System

Abstract

An advantage of some aspects of the invention is to provide a system that evaluates risks in mountain climbing planning and preparation and proposes appropriate equipment and preparation.SOLUTION: A system includes a reception unit, an evaluation unit, and a proposal unit. The acceptance section accepts an input of a mountain climbing plan. The evaluation section analyzes the mountain climbing plan accepted by the acceptance section to evaluate the risk. The proposal unit proposes equipment and preparation on the basis of the risk evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

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] Conventional techniques do not adequately assess risks and suggest appropriate equipment and preparations when planning and preparing for mountain climbing, so there is room for improvement.

[0005] The system according to the embodiment aims to evaluate risks in mountain climbing planning and preparation, and to suggest appropriate equipment and preparations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an evaluation unit, and a proposal unit. The reception unit accepts input of a mountain climbing plan. The evaluation unit analyzes the mountain climbing plan accepted by the reception unit and evaluates risks. The proposal unit proposes equipment and preparations based on the risks evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can assess risks in mountain climbing planning and preparation, and suggest appropriate equipment and preparation. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A mountain climbing support system according to an embodiment of the present invention accepts input of a mountain climbing plan, and uses AI to evaluate risks and suggest appropriate equipment and preparation. The mountain climbing support system analyzes the mountain climbing plan input by the climber and evaluates risks based on weather information, terrain data, past mountain climbing accident cases, and other factors. Furthermore, the mountain climbing support system recommends appropriate equipment and preparation and provides necessary information to the climber. For example, a climber inputs a mountain climbing plan. For example, the climber inputs information such as the starting point, destination, planned route, and climbing schedule. This information is input into AI. Next, the mountain climbing support system uses AI to analyze the input mountain climbing plan. The AI ​​evaluates risks based on data such as weather information, terrain data, and past mountain climbing accident cases. For example, if there is a possibility that the weather will worsen on the planned climbing day, the AI ​​evaluates the risk and issues a warning to the climber. Furthermore, if there have been past mountain climbing accidents on the same route, the system evaluates risks based on that information. Furthermore, the mountain climbing support system uses AI to recommend appropriate equipment and preparation to the climber. For example, if there is a possibility that the weather will worsen, the AI ​​recommends that the climber prepare cold weather gear and rain gear. Additionally, if the climbing route is steep, the AI ​​will provide the climber with advice on appropriate equipment and physical training. This allows the climbing support system to ensure the climber's safety and prevent accidents. For example, when a climber makes a plan, the AI ​​can assess risks and suggest necessary preparations, allowing the climber to climb safely. Also, if the weather suddenly changes during a climb, the AI ​​can issue an advance warning, allowing the climber to take appropriate action.

[0029] A mountain climbing support system according to an embodiment includes a reception unit, an evaluation unit, and a suggestion unit. The reception unit accepts input of a mountain climbing plan. The mountain climbing plan may include, but is not limited to, a starting point, a destination, a planned route, and a mountain climbing schedule. The reception unit, for example, stores the information entered by the climber in a database and uses it for subsequent processing. The evaluation unit uses AI to analyze the mountain climbing plan accepted by the reception unit and evaluates risks. Risks are evaluated based on, for example, weather information, terrain data, and past mountain climbing accident cases, but are not limited to, examples. For example, the evaluation unit acquires weather information and evaluates the possibility of worsening weather on the planned mountain climbing day. The evaluation unit can also evaluate risks based on past mountain climbing accident cases on the same route. Furthermore, the evaluation unit can analyze terrain data and evaluate the difficulty of the mountain climbing route. The suggestion unit uses AI to suggest appropriate equipment and preparations based on the risks evaluated by the evaluation unit. The suggestions include, but are not limited to, for example, suggesting the preparation of cold weather gear and rain gear when there is a possibility of worsening weather, or providing advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit suggests the preparation of cold weather gear and rain gear to the climber. The suggestion unit can also provide advice on appropriate equipment and physical training when the climbing route is steep. As a result, the mountain climbing support system according to the embodiment can consistently perform processes from inputting a mountain climbing plan to risk assessment and suggesting appropriate equipment and preparation.

[0030] The mountain climbing support system includes an acquisition unit that acquires weather information. The acquisition unit acquires the weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed, for example. The acquisition unit acquires the weather information, for example, from a weather database. The acquisition unit can also acquire real-time weather observation data. For example, the acquisition unit acquires wide-area weather information using meteorological satellite data. The acquisition unit can also acquire detailed weather information using ground observation data. By acquiring the weather information, the accuracy of risk assessment is improved. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the weather information acquired from the weather database into AI, which can analyze the weather information and use it for risk assessment.

