[0883] System Overview

[0884] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing and in autonomous vehicles. This system inputs mountain climbing plan information, collects and analyzes weather data, topographical data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions. The emotion engine also has the function of providing appropriate advice and warnings based on the user's emotional state.

[0885] Hardware and software used

[0886] Hardware: In-vehicle GPS, cameras, microphones, heart rate monitors, autonomous driving control systems, wearable devices

[0887] Software: Emotion recognition engine, weather data API, terrain information API, traffic information API, AI analysis algorithm

[0888] Program processing explanation

[0889] 1. Enter operation plan information

[0890] The user inputs information about the mountain climbing or driving plan (starting point, destination, departure time, etc.) into the terminal, and the information is sent to the server.

[0891] 2. Data collection

[0892] The server collects weather data, terrain data, traffic information, and past accident data based on the input plan information. Weather data is obtained from a weather service API, terrain data from a geographic information system (GIS), and traffic information from a traffic information API. Accident data is obtained from a mountain climbing accident database and a traffic accident database.

[0893] 3. Risk assessment and route optimization

[0894] The server analyzes the collected data and performs risk assessment. In this process, an AI algorithm is used to analyze the data and calculate risk points and risk levels. The server then generates an optimal route and identifies risk areas.

[0895] 4. Notice to Users

[0896] The server sends information about the optimal route and points of caution to the device, which then displays this information on a map for the user.

[0897] 5. Real-time monitoring and notifications

[0898] The server monitors the user's location in real time while hiking or driving. If there is a sudden change in weather conditions or a traffic risk, it will quickly analyze the situation and notify the user as necessary. The emotion engine analyzes the user's heart rate, facial expressions, and voice data, and suggests taking a break if stress or fatigue increases.

[0899] 6. Organizing mountain climbing and driving records

[0900] After the hike or drive is over, the server analyzes the user's behavior and situation and generates a detailed report, which also includes a review and suggestions for improvement for the next time.

[0901] Examples and prompts

[0902] Specific examples

[0903] Suppose a user plans a drive from Tokyo to Osaka. The user inputs the starting point, destination, and departure time and sends them to the server. The server collects weather, terrain, and traffic information and performs a risk assessment. It calculates the optimal route and risk-related areas and displays this information on the in-car display. During the drive, the system monitors the driver's emotional state and suggests taking a break if stress levels are high. It also notifies the driver in real time of any sudden weather changes or traffic risks. After the drive is over, a detailed driving report is generated to help plan the next drive.

[0904] Prompt Sentence Examples

[0905] As an assistant AI, please provide appropriate advice to the driver based on the following information:

[0906] Driving plan: Tokyo to Osaka, Scheduled departure time: 8:00 AM

[0907] Driver's emotional state: High stress

[0908] advice:

[0909] "You are currently traveling from Tokyo to Osaka. You seem to be feeling stressed along the way, so we recommend you take a break at a nearby service area. Take a deep breath and relax."

[0910] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0911] Step 1:

[0912] The user inputs the hiking or driving plan information (starting point, destination, departure time, etc.) into the terminal, and the information is sent to the server. Based on this input, the server saves the plan information and prepares for the necessary data collection.

[0913] Step 2:

[0914] Based on the input plan information, the server collects weather data, terrain data, traffic information, and past accident data. The server calls the weather service API to obtain weather data, obtains terrain data using a geographic information system (GIS), and collects traffic information using a traffic information API. In addition, it obtains past accident data from a mountain climbing accident database and a traffic accident database. This allows the server to obtain sufficient auxiliary data.

[0915] Step 3:

[0916] The server performs risk assessment based on collected weather data, terrain data, traffic information, and past accident data. It analyzes the data using AI analysis algorithms. Specifically, it calculates risk points and risk levels by integrating factors such as weather patterns, terrain complexity, traffic congestion, and past accident frequency. This generates basic data for safety assessment.

[0917] Step 4:

[0918] The server generates the optimal climbing or driving route based on the risk assessment results. Using an AI algorithm, it calculates the least risky route from the collected and analyzed data and generates detailed information about it (distance, elevation difference, estimated time, etc.). This provides safe route information that users can choose from.

[0919] Step 5:

[0920] The server sends the generated optimal route and information on risk and caution areas to the device. The device displays this information on a map for the user to use. Based on this information, the user can create a safe and optimal plan.

[0921] Step 6:

[0922] The server monitors the user's location information in real time while hiking or driving. It acquires GPS data to confirm the user's current location, and performs necessary analysis and promptly notifies the user in the event of a sudden change in weather conditions or traffic risks. This allows the user to respond to risks in real time.

[0923] Step 7:

[0924] The emotion engine analyzes the user's heart rate, facial expressions, and voice data, and provides appropriate advice or suggests rest if stress or fatigue increases. The server receives this data in real time and sends a notification to the user based on the emotion engine's evaluation results. This enables safety measures that take the user's mental state into consideration.

[0925] Step 8:

[0926] After the hike or drive is over, the server analyzes the user's behavior and situation and generates a detailed report. The report provides feedback to the user, including reflections and suggestions for improvement for the next trip. The device displays this report to the user, allowing them to use it to plan their next trip.

[0927] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0928] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0929] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0930] [Third embodiment]

[0931] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0932] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0933] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0934] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0935] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0936] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0937] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0938] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0939] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0940] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0941] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0942] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0943] System Overview

[0944] This invention is a system that uses AI to support safe mountain climbing planning. The system includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and conducting risk assessments, a means for generating optimal mountain climbing routes, a means for real-time monitoring and notification, and a means for organizing mountain climbing records and generating reports. Each of these means and specific examples are described below.

[0945] Program processing

[0946] 1. Enter your climbing plan information

[0947] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.). The user inputs the necessary information and presses the send button.

[0948] User: Enters a mountain climbing plan and sends it from the device to the server.

[0949] 2. Data collection

[0950] Server: Receives climbing plan information sent by users. Based on this information, it collects weather data, topography data, and past accident data for the target area. Weather data is obtained from weather services, topography data from a geographic information system (GIS), and past accident data from a climbing accident database.

[0951] 3. Risk assessment and route optimization

[0952] Server: Analyzes collected data and performs risk assessment using AI. Risk assessment includes factors such as weather, terrain, and past accident information. Based on the assessment results, multiple routes are generated and the risk points and risk levels for each are calculated. The route with the least risk is selected and detailed route information is generated.

[0953] 4. Providing optimal routes and important points to note

[0954] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[0955] Terminal: Displays the optimal route and risk warning points on a map to provide users with information.

[0956] User: Create a safe mountain climbing plan based on the displayed information.

[0957] 5. Real-time monitoring and notifications

[0958] Server: Monitors the user's location in real time while they are climbing. If there are any changes in weather conditions or terrain information, it analyzes them promptly and sends a notification to the device.

[0959] Device: Display emergency alerts and warning notifications to the user.

[0960] 6. Organizing climbing records and generating reports

[0961] Server: After the climb is completed, the server receives feedback from users, organizes the climb record, and generates a detailed report including reflections and areas for improvement.

[0962] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[0963] Specific examples

[0964] Assume that the user is planning to climb Mountain A next Saturday.

[0965] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[0966] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[0967] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[0968] 4. The server sends the optimal route information and risk warning information to the terminal.

[0969] 5. The device displays the optimal route and risk-related areas on a map for the user.

[0970] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[0971] 7. The device displays emergency alerts and warnings to the user.

[0972] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[0973] 9. The device can display this report to the user, helping them plan their next climb.

[0974] This will enable safe and secure mountain climbing using a system that makes full use of AI.

[0975] The processing flow will be explained below.

[0976] Step 1:

[0977] The user inputs mountain climbing plan information into the input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[0978] Step 2:

[0979] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data for the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[0980] Step 3:

[0981] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[0982] Step 4:

[0983] The server then inputs the analysis results into an AI algorithm to perform a risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, the risk points and risk level for each route are calculated.

[0984] Step 5:

[0985] The server generates multiple climbing routes based on the risk assessment results. From the generated routes, it selects the route that is assessed as having the least risk and generates detailed route information. The detailed route information includes distance, elevation change, estimated time, etc.

[0986] Step 6:

[0987] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[0988] Step 7:

[0989] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[0990] Step 8:

[0991] During the climb, the user's location information is periodically updated by the device and sent to the server.

[0992] Step 9:

[0993] The server monitors the received location information in real time, and if there is a sudden change in weather conditions or other risk information, it analyzes it promptly and notifies the user of the risk information as necessary.

[0994] Step 10:

[0995] The device displays emergency alerts and warning notifications to the user in real time.

[0996] Step 11:

[0997] After the climb is completed, the server receives feedback from the user, organizes the climb record, analyzes the user's behavior and situation, and generates a detailed report that includes reflections and areas for improvement.

[0998] Step 12:

[0999] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[1000] Example 1

[1001] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1002] Mountaineering is an activity in nature, and it involves many risks, such as changes in weather and terrain, and unexpected events. It is particularly difficult for inexperienced climbers to plan a safe climb and manage the risks. There is also a lack of real-time risk notifications during the climb and feedback after the climb is complete. Furthermore, there is a need for systems that provide optimal routes based on collected data and support for enjoying mountaineering while ensuring safety.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1004] In this invention, the server includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for performing risk assessment using a generative AI model, a means for generating an optimal mountain climbing route based on the risk assessment results, a means for real-time monitoring and notification during mountain climbing, and a means for organizing mountain climbing records and generating a report after the mountain climbing is completed, thereby enabling support for users to climb mountains safely.

[1005] "Mountain climbing plan information" is information such as the starting point, destination, planned date and time, and mountain climbing experience that the user inputs in order to carry out mountain climbing activities.

[1006] "Weather data" is data that indicates the weather conditions in a specific area, and includes information on weather conditions such as temperature, precipitation, and wind speed.

[1007] "Topography data" refers to data that indicates the topography and geographical features of the target area, including information such as elevation, slope, and topography.

[1008] "Past accident data" is information about accidents and incidents that have occurred during mountain climbing in the past, including the location, cause, and detailed circumstances of the accident.

[1009] A "generative AI model" is a model that uses artificial intelligence technology to analyze large amounts of data, learn patterns, and make predictions and classifications.

[1010] "Risk assessment" is the process of assessing the risks during mountain climbing by analyzing the weather, terrain, past accident information, etc. based on the data obtained.

[1011] The "optimal climbing route" is the safest and most efficient climbing route selected based on risk assessment.

[1012] "Real-time monitoring" is the process of tracking the user's location and environmental information in real time while climbing, and constantly monitoring the latest situation.

[1013] A "notification" is a warning or caution message sent from the server to the user terminal, and is information necessary for the user to take appropriate action.