[0031] The mountain climbing support system includes a collection unit that collects past cases of mountain accidents. The collection unit collects the past cases of mountain accidents. The cases of mountain accidents include, but are not limited to, causes of mountain accidents, locations of mountain accidents, and countermeasures. The collection unit, for example, collects the past cases of mountain accidents from a database. The collection unit can also collect the cases of mountain accidents by conducting field surveys. For example, the collection unit searches the database for past cases of mountain accidents and uses them for risk assessment. The collection unit can also conduct field surveys, collect the cases of mountain accidents, and store them in the database. In this way, collecting the past cases of mountain accidents improves the accuracy of risk assessment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the cases of mountain accidents collected from the database into AI, which can analyze the cases of mountain accidents and use them for risk assessment.

[0032] The mountain climbing support system includes a warning unit that issues a warning based on a risk assessment. The warning unit issues the warning based on the risk assessment. Examples of warnings include, but are not limited to, sudden changes in weather, dangerous areas in the terrain, and warnings based on past mountain climbing accidents. For example, the warning unit warns climbers of sudden changes in weather. The warning unit can also warn climbers of dangerous areas on the mountain climbing route. For example, the warning unit predicts sudden changes in weather and issues a warning to climbers. The warning unit can also identify dangerous areas in the terrain and warn climbers of the danger. In this way, issuing a warning based on the risk assessment can provide appropriate warnings to climbers. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the results of the risk assessment into AI, which can then generate and provide the warning to the climber.

[0033] The evaluation unit can evaluate risk based on weather information, terrain data, and past cases of mountain accidents. The evaluation unit evaluates risk based on weather information, for example. For example, the evaluation unit evaluates the possibility of worsening weather on the planned day of mountain climbing. The evaluation unit can also evaluate risk based on terrain data. For example, the evaluation unit analyzes the slope and geology of the mountain climbing route to evaluate risk. The evaluation unit can also evaluate risk based on past cases of mountain accidents. For example, the evaluation unit analyzes past cases of mountain accidents on the same route to evaluate risk. In this way, evaluating risk based on weather information, terrain data, and past cases of mountain accidents improves the accuracy of risk assessment. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input weather information, terrain data, and past cases of mountain accidents into AI, which then evaluates the risk.

[0034] The suggestion unit can suggest appropriate equipment and preparations based on the risk assessment. For example, the suggestion unit suggests preparing cold weather gear and rain gear when there is a possibility of worsening weather. For example, the suggestion unit suggests preparing cold weather gear and rain gear to the climber. The suggestion unit can also provide advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit provides advice on appropriate equipment and physical training to the climber. This improves the safety of the climber by suggesting appropriate equipment and preparations based on the risk assessment. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the results of the risk assessment into AI, which then suggests appropriate equipment and preparations.

[0035] The suggestion unit can suggest to the climber that they prepare cold weather gear and rain gear. For example, the suggestion unit suggests that they prepare cold weather gear and rain gear when there is a possibility that the weather will worsen. For example, the suggestion unit suggests that the climber prepare cold weather gear and rain gear. The suggestion unit can also suggest specific types and standards of cold weather gear and rain gear to the climber. For example, the suggestion unit suggests the material, durability, waterproofness, etc. of the cold weather gear. In this way, by suggesting the preparation of cold weather gear and rain gear, the safety of the climber is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the results of the risk assessment into AI, which can then suggest the preparation of cold weather gear and rain gear.

[0036] The suggestion unit can provide advice on equipment and physical training when the climbing route is difficult. For example, the suggestion unit provides advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit provides advice on appropriate equipment and physical training to the climber. The suggestion unit can also suggest specific equipment and physical training content according to the difficulty of the climbing route. For example, the suggestion unit suggests equipment selection criteria and training methods according to the difficulty of the climbing route. This improves the safety of the climber by providing advice on appropriate equipment and physical training when the climbing route is steep. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the results of a risk assessment into AI, which can then provide advice on appropriate equipment and physical training.

[0037] The reception unit can analyze the climber's past climbing history and suggest the optimal input method. For example, the reception unit automatically displays as candidates climbing routes that the climber has frequently input in the past. For example, the reception unit prioritizes suggesting input methods (voice, text, etc.) that the climber has used in the past. The reception unit can also predict and suggest routes to be used in specific seasons or time periods based on the climber's past climbing history. For example, the reception unit analyzes the climber's past climbing history and suggests the optimal input method. In this way, the optimal input method can be suggested by analyzing the climber's past climbing history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's past climbing history into AI, which then suggests the optimal input method.