[1014] A "mountain climbing record" is a detailed record of mountain climbing activities, and includes information such as start time, finish time, walking distance, and elevation difference.

[1015] A "report" is a detailed report generated based on climbing records and feedback, and includes reflections and suggestions for improvement that will be useful in planning your next climb.

[1016] This invention relates to a system for safely supporting mountain climbing plans, and uses AI technology to perform risk assessment, propose optimal routes, perform real-time monitoring, and generate reports.

[1017] System Overview

[1018] The system includes a means for inputting climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and performing risk assessment using a generative AI model, a means for generating optimal climbing routes, a means for real-time monitoring and notification, and a means for organizing climbing records and generating reports.

[1019] Entering mountain climbing plan information

[1020] The terminal provides an interface for the user to input mountain climbing plan information. This interface includes items such as the starting point, destination, planned date and time, and climbing experience. As a specific example, if a user plans to depart from trailhead A to summit B at 6:00 a.m. on September 15th, the user can enter this information into the terminal and press the "send" button to send the information to the server.

[1021] Data collection

[1022] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data. Weather data is obtained through the weather service API (e.g., OpenWeatherMap), terrain data is obtained from a geographic information system (GIS), and past accident data is obtained from a mountain climbing accident database.

[1023] Risk assessment and route optimization

[1024] The server integrates the collected data and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated and the risk points and risk level of each route are calculated. The route with the least risk is selected and detailed route information is generated, including a route map, distance, elevation change, and risk warning points.

[1025] Providing optimal routes and points to note

[1026] The server organizes information on optimal routes and risk-related points, and sends it to the terminal. The terminal displays this information on a map for the user. The user can then create a safe mountain climbing plan based on the displayed information.

[1027] Real-time monitoring and notifications

[1028] The server periodically collects GPS data to monitor the user's location in real time while climbing. The server tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, the server promptly sends a notification to the device and displays an emergency alert or warning to the user.

[1029] Organize your climbing records and generate reports

[1030] After the climb is completed, the server receives feedback from the user and organizes the climb record. For example, a detailed report is generated by collecting data such as the start time, finish time, walking distance, and elevation gain. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via their device. The user can use this report to plan their next climb.

[1031] Specific examples of prompt sentences are as follows:

[1032] "Plan your next climb by analyzing past climb records and weather data to suggest the best route."

[1033] "I'd like to plan a climbing route to Summit A. The starting point is Trailhead B, and the planned time and date is 6:00 a.m. on September 15th. What is the best route and what are the risks and precautions I should take?"

[1034] This allows users to climb mountains safely and efficiently, and enjoy nature with peace of mind.

[1035] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1036] Step 1:

[1037] The terminal displays a dedicated interface for the user to input mountain climbing plan information. The user inputs information such as the starting point, destination, planned date and time, and mountain climbing experience into this interface. For example, the starting point may be "Trailhead A," the destination may be "Summit B," the planned date and time may be "September 15th, 6:00 AM," and the climbing experience may be "Intermediate." Once this information is input, the user presses the "Send" button to send the information to the server. The input is the mountain climbing plan information input by the user, and the output is the mountain climbing plan information sent to the server.

[1038] Step 2:

[1039] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects the necessary data. Specifically, it obtains weather data from a weather service API (e.g., OpenWeatherMap), terrain data from a geographic information system (GIS), and past accident data from a mountain climbing accident database. The input is the received mountain climbing plan information, and the output is the collected weather data, terrain data, and past accident data.

[1040] Step 3:

[1041] The server integrates collected weather data, terrain data, and past accident data, and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated, and the risk points and risk levels for each route are calculated. The route with the least risk is selected, and detailed route information is generated. The input is the collected data, and the output is the risk assessment results and optimal route information.

[1042] Step 4:

[1043] The server organizes the selected optimal route information and information on risk-warning areas and sends it to the terminal. The terminal displays the received information on a map. This allows the user to visually confirm detailed information for creating a safe mountain climbing plan. The input is the optimal route information and information on risk-warning areas, and the output is the information sent to the terminal.

[1044] Step 5:

[1045] The server periodically collects GPS data to monitor the user's location in real time while climbing. It tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, it promptly sends a notification to the user's device, which then displays it to the user. The input is the GPS data collected in real time, and the output is notification information.

[1046] Step 6:

[1047] After the climb is completed, the server receives feedback from the user, organizes the climb record, and generates a detailed report. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via a terminal. The input is the user's feedback and the climb record, and the output is the generated detailed report.

[1048] This makes it possible to support the user in climbing safely and efficiently through each step.

[1049] (Application example 1)

[1050] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Conventional navigation systems and mountain climbing planning support systems lack the functionality to comprehensively consider real-time changing weather data, road conditions, past accident data, etc. to perform risk assessments and present optimal routes. As a result, users could face unexpected risks. In addition, they lacked the functionality to generate detailed reports after a hike or drive to evaluate safety and efficiency and incorporate them into future plans. This could compromise user safety and the accuracy of their plans.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1053] In this invention, the server includes a means for inputting mountain climbing plan information or driving plan information, a means for collecting weather data, topographical data, road data, and past accident data based on the input mountain climbing plan information or driving plan information, and a means for analyzing the collected data and performing risk assessment. This makes it possible to provide optimal routes that reflect a variety of information, such as weather, road conditions, and past accident data, in real time. It also provides real-time monitoring and emergency notifications during mountain climbing or driving, improving user safety. Furthermore, a detailed report can be generated after the end of the climb or drive, which can be used to plan the next trip.

[1054] "Mountain climbing plan information" is information entered by the mountain climber, such as the starting point, destination, planned date and time, and mountain climbing experience.

[1055] "Driving plan information" is information such as the departure point, destination, scheduled date and time entered by the driver.

[1056] "Weather data" refers to information about the weather, such as the current weather, temperature, precipitation, and wind speed.

[1057] "Topographic data" refers to geographical information such as mountain and terrain features, elevation, and slope.

[1058] "Road data" refers to geographical information about roads, such as road elevation, slope, and road width.

[1059] "Past accident data" refers to historical information on traffic accidents and mountain climbing accidents that have occurred in specific areas or on specific roads.

[1060] A "means" is a tool, method, or device used to achieve a particular end.

[1061] "Risk assessment" is the process of determining the existence and extent of potential risks based on weather conditions, road conditions, past accident data, etc.

[1062] The "optimal climbing route" is the climbing route that is analyzed to have the least risk, taking into account safety and efficiency.

[1063] An "optimal driving route" is a driving route that has been analyzed to have the least risk, taking into account safety and efficiency.

[1064] "Real-time monitoring" is the process of monitoring ongoing situations in real time and collecting and analyzing data instantly.

[1065] "Notification" is the act of conveying important information or warnings to a user.

[1066] A "report" is a document that organizes records after a hike or drive and provides information in a detailed report format.

[1067] "Location information" is information that indicates a specific location, such as the coordinates of the user's current location.

[1068] A "server" is a computer system that collects, analyzes, and provides data.

[1069] This invention is a system for supporting safe mountain climbing and driving plans. Based on the planning information entered by the user, the system collects and analyzes weather data, topographical data, road data, and past accident data, performs risk assessments, and provides optimal routes in real time. After the mountain climbing or driving is completed, a detailed report is generated and the safety and efficiency are evaluated.

[1070] The program proceeds as follows:

[1071] 1. Enter planning information

[1072] Users use their smartphones or in-car infotainment systems to input planning information such as starting point, destination, planned date and time, etc. This information is then sent from the device to a server.

[1073] 2. Data collection

[1074] Based on the planning information, the server collects weather data, topographical data, road data, and past accident data. These data are obtained from weather data APIs (e.g., OpenWeather), geographic information systems (GIS), and traffic information APIs.

[1075] 3. Risk assessment and route optimization

[1076] The server analyzes the collected data using AI analysis tools such as TensorFlow and PyTorch to perform risk assessments, including weather, road conditions, terrain, and past accident information. Based on the results of this analysis, the system generates the optimal route.

[1077] 4. Providing optimal routes and risk warnings

[1078] The generated optimal route and risk information are sent from the server to the terminal, which then displays this information on a map and provides it to the user.

[1079] 5. Real-time monitoring and notifications

[1080] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, it immediately reanalyzes the data and sends warnings and alerts to the user's device, ensuring the user's safety.

[1081] 6. Record keeping and report generation

[1082] After the hike or drive is complete, the server compiles the records and generates a detailed report that provides useful information for planning your next trip.

[1083] Examples:

[1084] If the user enters "Tokyo" as the departure point, "Shizuoka" as the destination, and "October 20th 13:00" as the scheduled time and date, the following can be used as the prompt text:

[1085] "Use AI to evaluate the optimal route from Tokyo to Shizuoka and create a safe driving route."

[1086] The server collects weather data from OpenWeather, terrain data from GIS, and road and real-time traffic information from a traffic information API. Using TensorFlow, it performs risk assessments based on this data and generates optimal routes. The generated information is sent to the in-car navigation system and smartphone and displayed in real time. If there are sudden changes in weather or road conditions during hiking or driving, the server immediately reanalyzes the data and notifies the user. After completion, it organizes detailed route history and generates a report that can be used to plan the next trip.

[1087] In this way, by implementing the present invention, the user can make safe and efficient mountain climbing and driving plans.

[1088] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1089] Step 1:

[1090] A user inputs planning information, such as a starting point, destination, and planned date and time, using a smartphone or in-car infotainment system. This information is sent from the device to a server. Inputs include the starting point, destination, date and time, and user-specific information. Outputs provide this planning information to the server.

[1091] Step 2:

[1092] The server collects weather data, topographical data (or road data), and past accident data based on the received plan information. Here, data is acquired using weather data APIs (such as OpenWeather), geographic information systems (GIS), and traffic information APIs. Plan information is used as input, and weather data, topographical data, road data, and past accident data are compiled as output.

[1093] Step 3:

[1094] The server analyzes the collected data and performs risk assessment using AI analysis tools (TensorFlow and PyTorch). This process evaluates the risk level based on weather conditions, road conditions, terrain, and past accident history. The input includes various collected data, and the output generates a risk assessment result.

[1095] Step 4:

[1096] The server generates the optimal route based on the risk assessment results. In this process, an algorithm for calculating the optimal route is applied to select a safe and efficient route for the user. The input is the risk assessment result, and the output is the optimal route information.

[1097] Step 5:

[1098] The server sends the generated information about the optimal route and risk warning points to the terminal. The terminal displays this information on a map and provides it to the user. The optimal route information is the input, and the route and risk warning points displayed on the map are provided to the user as the output.

[1099] Step 6:

[1100] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, the data is reanalyzed and alerts or warnings are sent to the user. In this step, data is processed and reevaluated in real time. The input includes real-time location information and new weather and road data, and the output is emergency alerts and notifications sent to the device.

[1101] Step 7:

[1102] After the climb or drive is completed, the server organizes the records and generates a detailed report, which provides useful information for planning the next climb or drive. The input includes the user's activity history and planning information, and the output is a detailed report, which is sent to the user's device and can be viewed.

[1103] The above processing flow enables safe and efficient mountain climbing and driving plans to be realized.

[1104] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1105] System Overview

[1106] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system includes functions for inputting mountain climbing plan information, collecting and analyzing weather data, topographical data, and past accident data, risk assessment, generating optimal mountain climbing routes, real-time monitoring and notifications, organizing mountain climbing records and generating reports, and an emotion engine that recognizes the user's emotions. It also includes a function that uses the emotion engine to provide appropriate advice and warnings based on the user's emotional state. Each method and specific examples are described in detail below.

[1107] Program processing

[1108] 1. Enter your climbing plan information

[1109] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.) When the user inputs the necessary information and presses the send button, the input information is sent to the server.

[1110] User: Enters a mountain climbing plan and sends it from the device to the server.

[1111] 2. Data collection

[1112] Server: Receives mountain climbing plan information sent by the user. Based on this information, it collects weather data, topographical data, and past accident data for the target area from the necessary data sources. Weather data is obtained from the weather service API, topographical data is collected from a geographic information system (GIS), and accident data is obtained from a mountain climbing accident database.

[1113] 3. Risk assessment and route optimization

[1114] Server: Analyzes collected weather data, topographical data, and past accident data. Weather data is used to extract and analyze weather patterns and forecasts, topographical data is used to extract elevation changes and ground surface conditions along the route, and accident data is used to extract and analyze risk factors.

[1115] Server: The analysis results are fed into an AI algorithm to perform risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, risk points and risk levels for each route are calculated.

[1116] Server: Generates multiple climbing routes based on the risk assessment results. From the generated routes, selects the route assessed as having the least risk and generates detailed route information, including distance, elevation difference, estimated time, etc.

[1117] 4. Providing optimal routes and important points to note

[1118] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[1119] Device: The optimal route and risk warning points are displayed on a map to provide users with information so they can plan their safe and optimal mountain climbing trip.

[1120] User: Create a safe mountain climbing plan based on the displayed information.

[1121] 5. Real-time monitoring and notifications

[1122] Server: Monitors the user's location in real time while they are climbing. If there is a sudden change in weather conditions or other risk information, it analyzes the situation promptly and notifies the user of the risk information as necessary.

[1123] Device: Display emergency alerts and warning notifications to users in real time.

[1124] 6. Organizing climbing records and generating reports

[1125] Server: After the climb is completed, the server receives feedback from the user and organizes the climb record. It analyzes the user's behavior and situation and generates a detailed report, which also includes reflections and suggestions for improvement.

[1126] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[1127] Introducing the Emotion Engine

[1128] 7. Emotion Engine Settings

[1129] Terminal: Provides an interface for the emotion engine to collect data such as the user's facial expressions, voice, and heart rate to measure the user's emotions.

[1130] User: Emotional data is provided to the device in real time via climbing equipment or wearable devices.

[1131] 8. Collecting and analyzing emotional data

[1132] Server: Receives collected emotion data and analyzes it in real time. The emotion engine evaluates the user's mental state and reflects it in risk assessment and route optimization as necessary.

[1133] 9. Emotion-based advice and notifications

[1134] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings if the user is under a high level of mental stress or feels stressed.

[1135] Device: Providing advice and reminders to the user, such as suggestions for resting places to calm down or simple breathing exercises to help them relax.

[1136] Specific examples

[1137] Assume that the user is planning to climb Mountain A next Saturday.

[1138] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[1139] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[1140] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[1141] 4. The server sends the optimal route information and risk warning information to the terminal.

[1142] 5. The device displays the optimal route and risk-related areas on a map for the user.

[1143] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[1144] 7. The device displays emergency alerts and warnings to the user.

[1145] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[1146] 9. The device can display this report to the user, helping them plan their next climb.

[1147] The introduction of an emotion engine allows the system to monitor the user's mental state in real time and provide not only risk assessment but also psychological support. For example, if the user is feeling stressed, the system can suggest appropriate rest areas and notify them of ways to relax, providing a safe and supported climbing experience.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] The user inputs mountain climbing plan information into an input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[1151] Step 2:

[1152] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data related to the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[1153] Step 3:

[1154] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[1155] Step 4:

[1156] The server then uses an AI algorithm to perform a risk assessment based on the analysis results. This assessment process takes into consideration weather, terrain, and past accident information to calculate the risk level for each climbing route. The assessment results then confirm the risk points and their levels.

[1157] Step 5:

[1158] The server generates multiple climbing routes based on the risk assessment results, selects the route with the lowest risk from the generated routes, and generates detailed route information, including distance, elevation change, estimated time required, and other information.

[1159] Step 6:

[1160] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[1161] Step 7:

[1162] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[1163] Step 8:

[1164] Users use wearable devices or terminals to provide their own emotional data (heart rate, facial expressions, voice, etc.) to the terminals in real time.

[1165] Step 9:

[1166] The terminal collects emotion data and transmits it to the server.

[1167] Step 10:

[1168] The server analyzes the received emotional data and assesses the user's emotional state, adjusting the risk assessment accordingly if the user is experiencing high mental load or stress.

[1169] Step 11:

[1170] During the climb, the user's location information is also periodically sent from the device to the server.

[1171] Step 12:

[1172] The server monitors the user's situation based on real-time location and emotion data received, and immediately analyzes any sudden changes in weather or other risk factors and generates warnings as needed.

[1173] Step 13:

[1174] The device displays emergency alerts and reminder messages to users in real time, including advice based on their emotional state, such as "take a break to relax."

[1175] Step 14:

[1176] After the climb is complete, the server receives feedback from the user, organizes the climb record, and analyzes the user's behavior, situation, and emotional data to generate a detailed report.

[1177] Step 15:

[1178] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[1179] The above processing flow allows the user to climb mountains safely and efficiently, and by using the emotion engine, mental support is also provided at the same time.

[1180] Example 2

[1181] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1182] Mountaineering is a challenge to nature, but it also involves various risks, making it important to plan ahead and take into account weather changes, terrain conditions, and past accident data. However, comprehensively collecting and analyzing this information requires specialized knowledge and large amounts of data processing, making it difficult for average climbers. Furthermore, monitoring risks in real time during a climb and organizing records afterward are both cumbersome, and a system to address these issues is needed. Furthermore, supporting safe climbing by taking into account the climber's emotional state is also an important issue.

[1183] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1184] In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and conducting risk assessments; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during the mountain climbing; means for compiling a mountain climbing record and generating a report after the mountain climbing is completed; means for collecting and analyzing the user's emotional data; and means for providing appropriate advice and warnings based on the analyzed emotional data. This increases the safety of mountain climbing plans. It also provides real-time risk monitoring during the mountain climbing and appropriate support based on the user's emotional state, allowing for efficient record organization after the mountain climbing is completed.

[1185] "Mountain climbing plan information" refers to information necessary when climbing a mountain, and specifically includes the starting point, destination, planned date and time, mountain climbing experience, and the like.

[1186] "Weather data" refers to data that indicates weather conditions related to the climbing route or climbing journey, and specifically includes temperature, humidity, wind speed, precipitation, weather forecast, and the like.

[1187] "Topographical data" refers to data that indicates the physical characteristics of a climbing route, and specifically includes the type of ground surface, elevation difference, gradient, geographical features, and the like.

[1188] "Past accident data" refers to data related to past accidents and troubles that have occurred while climbing mountains, and specifically includes the location of the accident, the cause, the date and time of the accident, and the extent of the damage.

[1189] "Risk assessment" refers to the process of analyzing collected data and evaluating the risks and dangers associated with a climbing route, taking into account factors such as weather fluctuations, the difficulty of the terrain, and past accident data.

[1190] A "climbing route" is a path from a starting point to a destination, usually depicted on a map. The optimal climbing route is selected based on a risk assessment.

[1191] "Real-time monitoring" refers to the process of monitoring a user's location and environmental conditions in real time while climbing, including the continuous collection and analysis of GPS and weather data.

[1192] "Notification" refers to the process of providing information to users during the climbing or planning stage, specifically including emergency alerts, risk warnings, advice, etc.

[1193] A "mountain climbing record" is a record of the progress and results of mountain climbing activities, and specifically includes the mountain climbing route, activity history, environmental conditions, user feedback, and the like.

[1194] A "report" is a document that compiles collected and organized climbing records, and specifically includes a review of the climb and areas for improvement for the next time.

[1195] "Emotion data" is data that indicates the user's psychological state and emotions, and specifically includes facial expressions, voice, heart rate, and the like.

[1196] "Advice" refers to advice and guidance provided to users during the climbing or planning stages, and specifically includes suggestions for safe climbing and ways to reduce stress.

[1197] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system inputs mountain climbing plan information, collects and analyzes weather data, terrain data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions.

[1198] Hardware and Software Used

[1199] The system uses the following hardware and software:

[1200] Terminal: A device that acts as a user interface, such as a smartphone or tablet.

[1201] Server: Data collection, analysis, route generation, monitoring, and report generation are performed using a high-performance cloud server.

[1202] Weather Service API: Used to retrieve weather data.

[1203] Geographic Information Systems (GIS): Used to collect topographical data.

[1204] Mountaineering accident database: Used to obtain past accident data.

[1205] Emotion engine: Analyzes the user's emotional data and provides appropriate advice and warnings.

[1206] Generative AI models: Used for risk assessment and optimal route generation.

[1207] Explanation of program processing

[1208] 1. Enter your climbing plan information

[1209] Terminal: Provides an interface for users to input their starting point, destination, planned date and time, climbing experience, etc. When the user inputs the information and presses the send button, the information is sent to the server.

[1210] User: Enters the climbing plan into the device interface and presses the send button.

[1211] 2. Data collection

[1212] Server: Based on the received mountain climbing plan information, it collects weather data, terrain data, and past accident data. Weather data is collected from the weather service API, terrain data from GIS, and accident data from the mountain climbing accident database.

[1213] 3. Risk assessment and route optimization

[1214] Server: Analyzes the collected data and performs risk assessments using AI algorithms based on weather data, topographical data, and past accident data.

[1215] Server: Generates optimal climbing routes based on risk assessment. Route information includes distance, elevation gain, estimated time, etc.

[1216] 4. Providing optimal routes and important points to note

[1217] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[1218] Terminal: The received information is displayed on a map, allowing users to create a safe and optimal mountain climbing plan.

[1219] 5. Real-time monitoring and notifications

[1220] Server: Monitors the user's location in real time while climbing, and notifies them of sudden changes in weather and risks.