[0038] When inputting a mountain climbing plan, the reception unit can perform filtering based on the climber's current physical condition and health status. For example, if the climber is tired, the reception unit can prioritize suggesting a shorter and safer route. For example, if the climber is in good health, the reception unit can suggest a slightly more difficult route. Furthermore, if the climber is in poor health, the reception unit can also suggest a route that includes many rest points. For example, the reception unit performs filtering based on the climber's current physical condition and health status. In this way, by filtering according to the climber's physical condition and health status, a more appropriate mountain climbing plan can be proposed. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's physical condition data into AI, which then performs filtering.

[0039] When inputting a mountain climbing plan, the reception unit can select the optimal input means according to the climber's input method. For example, the reception unit can automatically set the mountain climbing plan by the climber simply inputting "from the trailhead of Mt. A to the summit" by voice. For example, the reception unit can easily set the mountain climbing plan by the climber performing specific gestures on the smartphone screen. The reception unit can also allow the climber to set the mountain climbing plan more intuitively by combining voice input and gesture input. For example, the reception unit selects the optimal input means according to the climber's input method. This allows the climber to smoothly input the mountain climbing plan by selecting the optimal input means according to the climber's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the climber's input data into AI, which then selects the optimal input means.

[0040] When inputting a mountain climbing plan, the reception unit can prioritize inputting highly relevant plans by taking into account the geographical location information of the climber. The reception unit, for example, prioritizes suggesting mountain climbing routes close to the climber's current location. For example, the reception unit can suggest relevant mountain climbing plans based on places the climber has visited in the past. The reception unit can also prioritize suggesting mountain climbing routes that are easily accessible from the climber's current location. For example, the reception unit prioritizes inputting highly relevant plans by taking into account the geographical location information of the climber. In this way, highly relevant plans can be prioritized by taking into account the geographical location information of the climber. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the climber to AI, and the AI ​​can prioritize inputting highly relevant plans.

[0041] When inputting a mountain climbing plan, the reception unit can analyze the climber's social media activity and input the related plan. For example, the reception unit can suggest a mountain climbing plan based on the location where the climber checked in on social media. For example, the reception unit can analyze the content of the climber's social media posts and suggest the related mountain climbing plan. The reception unit can also suggest the related mountain climbing plan by referring to the activity of the climber's friends on social media. For example, the reception unit can analyze the climber's social media activity and input the related plan. In this way, the related plan can be input by analyzing the climber's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's social media data into AI, which then inputs the related plan.

[0042] When inputting a mountain climbing plan, the reception unit can customize the input method by reflecting the climber's past feedback. For example, the reception unit preferentially suggests input methods that the climber has previously preferred. For example, the reception unit customizes the input interface based on the climber's past feedback. The reception unit can also optimize the input procedure by reflecting the climber's past feedback. For example, the reception unit customizes the input method by reflecting the climber's past feedback. In this way, the input method can be customized by reflecting the climber's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's feedback data into AI, which can customize the input method.

[0043] The evaluation unit can adjust the accuracy of the evaluation based on the level of detail of the mountain climbing plan when assessing risks. For example, if the mountain climbing plan is detailed, the evaluation unit increases the accuracy of the risk assessment. For example, if the mountain climbing plan is simple, the evaluation unit decreases the accuracy of the risk assessment. The evaluation unit can also adjust the criteria for risk assessment according to the level of detail of the mountain climbing plan. For example, the evaluation unit adjusts the accuracy of the evaluation based on the level of detail of the mountain climbing plan. This allows for a more appropriate risk assessment by adjusting the accuracy of the evaluation according to the level of detail of the mountain climbing plan. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input detailed data of the mountain climbing plan into AI, which then adjusts the accuracy of the evaluation.

[0044] When assessing risk, the evaluation unit can apply different evaluation algorithms depending on the category of the climbing route. For example, the evaluation unit applies a simple risk evaluation algorithm to a route for beginners. For example, the evaluation unit applies a standard risk evaluation algorithm to a route for intermediate climbers. The evaluation unit can also apply a detailed risk evaluation algorithm to a route for advanced climbers. For example, the evaluation unit applies different evaluation algorithms depending on the category of the climbing route. In this way, more appropriate risk assessment can be performed by applying different evaluation algorithms depending on the category of the climbing route. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input climbing route category data into AI, and the AI ​​can apply different evaluation algorithms.