[1221] Terminal: Displays emergency alerts and warnings in real time.

[1222] 6. Organizing climbing records and generating reports

[1223] Server: Receives user feedback after the climb, organizes the climbing record, generates detailed reports, and provides them to users.

[1224] Terminal: The generated report can be displayed to the user to help them plan their next mountain climbing trip.

[1225] 7. Emotion Engine Configuration and Analysis

[1226] Terminal: Provides a data collection interface for the emotion engine, including a camera for recognizing the user's facial expressions, a microphone for analyzing voice, and a heart rate sensor.

[1227] User: Provides real-time emotional data through climbing equipment and wearable devices.

[1228] Server: Analyzes the collected emotional data, evaluates the user's mental state, and reflects this in risk assessment and route optimization as necessary.

[1229] 8. Emotion-based advice and notifications

[1230] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings when the user is under high mental stress or feels stressed.

[1231] Device: Displays specific advice and reminders to the user, such as "Take a break" or "Breathing exercises to relax."

[1232] Specific examples

[1233] Assume that the user is planning to climb Mountain A next Saturday.

[1234] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[1235] 2. The server collects surrounding weather data, topographical data, and past accident data, and organizes it specifically for Mountain A.

[1236] 3. The server performs a risk assessment, selects the safest route, and generates detailed information about it.

[1237] 4. The server sends the optimal route information and risk warning information to the terminal.

[1238] 5. The device displays the optimal route and risk warning points on a map for the user.

[1239] 6. During the climb, the server monitors the user's location in real time and notifies them of any sudden changes in the weather.

[1240] 7. The device displays emergency alerts and warnings.

[1241] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report that users can use to plan their next climb.

[1242] 9. The device collects emotional data using a facial recognition camera and heart rate sensor. The server analyzes the emotional data in real time and generates appropriate advice if stress is detected. The device then displays suggestions for reducing stress.

[1243] Prompt Sentence Examples

[1244] "Please tell me what climbing routes are safe."

[1245] "I'm an intermediate climber and I'm planning to climb Mountain A on September 15th. What's the best route?"

[1246] "What should I do if I feel stressed while climbing?"

[1247] This invention makes it possible to improve the safety of mountain climbing plans, monitor risks in real time during climbing, and provide appropriate support based on emotional states. In addition, by efficiently organizing records after climbing, it is possible to use the information to plan the next climbing trip.

[1248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1249] Step 1:

[1250] Terminal: Provides an input interface for mountain climbing plan information. The user inputs the starting point, destination, planned date and time, and mountain climbing experience into the interface.

[1251] Input: User inputs starting point, destination, planned date and time, and climbing experience.

[1252] Specific behavior: The user enters information into the interface and presses the submit button.

[1253] Output: The entered mountain climbing plan information is sent to the server.

[1254] Step 2:

[1255] Server: Receives mountain climbing plan information and collects necessary data (weather data, terrain data, past accident data).

[1256] Input: Received climbing plan information.

[1257] Specific operation: The server sends a request to the weather service API to obtain weather data, collects topographical data from the GIS, and obtains past accident data from the mountaineering accident database.

[1258] Output: Acquired weather data, topographical data, and past accident data.

[1259] Step 3:

[1260] Server: Analyzes collected data and performs risk assessment.

[1261] Input: Weather data, terrain data, historical accident data.

[1262] Specific operation: The server analyzes meteorological data to identify weather patterns, analyzes topographical data to evaluate the elevation difference and ground surface condition of the route, and analyzes past accident data to extract risk factors. These are then integrated and used by an AI algorithm to perform a risk assessment.

[1263] Output: Risk assessment results (risk points, risk level).

[1264] Step 4:

[1265] Server: Generates the optimal climbing route based on the risk assessment results.

[1266] Input: Risk assessment results.

[1267] How it works: The server uses an AI algorithm to generate multiple climbing routes, evaluate the safety of each route, and select the safest one. Route information includes distance, elevation gain, estimated time, etc.

[1268] Output: Generated optimal climbing route information.

[1269] Step 5:

[1270] Server: Sends optimal route information and risk warning information to the device.

[1271] Input: Generated optimal climbing route information.

[1272] Specific operation: The server sends route information and details of risk warning areas to the terminal.

[1273] Output: Information on the best climbing route and risky areas is sent to the device.

[1274] Step 6:

[1275] Terminal: Displays optimal route information and risk warning areas on a map.

[1276] Input: Information on optimal climbing routes and risk warning points sent from the server.

[1277] Specific operation: The device displays route lines and markers for risk warning areas on a map, allowing users to plan safe mountain climbing.

[1278] Output: A visual representation of the climbing route and risk warnings to the user.

[1279] Step 7:

[1280] Server: Monitors the user's location in real time while climbing and notifies them of sudden changes in weather and other risks.

[1281] Input: User location, real-time weather data, and other risk information.

[1282] Specific operation: Regularly acquires GPS data and receives the latest weather information in real time from the weather service API. If a risk is detected, it prepares to notify the user immediately.

[1283] Output: Real-time notifications (emergency alerts and warnings).

[1284] Step 8:

[1285] Terminal: Displays emergency alerts and warnings in real time.

[1286] Input: Notification from the server.

[1287] Specific behavior: The device will immediately display the received notification on the screen and alert the user with sound and vibration.

[1288] Output: Visual and audible alerts and reminders provided to the user.

[1289] Step 9:

[1290] Server: After the climb is completed, the server receives feedback from the user and organizes the climbing record.

[1291] Input: User feedback, data collected during the climb.

[1292] Specific operation: The server analyzes the collected data and organizes the climbing route history, weather changes, risk warning points, etc. It then generates a detailed report including reviews and points for improvement.

[1293] Output: The generated climbing report.

[1294] Step 10:

[1295] Terminal: Displays the generated report to the user.

[1296] Input: Climbing report sent from the server.

[1297] What it does: The device displays a visual report to help users plan their next hike.

[1298] Output: A detailed climb report that is displayed to the user.

[1299] Step 11:

[1300] Terminal: Provides a data collection interface for the emotion engine.

[1301] Input: Data such as the user's facial expressions, voice, and heart rate.

[1302] How it works: The emotion engine uses a facial recognition camera, a voice analysis microphone, and a heart rate sensor to collect user emotional data.

[1303] Output: The collected emotion data is sent to the server.

[1304] Step 12:

[1305] Server: Analyzes the collected emotion data.

[1306] Input: Collected emotion data.

[1307] Specific operation: The server uses the emotion engine to evaluate the user's mental state in real time and reflects this in risk assessment and route optimization as necessary.

[1308] Output: Evaluation results and countermeasures based on the user's psychological state.

[1309] Step 13:

[1310] Server: Generates advice and warnings based on emotion data and sends them to the device.

[1311] Input: Evaluation results based on the user's mental state.

[1312] Specific behavior: The server generates appropriate advice and reminders based on the user's emotional state. For example, it creates notifications including "Take a break" and "Breathing exercises for relaxation."

[1313] Output: Advice and warnings based on emotion data are sent to the device.

[1314] Step 14:

[1315] Device: Displays advice and warnings to users based on emotional data.

[1316] Input: Notifications based on emotion data sent from the server.

[1317] Specific operation: The device displays the received advice or warning on the screen and notifies the user by sound or vibration.

[1318] Output: Specific advice or reminders provided to the user.

[1319] (Application example 2)

[1320] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1321] Existing mountain climbing planning systems perform risk assessments using weather data, topographical data, and past accident data, but do not provide real-time safety support that takes into account the climber's mental state. In particular, it is necessary to provide appropriate advice and warnings for safe driving by taking into account the driver's emotional state and stress during long-distance drives in autonomous vehicles. The present invention aims to solve this problem and improve the safety of autonomous vehicles by introducing an emotion engine.

[1322] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and performing risk assessment; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during mountain climbing; means for organizing the mountain climbing record and generating a report after the mountain climbing is completed; and means for collecting and analyzing emotional data in real time and providing appropriate advice and warnings based on the mental state. This makes it possible to realize a safe and comfortable driving plan not only for mountain climbers but also for drivers of autonomous vehicles.

[1323] "Mountain climbing plan information" is information that a mountain climber inputs detailed information such as the starting point, destination, planned date and time, and mountain climbing experience.

[1324] "Weather data" refers to data regarding weather conditions in a target area obtained using a weather service API.

[1325] "Topographic Data" means data about the surface conditions of a target area collected from a geographic information system (GIS).

[1326] "Past accident data" is information about accidents that have occurred in the past, obtained from a mountain climbing accident database.

[1327] "Risk assessment" is the process of analyzing collected weather data, topographical data, and past accident data to assess the risk level for each climbing route.

[1328] The "optimal climbing route" is the climbing route that is evaluated as the safest among multiple candidate routes based on the results of the risk assessment.

[1329] "Real-time monitoring" is a function that monitors the user's location and other data in real time while climbing, and notifies them as needed.

[1330] "Report generation" is the process of organizing the climbing record after the climb is completed and creating a detailed report that includes reflections and suggestions for improvement for the next climb.

[1331] The "Emotion Engine" is a system that collects and analyzes data such as the user's facial expressions, voice, and heart rate to evaluate their mental state.

[1332] "Emotion data" refers to data such as the user's facial expressions, voice, and heart rate collected through the emotion engine.

[1333] "Advice and warnings" refers to the provision of information based on emotional data to encourage the user to take appropriate action or take appropriate precautions.

[1334] System Overview

[1335] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing and in autonomous vehicles. This system inputs mountain climbing plan information, collects and analyzes weather data, topographical data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions. The emotion engine also has the function of providing appropriate advice and warnings based on the user's emotional state.

[1336] Hardware and software used

[1337] Hardware: In-vehicle GPS, cameras, microphones, heart rate monitors, autonomous driving control systems, wearable devices

[1338] Software: Emotion recognition engine, weather data API, terrain information API, traffic information API, AI analysis algorithm

[1339] Program processing explanation

[1340] 1. Enter operation plan information

[1341] The user inputs information about the mountain climbing or driving plan (starting point, destination, departure time, etc.) into the terminal, and the information is sent to the server.

[1342] 2. Data collection

[1343] The server collects weather data, terrain data, traffic information, and past accident data based on the input plan information. Weather data is obtained from a weather service API, terrain data from a geographic information system (GIS), and traffic information from a traffic information API. Accident data is obtained from a mountain climbing accident database and a traffic accident database.

[1344] 3. Risk assessment and route optimization

[1345] The server analyzes the collected data and performs risk assessment. In this process, an AI algorithm is used to analyze the data and calculate risk points and risk levels. The server then generates an optimal route and identifies risk areas.

[1346] 4. Notice to Users

[1347] The server sends information about the optimal route and points of caution to the device, which then displays this information on a map for the user.

[1348] 5. Real-time monitoring and notifications

[1349] The server monitors the user's location in real time while hiking or driving. If there is a sudden change in weather conditions or a traffic risk, it will quickly analyze the situation and notify the user as necessary. The emotion engine analyzes the user's heart rate, facial expressions, and voice data, and suggests taking a break if stress or fatigue increases.

[1350] 6. Organizing mountain climbing and driving records

[1351] After the hike or drive is over, the server analyzes the user's behavior and situation and generates a detailed report, which also includes a review and suggestions for improvement for the next time.

[1352] Examples and prompts

[1353] Specific examples

[1354] Suppose a user plans a drive from Tokyo to Osaka. The user inputs the starting point, destination, and departure time and sends them to the server. The server collects weather, terrain, and traffic information and performs a risk assessment. It calculates the optimal route and risk-related areas and displays this information on the in-car display. During the drive, the system monitors the driver's emotional state and suggests taking a break if stress levels are high. It also notifies the driver in real time of any sudden weather changes or traffic risks. After the drive is over, a detailed driving report is generated to help plan the next drive.

[1355] Prompt Sentence Examples

[1356] As an assistant AI, please provide appropriate advice to the driver based on the following information:

[1357] Driving plan: Tokyo to Osaka, Scheduled departure time: 8:00 AM

[1358] Driver's emotional state: High stress

[1359] advice:

[1360] "You are currently traveling from Tokyo to Osaka. You seem to be feeling stressed along the way, so we recommend you take a break at a nearby service area. Take a deep breath and relax."

[1361] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1362] Step 1:

[1363] The user inputs the hiking or driving plan information (starting point, destination, departure time, etc.) into the terminal, and the information is sent to the server. Based on this input, the server saves the plan information and prepares for the necessary data collection.

[1364] Step 2:

[1365] Based on the input plan information, the server collects weather data, terrain data, traffic information, and past accident data. The server calls the weather service API to obtain weather data, obtains terrain data using a geographic information system (GIS), and collects traffic information using a traffic information API. In addition, it obtains past accident data from a mountain climbing accident database and a traffic accident database. This allows the server to obtain sufficient auxiliary data.

[1366] Step 3:

[1367] The server performs risk assessment based on collected weather data, terrain data, traffic information, and past accident data. It analyzes the data using AI analysis algorithms. Specifically, it calculates risk points and risk levels by integrating factors such as weather patterns, terrain complexity, traffic congestion, and past accident frequency. This generates basic data for safety assessment.

[1368] Step 4:

[1369] The server generates the optimal climbing or driving route based on the risk assessment results. Using an AI algorithm, it calculates the least risky route from the collected and analyzed data and generates detailed information about it (distance, elevation difference, estimated time, etc.). This provides safe route information that users can choose from.

[1370] Step 5:

[1371] The server sends the generated optimal route and information on risk and caution areas to the device. The device displays this information on a map for the user to use. Based on this information, the user can create a safe and optimal plan.

[1372] Step 6:

[1373] The server monitors the user's location information in real time while hiking or driving. It acquires GPS data to confirm the user's current location, and performs necessary analysis and promptly notifies the user in the event of a sudden change in weather conditions or traffic risks. This allows the user to respond to risks in real time.

[1374] Step 7:

[1375] The emotion engine analyzes the user's heart rate, facial expressions, and voice data, and provides appropriate advice or suggests rest if stress or fatigue increases. The server receives this data in real time and sends a notification to the user based on the emotion engine's evaluation results. This enables safety measures that take the user's mental state into consideration.

[1376] Step 8:

[1377] After the hike or drive is over, the server analyzes the user's behavior and situation and generates a detailed report. The report provides feedback to the user, including reflections and suggestions for improvement for the next trip. The device displays this report to the user, allowing them to use it to plan their next trip.

[1378] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1379] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1380] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1381] [Fourth embodiment]

[1382] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1383] 7, a 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.

[1384] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1385] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1386] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1388] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1389] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1390] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1391] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1392] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1393] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1394] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1395] System Overview

[1396] This invention is a system that uses AI to support safe mountain climbing planning. The system includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and conducting risk assessments, a means for generating optimal mountain climbing routes, a means for real-time monitoring and notification, and a means for organizing mountain climbing records and generating reports. Each of these means and specific examples are described below.

[1397] Program processing

[1398] 1. Enter your climbing plan information

[1399] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.). The user inputs the necessary information and presses the send button.

[1400] User: Enters a mountain climbing plan and sends it from the device to the server.

[1401] 2. Data collection

[1402] Server: Receives climbing plan information sent by users. Based on this information, it collects weather data, topography data, and past accident data for the target area. Weather data is obtained from weather services, topography data from a geographic information system (GIS), and past accident data from a climbing accident database.

[1403] 3. Risk assessment and route optimization

[1404] Server: Analyzes collected data and performs risk assessment using AI. Risk assessment includes factors such as weather, terrain, and past accident information. Based on the assessment results, multiple routes are generated and the risk points and risk levels for each are calculated. The route with the least risk is selected and detailed route information is generated.

[1405] 4. Providing optimal routes and important points to note

[1406] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[1407] Terminal: Displays the optimal route and risk warning points on a map to provide users with information.

[1408] User: Create a safe mountain climbing plan based on the displayed information.

[1409] 5. Real-time monitoring and notifications

[1410] Server: Monitors the user's location in real time while they are climbing. If there are any changes in weather conditions or terrain information, it analyzes them promptly and sends a notification to the device.

[1411] Device: Display emergency alerts and warning notifications to the user.

[1412] 6. Organizing climbing records and generating reports

[1413] Server: After the climb is completed, the server receives feedback from users, organizes the climb record, and generates a detailed report including reflections and areas for improvement.

[1414] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[1415] Specific examples

[1416] Assume that the user is planning to climb Mountain A next Saturday.

[1417] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[1418] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[1419] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[1420] 4. The server sends the optimal route information and risk warning information to the terminal.

[1421] 5. The device displays the optimal route and risk-related areas on a map for the user.

[1422] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[1423] 7. The device displays emergency alerts and warnings to the user.

[1424] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[1425] 9. The device can display this report to the user, helping them plan their next climb.

[1426] This will enable safe and secure mountain climbing using a system that makes full use of AI.

[1427] The processing flow will be explained below.

[1428] Step 1:

[1429] The user inputs mountain climbing plan information into the input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[1430] Step 2:

[1431] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data for the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[1432] Step 3:

[1433] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[1434] Step 4:

[1435] The server then inputs the analysis results into an AI algorithm to perform a risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, the risk points and risk level for each route are calculated.

[1436] Step 5:

[1437] The server generates multiple climbing routes based on the risk assessment results. From the generated routes, it selects the route that is assessed as having the least risk and generates detailed route information. The detailed route information includes distance, elevation change, estimated time, etc.

[1438] Step 6:

[1439] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[1440] Step 7:

[1441] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[1442] Step 8:

[1443] During the climb, the user's location information is periodically updated by the device and sent to the server.

[1444] Step 9:

[1445] The server monitors the received location information in real time, and if there is a sudden change in weather conditions or other risk information, it analyzes it promptly and notifies the user of the risk information as necessary.

[1446] Step 10:

[1447] The device displays emergency alerts and warning notifications to the user in real time.

[1448] Step 11:

[1449] After the climb is completed, the server receives feedback from the user, organizes the climb record, analyzes the user's behavior and situation, and generates a detailed report that includes reflections and areas for improvement.

[1450] Step 12:

[1451] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[1452] Example 1

[1453] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1454] Mountaineering is an activity in nature, and it involves many risks, such as changes in weather and terrain, and unexpected events. It is particularly difficult for inexperienced climbers to plan a safe climb and manage the risks. There is also a lack of real-time risk notifications during the climb and feedback after the climb is complete. Furthermore, there is a need for systems that provide optimal routes based on collected data and support for enjoying mountaineering while ensuring safety.

[1455] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1456] In this invention, the server includes a means for inputting mountain climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for performing risk assessment using a generative AI model, a means for generating an optimal mountain climbing route based on the risk assessment results, a means for real-time monitoring and notification during mountain climbing, and a means for organizing mountain climbing records and generating a report after the mountain climbing is completed, thereby enabling support for users to climb mountains safely.

[1457] "Mountain climbing plan information" is information such as the starting point, destination, planned date and time, and mountain climbing experience that the user inputs in order to carry out mountain climbing activities.

[1458] "Weather data" is data that indicates the weather conditions in a specific area, and includes information on weather conditions such as temperature, precipitation, and wind speed.

[1459] "Topography data" refers to data that indicates the topography and geographical features of the target area, including information such as elevation, slope, and topography.

[1460] "Past accident data" is information about accidents and incidents that have occurred during mountain climbing in the past, including the location, cause, and detailed circumstances of the accident.

[1461] A "generative AI model" is a model that uses artificial intelligence technology to analyze large amounts of data, learn patterns, and make predictions and classifications.

[1462] "Risk assessment" is the process of assessing the risks during mountain climbing by analyzing the weather, terrain, past accident information, etc. based on the data obtained.

[1463] The "optimal climbing route" is the safest and most efficient climbing route selected based on risk assessment.

[1464] "Real-time monitoring" is the process of tracking the user's location and environmental information in real time while climbing, and constantly monitoring the latest situation.

[1465] A "notification" is a warning or caution message sent from the server to the user terminal, and is information necessary for the user to take appropriate action.

[1466] A "mountain climbing record" is a detailed record of mountain climbing activities, and includes information such as start time, finish time, walking distance, and elevation difference.

[1467] A "report" is a detailed report generated based on climbing records and feedback, and includes reflections and suggestions for improvement that will be useful in planning your next climb.

[1468] This invention relates to a system for safely supporting mountain climbing plans, and uses AI technology to perform risk assessment, propose optimal routes, perform real-time monitoring, and generate reports.

[1469] System Overview

[1470] The system includes a means for inputting climbing plan information, a means for collecting weather data, topographical data, and past accident data, a means for analyzing this data and performing risk assessment using a generative AI model, a means for generating optimal climbing routes, a means for real-time monitoring and notification, and a means for organizing climbing records and generating reports.

[1471] Entering mountain climbing plan information

[1472] The terminal provides an interface for the user to input mountain climbing plan information. This interface includes items such as the starting point, destination, planned date and time, and climbing experience. As a specific example, if a user plans to depart from trailhead A to summit B at 6:00 a.m. on September 15th, the user can enter this information into the terminal and press the "send" button to send the information to the server.

[1473] Data collection

[1474] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data. Weather data is obtained through the weather service API (e.g., OpenWeatherMap), terrain data is obtained from a geographic information system (GIS), and past accident data is obtained from a mountain climbing accident database.