[0045] When assessing risks, the evaluation unit can improve the accuracy of the assessment by referring to the climber's past evaluation results. The evaluation unit improves the accuracy of the assessment, for example, based on risks the climber has encountered in the past. For example, the evaluation unit analyzes the climber's past evaluation results and optimizes the risk assessment algorithm. The evaluation unit can also adjust the risk assessment criteria by referring to the climber's past evaluation results. For example, the evaluation unit improves the accuracy of the assessment by referring to the climber's past evaluation results. In this way, the accuracy of the risk assessment is improved by referring to the climber's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the climber's past evaluation data into AI, which can improve the accuracy of the assessment.

[0046] The evaluation unit can determine the priority of the evaluation based on the time of submission of the mountain climbing plan during risk evaluation. For example, if the mountain climbing plan is submitted early, the evaluation unit performs a detailed risk evaluation. For example, if the mountain climbing plan is submitted at the last minute, the evaluation unit performs a quick risk evaluation. The evaluation unit can also adjust the priority of the evaluation based on the time of submission of the mountain climbing plan. For example, the evaluation unit determines the priority of the evaluation based on the time of submission of the mountain climbing plan. In this way, by determining the priority of the evaluation based on the time of submission of the mountain climbing plan, more appropriate risk evaluation can be performed. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the time of submission of the mountain climbing plan into AI, and the AI ​​can determine the priority.

[0047] The evaluation unit can adjust the order of evaluation based on the relevance of the mountain climbing plan during risk evaluation. For example, the evaluation unit prioritizes evaluation if the mountain climbing plan is highly relevant to past mountain climbing accident cases. For example, the evaluation unit prioritizes evaluation if the mountain climbing plan is highly relevant to weather information. The evaluation unit can also adjust the order of evaluation according to the relevance of the mountain climbing plan. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the mountain climbing plan. In this way, by adjusting the order of evaluation according to the relevance of the mountain climbing plan, more appropriate risk evaluation can be performed. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input relevance data of the mountain climbing plan into AI, and the AI ​​can adjust the order of evaluation.

[0048] When assessing risks, the evaluation unit can adjust the level of detail of the evaluation according to the expertise level of the climber. For example, the evaluation unit provides a detailed risk evaluation to a beginner climber. For example, the evaluation unit provides a standard risk evaluation to an intermediate climber. The evaluation unit can also provide a simplified risk evaluation to an advanced climber. For example, the evaluation unit adjusts the level of detail of the evaluation according to the expertise level of the climber. In this way, a more appropriate risk assessment can be performed by adjusting the level of detail of the evaluation according to the expertise level of the climber. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the expertise level data of the climber into AI, which can adjust the level of detail of the evaluation.

[0049] The suggestion unit can adjust the level of detail of the proposal based on the importance of the risk when making a proposal. For example, when the risk is high, the suggestion unit provides a detailed proposal. For example, when the risk is low, the suggestion unit provides a simple proposal. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the risk. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the risk. In this way, by adjusting the level of detail of the proposal based on the importance of the risk, a more appropriate proposal can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input risk assessment data to AI, and the AI ​​can adjust the level of detail of the proposal.

[0050] When making a proposal, the proposal unit can apply different proposal algorithms depending on the risk category. For example, for weather risk, the proposal unit proposes equipment appropriate for the weather. For example, for terrain risk, the proposal unit proposes equipment appropriate for the terrain. Furthermore, for distress risk, the proposal unit can also propose equipment for preventing distress. For example, the proposal unit applies different proposal algorithms depending on the risk category. In this way, more appropriate proposals can be made by applying different proposal algorithms depending on the risk category. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk category data into AI, and the AI ​​can apply different proposal algorithms.

[0051] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the climber's past proposal results. The suggestion unit improves the accuracy of the proposal, for example, based on proposals the climber has received in the past. For example, the suggestion unit analyzes the climber's past proposal results and optimizes the proposal algorithm. The suggestion unit can also adjust the criteria for the proposal by referring to the climber's past proposal results. For example, the suggestion unit improves the accuracy of the proposal by referring to the climber's past proposal results. In this way, the accuracy of the proposal is improved by referring to the climber's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the climber's past proposal data into AI, which can improve the accuracy of the proposal.

[0052] The proposal unit can determine the priority of the proposal based on the time of risk submission when making a proposal. For example, if a risk is submitted early, the proposal unit provides a detailed proposal. For example, if a risk is submitted at the last minute, the proposal unit provides a quick proposal. The proposal unit can also adjust the priority of the proposal based on the time of risk submission. For example, the proposal unit determines the priority of the proposal based on the time of risk submission. This allows for more appropriate proposals to be made by determining the priority of the proposal based on the time of risk submission. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk submission time data into AI, which can then determine the priority.