[1475] Risk assessment and route optimization

[1476] The server integrates the collected data and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated and the risk points and risk level of each route are calculated. The route with the least risk is selected and detailed route information is generated, including a route map, distance, elevation change, and risk warning points.

[1477] Providing optimal routes and points to note

[1478] The server organizes information on optimal routes and risk-related points, and sends it to the terminal. The terminal displays this information on a map for the user. The user can then create a safe mountain climbing plan based on the displayed information.

[1479] Real-time monitoring and notifications

[1480] The server periodically collects GPS data to monitor the user's location in real time while climbing. The server tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, the server promptly sends a notification to the device and displays an emergency alert or warning to the user.

[1481] Organize your climbing records and generate reports

[1482] After the climb is completed, the server receives feedback from the user and organizes the climb record. For example, a detailed report is generated by collecting data such as the start time, finish time, walking distance, and elevation gain. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via their device. The user can use this report to plan their next climb.

[1483] Specific examples of prompt sentences are as follows:

[1484] "Plan your next climb by analyzing past climb records and weather data to suggest the best route."

[1485] "I'd like to plan a climbing route to Summit A. The starting point is Trailhead B, and the planned time and date is 6:00 a.m. on September 15th. What is the best route and what are the risks and precautions I should take?"

[1486] This allows users to climb mountains safely and efficiently, and enjoy nature with peace of mind.

[1487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1488] Step 1:

[1489] The terminal displays a dedicated interface for the user to input mountain climbing plan information. The user inputs information such as the starting point, destination, planned date and time, and mountain climbing experience into this interface. For example, the starting point may be "Trailhead A," the destination may be "Summit B," the planned date and time may be "September 15th, 6:00 AM," and the climbing experience may be "Intermediate." Once this information is input, the user presses the "Send" button to send the information to the server. The input is the mountain climbing plan information input by the user, and the output is the mountain climbing plan information sent to the server.

[1490] Step 2:

[1491] The server receives the mountain climbing plan information sent by the user. Based on this information, it collects the necessary data. Specifically, it obtains weather data from a weather service API (e.g., OpenWeatherMap), terrain data from a geographic information system (GIS), and past accident data from a mountain climbing accident database. The input is the received mountain climbing plan information, and the output is the collected weather data, terrain data, and past accident data.

[1492] Step 3:

[1493] The server integrates collected weather data, terrain data, and past accident data, and performs a risk assessment using a generative AI model. This risk assessment includes weather conditions, terrain characteristics, and past accident information. Based on the assessment results, multiple climbing routes are generated, and the risk points and risk levels for each route are calculated. The route with the least risk is selected, and detailed route information is generated. The input is the collected data, and the output is the risk assessment results and optimal route information.

[1494] Step 4:

[1495] The server organizes the selected optimal route information and information on risk-warning areas and sends it to the terminal. The terminal displays the received information on a map. This allows the user to visually confirm detailed information for creating a safe mountain climbing plan. The input is the optimal route information and information on risk-warning areas, and the output is the information sent to the terminal.

[1496] Step 5:

[1497] The server periodically collects GPS data to monitor the user's location in real time while climbing. It tracks the user's current location based on the collected GPS data and analyzes changes in weather and terrain information. If a serious risk is identified, it promptly sends a notification to the user's device, which then displays it to the user. The input is the GPS data collected in real time, and the output is notification information.

[1498] Step 6:

[1499] After the climb is completed, the server receives feedback from the user, organizes the climb record, and generates a detailed report. This report includes a review of the climb and suggestions for improvement for the next climb, and is provided to the user via a terminal. The input is the user's feedback and the climb record, and the output is the generated detailed report.

[1500] This makes it possible to support the user in climbing safely and efficiently through each step.

[1501] (Application example 1)

[1502] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1503] Conventional navigation systems and mountain climbing planning support systems lack the functionality to comprehensively consider real-time changing weather data, road conditions, past accident data, etc. to perform risk assessments and present optimal routes. As a result, users could face unexpected risks. In addition, they lacked the functionality to generate detailed reports after a hike or drive to evaluate safety and efficiency and incorporate them into future plans. This could compromise user safety and the accuracy of their plans.

[1504] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1505] In this invention, the server includes a means for inputting mountain climbing plan information or driving plan information, a means for collecting weather data, topographical data, road data, and past accident data based on the input mountain climbing plan information or driving plan information, and a means for analyzing the collected data and performing risk assessment. This makes it possible to provide optimal routes that reflect a variety of information, such as weather, road conditions, and past accident data, in real time. It also provides real-time monitoring and emergency notifications during mountain climbing or driving, improving user safety. Furthermore, a detailed report can be generated after the end of the climb or drive, which can be used to plan the next trip.

[1506] "Mountain climbing plan information" is information entered by the mountain climber, such as the starting point, destination, planned date and time, and mountain climbing experience.

[1507] "Driving plan information" is information such as the departure point, destination, scheduled date and time entered by the driver.

[1508] "Weather data" refers to information about the weather, such as the current weather, temperature, precipitation, and wind speed.

[1509] "Topographic data" refers to geographical information such as mountain and terrain features, elevation, and slope.

[1510] "Road data" refers to geographical information about roads, such as road elevation, slope, and road width.

[1511] "Past accident data" refers to historical information on traffic accidents and mountain climbing accidents that have occurred in specific areas or on specific roads.

[1512] A "means" is a tool, method, or device used to achieve a particular end.

[1513] "Risk assessment" is the process of determining the existence and extent of potential risks based on weather conditions, road conditions, past accident data, etc.

[1514] The "optimal climbing route" is the climbing route that is analyzed to have the least risk, taking into account safety and efficiency.

[1515] An "optimal driving route" is a driving route that has been analyzed to have the least risk, taking into account safety and efficiency.

[1516] "Real-time monitoring" is the process of monitoring ongoing situations in real time and collecting and analyzing data instantly.

[1517] "Notification" is the act of conveying important information or warnings to a user.

[1518] A "report" is a document that organizes records after a hike or drive and provides information in a detailed report format.

[1519] "Location information" is information that indicates a specific location, such as the coordinates of the user's current location.

[1520] A "server" is a computer system that collects, analyzes, and provides data.

[1521] This invention is a system for supporting safe mountain climbing and driving plans. Based on the planning information entered by the user, the system collects and analyzes weather data, topographical data, road data, and past accident data, performs risk assessments, and provides optimal routes in real time. After the mountain climbing or driving is completed, a detailed report is generated and the safety and efficiency are evaluated.

[1522] The program proceeds as follows:

[1523] 1. Enter planning information

[1524] Users use their smartphones or in-car infotainment systems to input planning information such as starting point, destination, planned date and time, etc. This information is then sent from the device to a server.

[1525] 2. Data collection

[1526] Based on the planning information, the server collects weather data, topographical data, road data, and past accident data. These data are obtained from weather data APIs (e.g., OpenWeather), geographic information systems (GIS), and traffic information APIs.

[1527] 3. Risk assessment and route optimization

[1528] The server analyzes the collected data using AI analysis tools such as TensorFlow and PyTorch to perform risk assessments, including weather, road conditions, terrain, and past accident information. Based on the results of this analysis, the system generates the optimal route.

[1529] 4. Providing optimal routes and risk warnings

[1530] The generated optimal route and risk information are sent from the server to the terminal, which then displays this information on a map and provides it to the user.

[1531] 5. Real-time monitoring and notifications

[1532] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, it immediately reanalyzes the data and sends warnings and alerts to the user's device, ensuring the user's safety.

[1533] 6. Record keeping and report generation

[1534] After the hike or drive is complete, the server compiles the records and generates a detailed report that provides useful information for planning your next trip.

[1535] Examples:

[1536] If the user enters "Tokyo" as the departure point, "Shizuoka" as the destination, and "October 20th 13:00" as the scheduled time and date, the following can be used as the prompt text:

[1537] "Use AI to evaluate the optimal route from Tokyo to Shizuoka and create a safe driving route."

[1538] The server collects weather data from OpenWeather, terrain data from GIS, and road and real-time traffic information from a traffic information API. Using TensorFlow, it performs risk assessments based on this data and generates optimal routes. The generated information is sent to the in-car navigation system and smartphone and displayed in real time. If there are sudden changes in weather or road conditions during hiking or driving, the server immediately reanalyzes the data and notifies the user. After completion, it organizes detailed route history and generates a report that can be used to plan the next trip.

[1539] In this way, by implementing the present invention, the user can make safe and efficient mountain climbing and driving plans.

[1540] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1541] Step 1:

[1542] A user inputs planning information, such as a starting point, destination, and planned date and time, using a smartphone or in-car infotainment system. This information is sent from the device to a server. Inputs include the starting point, destination, date and time, and user-specific information. Outputs provide this planning information to the server.

[1543] Step 2:

[1544] The server collects weather data, topographical data (or road data), and past accident data based on the received plan information. Here, data is acquired using weather data APIs (such as OpenWeather), geographic information systems (GIS), and traffic information APIs. Plan information is used as input, and weather data, topographical data, road data, and past accident data are compiled as output.

[1545] Step 3:

[1546] The server analyzes the collected data and performs risk assessment using AI analysis tools (TensorFlow and PyTorch). This process evaluates the risk level based on weather conditions, road conditions, terrain, and past accident history. The input includes various collected data, and the output generates a risk assessment result.

[1547] Step 4:

[1548] The server generates the optimal route based on the risk assessment results. In this process, an algorithm for calculating the optimal route is applied to select a safe and efficient route for the user. The input is the risk assessment result, and the output is the optimal route information.

[1549] Step 5:

[1550] The server sends the generated information about the optimal route and risk warning points to the terminal. The terminal displays this information on a map and provides it to the user. The optimal route information is the input, and the route and risk warning points displayed on the map are provided to the user as the output.

[1551] Step 6:

[1552] The server monitors the user's location in real time while hiking or driving. If there are changes in weather or road conditions, the data is reanalyzed and alerts or warnings are sent to the user. In this step, data is processed and reevaluated in real time. The input includes real-time location information and new weather and road data, and the output is emergency alerts and notifications sent to the device.

[1553] Step 7:

[1554] After the climb or drive is completed, the server organizes the records and generates a detailed report, which provides useful information for planning the next climb or drive. The input includes the user's activity history and planning information, and the output is a detailed report, which is sent to the user's device and can be viewed.

[1555] The above processing flow enables safe and efficient mountain climbing and driving plans to be realized.

[1556] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1557] System Overview

[1558] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system includes functions for inputting mountain climbing plan information, collecting and analyzing weather data, topographical data, and past accident data, risk assessment, generating optimal mountain climbing routes, real-time monitoring and notifications, organizing mountain climbing records and generating reports, and an emotion engine that recognizes the user's emotions. It also includes a function that uses the emotion engine to provide appropriate advice and warnings based on the user's emotional state. Each method and specific examples are described in detail below.

[1559] Program processing

[1560] 1. Enter your climbing plan information

[1561] Terminal: Provides an interface for users to input their mountain climbing plans (starting point, destination, planned date and time, climbing experience, etc.) When the user inputs the necessary information and presses the send button, the input information is sent to the server.

[1562] User: Enters a mountain climbing plan and sends it from the device to the server.

[1563] 2. Data collection

[1564] Server: Receives mountain climbing plan information sent by the user. Based on this information, it collects weather data, topographical data, and past accident data for the target area from the necessary data sources. Weather data is obtained from the weather service API, topographical data is collected from a geographic information system (GIS), and accident data is obtained from a mountain climbing accident database.

[1565] 3. Risk assessment and route optimization

[1566] Server: Analyzes collected weather data, topographical data, and past accident data. Weather data is used to extract and analyze weather patterns and forecasts, topographical data is used to extract elevation changes and ground surface conditions along the route, and accident data is used to extract and analyze risk factors.

[1567] Server: The analysis results are fed into an AI algorithm to perform risk assessment. This process integrates weather, terrain, and past accident information to assess the risk level for each climbing route. From the assessment results, risk points and risk levels for each route are calculated.

[1568] Server: Generates multiple climbing routes based on the risk assessment results. From the generated routes, selects the route assessed as having the least risk and generates detailed route information, including distance, elevation difference, estimated time, etc.

[1569] 4. Providing optimal routes and important points to note

[1570] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[1571] Device: The optimal route and risk warning points are displayed on a map to provide users with information so they can plan their safe and optimal mountain climbing trip.

[1572] User: Create a safe mountain climbing plan based on the displayed information.

[1573] 5. Real-time monitoring and notifications

[1574] Server: Monitors the user's location in real time while they are climbing. If there is a sudden change in weather conditions or other risk information, it analyzes the situation promptly and notifies the user of the risk information as necessary.

[1575] Device: Display emergency alerts and warning notifications to users in real time.

[1576] 6. Organizing climbing records and generating reports

[1577] Server: After the climb is completed, the server receives feedback from the user and organizes the climb record. It analyzes the user's behavior and situation and generates a detailed report, which also includes reflections and suggestions for improvement.

[1578] Terminal: The generated report is displayed to the user, who can use it to plan their next mountain climbing trip.

[1579] Introducing the Emotion Engine

[1580] 7. Emotion Engine Settings

[1581] Terminal: Provides an interface for the emotion engine to collect data such as the user's facial expressions, voice, and heart rate to measure the user's emotions.

[1582] User: Emotional data is provided to the device in real time via climbing equipment or wearable devices.

[1583] 8. Collecting and analyzing emotional data

[1584] Server: Receives collected emotion data and analyzes it in real time. The emotion engine evaluates the user's mental state and reflects it in risk assessment and route optimization as necessary.

[1585] 9. Emotion-based advice and notifications

[1586] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings if the user is under a high level of mental stress or feels stressed.

[1587] Device: Providing advice and reminders to the user, such as suggestions for resting places to calm down or simple breathing exercises to help them relax.

[1588] Specific examples

[1589] Assume that the user is planning to climb Mountain A next Saturday.

[1590] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[1591] 2. The server collects data on the surrounding weather, terrain, and past accidents. This data is organized specifically for Mountain A.

[1592] 3. The server performs risk assessment based on the data, calculates risk points and risk levels, selects the safest route, and generates detailed information about it.

[1593] 4. The server sends the optimal route information and risk warning information to the terminal.

[1594] 5. The device displays the optimal route and risk-related areas on a map for the user.

[1595] 6. During the climb, the server monitors the user's location in real time and notifies them of sudden changes in weather and other risk information.

[1596] 7. The device displays emergency alerts and warnings to the user.

[1597] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report.

[1598] 9. The device can display this report to the user, helping them plan their next climb.

[1599] The introduction of an emotion engine allows the system to monitor the user's mental state in real time and provide not only risk assessment but also psychological support. For example, if the user is feeling stressed, the system can suggest appropriate rest areas and notify them of ways to relax, providing a safe and supported climbing experience.

[1600] The processing flow will be explained below.

[1601] Step 1:

[1602] The user inputs mountain climbing plan information into an input form provided on the terminal. Specific input items include the starting point, destination, planned date and time, mountain climbing experience, etc. Once the user has entered all the necessary information and pressed the send button, the input information is sent to the server.

[1603] Step 2:

[1604] The server receives the climbing plan information sent by the user. Based on this information, it collects weather data, terrain data, and past accident data related to the climbing route from the necessary data sources. Weather data is obtained from the weather service API, terrain data from the geographic information system (GIS), and accident data from the climbing accident database.

[1605] Step 3:

[1606] The server analyzes the collected weather data, topographical data, and past accident data. The weather data is used to extract and analyze weather patterns and forecasts, the topographical data is used to extract elevation changes and ground surface conditions along the route, and the accident data is used to extract and analyze risk factors.

[1607] Step 4:

[1608] The server then uses an AI algorithm to perform a risk assessment based on the analysis results. This assessment process takes into consideration weather, terrain, and past accident information to calculate the risk level for each climbing route. The assessment results then confirm the risk points and their levels.

[1609] Step 5:

[1610] The server generates multiple climbing routes based on the risk assessment results, selects the route with the lowest risk from the generated routes, and generates detailed route information, including distance, elevation change, estimated time required, and other information.

[1611] Step 6:

[1612] The server sends the generated optimal route and information on risk-warning areas to the terminal.

[1613] Step 7:

[1614] The device displays the optimal route and risk-related points on a map to provide users with information, allowing them to create a safe and optimal mountain climbing plan based on this information.

[1615] Step 8:

[1616] Users use wearable devices or terminals to provide their own emotional data (heart rate, facial expressions, voice, etc.) to the terminals in real time.

[1617] Step 9:

[1618] The terminal collects emotion data and transmits it to the server.

[1619] Step 10:

[1620] The server analyzes the received emotional data and assesses the user's emotional state, adjusting the risk assessment accordingly if the user is experiencing high mental load or stress.

[1621] Step 11:

[1622] During the climb, the user's location information is also periodically sent from the device to the server.

[1623] Step 12:

[1624] The server monitors the user's situation based on real-time location and emotion data received, and immediately analyzes any sudden changes in weather or other risk factors and generates warnings as needed.

[1625] Step 13:

[1626] The device displays emergency alerts and reminder messages to users in real time, including advice based on their emotional state, such as "take a break to relax."

[1627] Step 14:

[1628] After the climb is complete, the server receives feedback from the user, organizes the climb record, and analyzes the user's behavior, situation, and emotional data to generate a detailed report.

[1629] Step 15:

[1630] The device displays the generated report to the user, who can use it to plan their next mountain climbing trip.

[1631] The above processing flow allows the user to climb mountains safely and efficiently, and by using the emotion engine, mental support is also provided at the same time.

[1632] Example 2

[1633] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1634] Mountaineering is a challenge to nature, but it also involves various risks, making it important to plan ahead and take into account weather changes, terrain conditions, and past accident data. However, comprehensively collecting and analyzing this information requires specialized knowledge and large amounts of data processing, making it difficult for average climbers. Furthermore, monitoring risks in real time during a climb and organizing records afterward are both cumbersome, and a system to address these issues is needed. Furthermore, supporting safe climbing by taking into account the climber's emotional state is also an important issue.

[1635] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1636] In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and conducting risk assessments; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during the mountain climbing; means for compiling a mountain climbing record and generating a report after the mountain climbing is completed; means for collecting and analyzing the user's emotional data; and means for providing appropriate advice and warnings based on the analyzed emotional data. This increases the safety of mountain climbing plans. It also provides real-time risk monitoring during the mountain climbing and appropriate support based on the user's emotional state, allowing for efficient record organization after the mountain climbing is completed.

[1637] "Mountain climbing plan information" refers to information necessary when climbing a mountain, and specifically includes the starting point, destination, planned date and time, mountain climbing experience, and the like.

[1638] "Weather data" refers to data that indicates weather conditions related to the climbing route or climbing journey, and specifically includes temperature, humidity, wind speed, precipitation, weather forecast, and the like.

[1639] "Topographical data" refers to data that indicates the physical characteristics of a climbing route, and specifically includes the type of ground surface, elevation difference, gradient, geographical features, and the like.

[1640] "Past accident data" refers to data related to past accidents and troubles that have occurred while climbing mountains, and specifically includes the location of the accident, the cause, the date and time of the accident, and the extent of the damage.

[1641] "Risk assessment" refers to the process of analyzing collected data and evaluating the risks and dangers associated with a climbing route, taking into account factors such as weather fluctuations, the difficulty of the terrain, and past accident data.

[1642] A "climbing route" is a path from a starting point to a destination, usually depicted on a map. The optimal climbing route is selected based on a risk assessment.

[1643] "Real-time monitoring" refers to the process of monitoring a user's location and environmental conditions in real time while climbing, including the continuous collection and analysis of GPS and weather data.

[1644] "Notification" refers to the process of providing information to users during the climbing or planning stage, specifically including emergency alerts, risk warnings, advice, etc.

[1645] A "mountain climbing record" is a record of the progress and results of mountain climbing activities, and specifically includes the mountain climbing route, activity history, environmental conditions, user feedback, and the like.

[1646] A "report" is a document that compiles collected and organized climbing records, and specifically includes a review of the climb and areas for improvement for the next time.

[1647] "Emotion data" is data that indicates the user's psychological state and emotions, and specifically includes facial expressions, voice, heart rate, and the like.

[1648] "Advice" refers to advice and guidance provided to users during the climbing or planning stages, and specifically includes suggestions for safe climbing and ways to reduce stress.

[1649] This invention is a system that uses AI and an emotion engine to improve safety during mountain climbing. This system inputs mountain climbing plan information, collects and analyzes weather data, terrain data, and past accident data, assesses risks, generates optimal mountain climbing routes, monitors and notifies users in real time, organizes mountain climbing records and generates reports, and includes an emotion engine that recognizes users' emotions.

[1650] Hardware and Software Used

[1651] The system uses the following hardware and software:

[1652] Terminal: A device that acts as a user interface, such as a smartphone or tablet.

[1653] Server: Data collection, analysis, route generation, monitoring, and report generation are performed using a high-performance cloud server.

[1654] Weather Service API: Used to retrieve weather data.

[1655] Geographic Information Systems (GIS): Used to collect topographical data.

[1656] Mountaineering accident database: Used to obtain past accident data.

[1657] Emotion engine: Analyzes the user's emotional data and provides appropriate advice and warnings.

[1658] Generative AI models: Used for risk assessment and optimal route generation.

[1659] Explanation of program processing

[1660] 1. Enter your climbing plan information

[1661] Terminal: Provides an interface for users to input their starting point, destination, planned date and time, climbing experience, etc. When the user inputs the information and presses the send button, the information is sent to the server.

[1662] User: Enters the climbing plan into the device interface and presses the send button.

[1663] 2. Data collection

[1664] Server: Based on the received mountain climbing plan information, it collects weather data, terrain data, and past accident data. Weather data is collected from the weather service API, terrain data from GIS, and accident data from the mountain climbing accident database.

[1665] 3. Risk assessment and route optimization

[1666] Server: Analyzes the collected data and performs risk assessments using AI algorithms based on weather data, topographical data, and past accident data.

[1667] Server: Generates optimal climbing routes based on risk assessment. Route information includes distance, elevation gain, estimated time, etc.

[1668] 4. Providing optimal routes and important points to note

[1669] Server: Organizes information on optimal routes and risk-related areas and sends it to the device.

[1670] Terminal: The received information is displayed on a map, allowing users to create a safe and optimal mountain climbing plan.

[1671] 5. Real-time monitoring and notifications

[1672] Server: Monitors the user's location in real time while climbing, and notifies them of sudden changes in weather and risks.

[1673] Terminal: Displays emergency alerts and warnings in real time.

[1674] 6. Organizing climbing records and generating reports

[1675] Server: Receives user feedback after the climb, organizes the climbing record, generates detailed reports, and provides them to users.

[1676] Terminal: The generated report can be displayed to the user to help them plan their next mountain climbing trip.

[1677] 7. Emotion Engine Configuration and Analysis

[1678] Terminal: Provides a data collection interface for the emotion engine, including a camera for recognizing the user's facial expressions, a microphone for analyzing voice, and a heart rate sensor.

[1679] User: Provides real-time emotional data through climbing equipment and wearable devices.

[1680] Server: Analyzes the collected emotional data, evaluates the user's mental state, and reflects this in risk assessment and route optimization as necessary.

[1681] 8. Emotion-based advice and notifications

[1682] Server: Evaluates the user's psychological state based on emotional data, and provides appropriate advice and warnings when the user is under high mental stress or feels stressed.

[1683] Device: Displays specific advice and reminders to the user, such as "Take a break" or "Breathing exercises to relax."

[1684] Specific examples

[1685] Assume that the user is planning to climb Mountain A next Saturday.

[1686] 1. The user enters the starting point (trailhead), destination (peak), planned date and time (6:00 a.m. on September 15th), and climbing experience (intermediate) on the terminal and sends it to the server.

[1687] 2. The server collects surrounding weather data, topographical data, and past accident data, and organizes it specifically for Mountain A.

[1688] 3. The server performs a risk assessment, selects the safest route, and generates detailed information about it.

[1689] 4. The server sends the optimal route information and risk warning information to the terminal.

[1690] 5. The device displays the optimal route and risk warning points on a map for the user.

[1691] 6. During the climb, the server monitors the user's location in real time and notifies them of any sudden changes in the weather.

[1692] 7. The device displays emergency alerts and warnings.

[1693] 8. After the climb is completed, the server organizes the climbing records and generates a detailed report that users can use to plan their next climb.

[1694] 9. The device collects emotional data using a facial recognition camera and heart rate sensor. The server analyzes the emotional data in real time and generates appropriate advice if stress is detected. The device then displays suggestions for reducing stress.

[1695] Prompt Sentence Examples

[1696] "Please tell me what climbing routes are safe."

[1697] "I'm an intermediate climber and I'm planning to climb Mountain A on September 15th. What's the best route?"

[1698] "What should I do if I feel stressed while climbing?"

[1699] This invention makes it possible to improve the safety of mountain climbing plans, monitor risks in real time during climbing, and provide appropriate support based on emotional states. In addition, by efficiently organizing records after climbing, it is possible to use the information to plan the next climbing trip.

[1700] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1701] Step 1:

[1702] Terminal: Provides an input interface for mountain climbing plan information. The user inputs the starting point, destination, planned date and time, and mountain climbing experience into the interface.

[1703] Input: User inputs starting point, destination, planned date and time, and climbing experience.

[1704] Specific behavior: The user enters information into the interface and presses the submit button.

[1705] Output: The entered mountain climbing plan information is sent to the server.

[1706] Step 2:

[1707] Server: Receives mountain climbing plan information and collects necessary data (weather data, terrain data, past accident data).

[1708] Input: Received climbing plan information.

[1709] Specific operation: The server sends a request to the weather service API to obtain weather data, collects topographical data from the GIS, and obtains past accident data from the mountaineering accident database.

[1710] Output: Acquired weather data, topographical data, and past accident data.

[1711] Step 3:

[1712] Server: Analyzes collected data and performs risk assessment.

[1713] Input: Weather data, terrain data, historical accident data.

[1714] Specific operation: The server analyzes meteorological data to identify weather patterns, analyzes topographical data to evaluate the elevation difference and ground surface condition of the route, and analyzes past accident data to extract risk factors. These are then integrated and used by an AI algorithm to perform a risk assessment.

[1715] Output: Risk assessment results (risk points, risk level).

[1716] Step 4:

[1717] Server: Generates the optimal climbing route based on the risk assessment results.

[1718] Input: Risk assessment results.

[1719] How it works: The server uses an AI algorithm to generate multiple climbing routes, evaluate the safety of each route, and select the safest one. Route information includes distance, elevation gain, estimated time, etc.

[1720] Output: Generated optimal climbing route information.

[1721] Step 5:

[1722] Server: Sends optimal route information and risk warning information to the device.

[1723] Input: Generated optimal climbing route information.

[1724] Specific operation: The server sends route information and details of risk warning areas to the terminal.

[1725] Output: Information on the best climbing route and risky areas is sent to the device.

[1726] Step 6:

[1727] Terminal: Displays optimal route information and risk warning areas on a map.

[1728] Input: Information on optimal climbing routes and risk warning points sent from the server.

[1729] Specific operation: The device displays route lines and markers for risk warning areas on a map, allowing users to plan safe mountain climbing.

[1730] Output: A visual representation of the climbing route and risk warnings to the user.

[1731] Step 7:

[1732] Server: Monitors the user's location in real time while climbing and notifies them of sudden changes in weather and other risks.

[1733] Input: User location, real-time weather data, and other risk information.

[1734] Specific operation: Regularly acquires GPS data and receives the latest weather information in real time from the weather service API. If a risk is detected, it prepares to notify the user immediately.

[1735] Output: Real-time notifications (emergency alerts and warnings).

[1736] Step 8:

[1737] Terminal: Displays emergency alerts and warnings in real time.

[1738] Input: Notification from the server.

[1739] Specific behavior: The device will immediately display the received notification on the screen and alert the user with sound and vibration.

[1740] Output: Visual and audible alerts and reminders provided to the user.

[1741] Step 9:

[1742] Server: After the climb is completed, the server receives feedback from the user and organizes the climbing record.

[1743] Input: User feedback, data collected during the climb.

[1744] Specific operation: The server analyzes the collected data and organizes the climbing route history, weather changes, risk warning points, etc. It then generates a detailed report including reviews and points for improvement.

[1745] Output: The generated climbing report.

[1746] Step 10:

[1747] Terminal: Displays the generated report to the user.

[1748] Input: Climbing report sent from the server.

[1749] What it does: The device displays a visual report to help users plan their next hike.

[1750] Output: A detailed climb report that is displayed to the user.

[1751] Step 11:

[1752] Terminal: Provides a data collection interface for the emotion engine.

[1753] Input: Data such as the user's facial expressions, voice, and heart rate.

[1754] How it works: The emotion engine uses a facial recognition camera, a voice analysis microphone, and a heart rate sensor to collect user emotional data.

[1755] Output: The collected emotion data is sent to the server.

[1756] Step 12:

[1757] Server: Analyzes the collected emotion data.

[1758] Input: Collected emotion data.

[1759] Specific operation: The server uses the emotion engine to evaluate the user's mental state in real time and reflects this in risk assessment and route optimization as necessary.

[1760] Output: Evaluation results and countermeasures based on the user's psychological state.

[1761] Step 13:

[1762] Server: Generates advice and warnings based on emotion data and sends them to the device.

[1763] Input: Evaluation results based on the user's mental state.

[1764] Specific behavior: The server generates appropriate advice and reminders based on the user's emotional state. For example, it creates notifications including "Take a break" and "Breathing exercises for relaxation."

[1765] Output: Advice and warnings based on emotion data are sent to the device.

[1766] Step 14:

[1767] Device: Displays advice and warnings to users based on emotional data.

[1768] Input: Notifications based on emotion data sent from the server.

[1769] Specific operation: The device displays the received advice or warning on the screen and notifies the user by sound or vibration.

[1770] Output: Specific advice or reminders provided to the user.

[1771] (Application example 2)

[1772] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1773] Existing mountain climbing planning systems perform risk assessments using weather data, topographical data, and past accident data, but do not provide real-time safety support that takes into account the climber's mental state. In particular, it is necessary to provide appropriate advice and warnings for safe driving by taking into account the driver's emotional state and stress during long-distance drives in autonomous vehicles. The present invention aims to solve this problem and improve the safety of autonomous vehicles by introducing an emotion engine.

[1774] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting mountain climbing plan information; means for collecting weather data, topographical data, and past accident data based on the input mountain climbing plan information; means for analyzing the collected data and performing risk assessment; means for generating an optimal mountain climbing route based on the risk assessment results; means for providing the generated mountain climbing route and risk warning points to the user; means for real-time monitoring and notification during mountain climbing; means for organizing the mountain climbing record and generating a report after the mountain climbing is completed; and means for collecting and analyzing emotional data in real time and providing appropriate advice and warnings based on the mental state. This makes it possible to realize a safe and comfortable driving plan not only for mountain climbers but also for drivers of autonomous vehicles.

[1775] "Mountain climbing plan information" is i...

Claims

1. A means for inputting mountain climbing plan information; A means for collecting weather data, topographical data, and past accident data based on inputted mountain climbing plan information; a means of analyzing the collected data and conducting a risk assessment; A means for generating an optimal climbing route based on the risk assessment results; A means for providing the generated climbing route and risk warning points to the user; a means for real-time monitoring and notification during the climb; A means to organize the climbing record and generate a report after the climbing is completed, A system including:

2. 10. The system of claim 1, further comprising means for periodically updating the weather data from the collected data.

3. The system of claim 1 , further comprising means for monitoring the user's location information in real time and notifying the user of unexpected weather changes or risks.

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