[0053] When making a proposal, the suggestion unit can adjust the order of the proposals based on the relevance of the risks. For example, the suggestion unit prioritizes the proposal when the risk is highly relevant to past accident cases. For example, the suggestion unit prioritizes the proposal when the risk is highly relevant to weather information. The suggestion unit can also adjust the order of the proposals based on the relevance of the risks. For example, the suggestion unit adjusts the order of the proposals based on the relevance of the risks. In this way, by adjusting the order of the proposals based on the relevance of the risks, more appropriate proposals can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input risk relevance data into AI, which can adjust the order of the proposals.

[0054] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the expertise level of the climber. For example, the suggestion unit provides suggestions in simple language to a beginner climber. For example, the suggestion unit can provide suggestions using standard technical terminology to an intermediate climber. The suggestion unit can also provide suggestions using detailed technical terminology to an advanced climber. For example, the suggestion unit adjusts the use of technical terminology in the suggestion according to the expertise level of the climber. This allows for more appropriate suggestions to be made by adjusting the use of technical terminology in the suggestion according to the expertise level of the climber. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the expertise level data of the climber into AI, which can adjust the use of technical terminology.

[0055] When acquiring weather information, the acquisition unit can analyze the climber's past climbing history and select the optimal acquisition method. The acquisition unit selects the optimal method based on, for example, weather information acquisition methods used by the climber in the past. For example, the acquisition unit adjusts the weather information acquisition frequency based on the climber's past climbing history. The acquisition unit can also analyze the climber's past climbing history and select the most efficient weather information acquisition method. For example, the acquisition unit analyzes the climber's past climbing history when acquiring weather information and selects the optimal acquisition method. In this way, the optimal weather information acquisition method can be selected by analyzing the climber's past climbing history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's past climbing history data into AI, which selects the optimal acquisition method.

[0056] When acquiring weather information, the acquisition unit can perform filtering based on the climber's current climbing plan. The acquisition unit, for example, prioritizes acquiring relevant weather information based on the climber's current climbing plan. For example, the acquisition unit acquires weather information for a specific area based on the climber's climbing route. The acquisition unit can also acquire weather information for a specific date based on the climber's climbing itinerary. For example, the acquisition unit performs filtering based on the climber's current climbing plan when acquiring weather information. In this way, filtering based on the climber's current climbing plan can provide more relevant weather information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input climbing plan data into AI, which then performs filtering.

[0057] When acquiring weather information, the acquisition unit can select the optimal acquisition means according to the climber's input method. For example, if the climber acquires weather information by voice, the acquisition unit provides the information using voice recognition technology. For example, if the climber acquires weather information by text, the acquisition unit provides text-based information. Furthermore, if the climber acquires weather information by image, the acquisition unit can also provide visual information. For example, when acquiring weather information, the acquisition unit selects the optimal acquisition means according to the climber's input method. This allows for smooth acquisition of weather information by selecting the optimal acquisition means according to the climber's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's input data into AI, which then selects the optimal acquisition means.

[0058] When acquiring weather information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the geographical location information of the climber. The acquisition unit, for example, prioritizes acquiring relevant weather information based on the climber's current location. For example, the acquisition unit prioritizes acquiring weather information for a specific area based on the climber's climbing route. The acquisition unit can also prioritize acquiring weather information for a specific date based on the climber's climbing itinerary. For example, the acquisition unit prioritizes acquiring highly relevant information by taking into account the geographical location information of the climber when acquiring weather information. In this way, by taking into account the geographical location information of the climber, highly relevant weather information can be prioritized. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the climber into AI, which then prioritizes acquiring highly relevant information.

[0059] The acquisition unit can analyze the climber's social media activity and acquire related information when acquiring weather information. For example, the acquisition unit acquires weather information related to a location where the climber has checked in on social media. For example, the acquisition unit analyzes the climber's social media posts to acquire related weather information. The acquisition unit can also acquire related weather information by referring to the activity of the climber's friends on social media. For example, the acquisition unit analyzes the climber's social media activity and acquires related information when acquiring weather information. In this way, related weather information can be acquired by analyzing the climber's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's social media data into AI, which then acquires related information.

[0060] When acquiring weather information, the acquisition unit can customize the acquisition method by reflecting the climber's past feedback. For example, the acquisition unit preferentially suggests weather information acquisition methods that the climber has previously preferred. For example, the acquisition unit customizes the weather information acquisition interface based on the climber's past feedback. The acquisition unit can also optimize the weather information acquisition procedure by reflecting the climber's past feedback. For example, the acquisition unit customizes the acquisition method by reflecting the climber's past feedback when acquiring weather information. In this way, the weather information acquisition method can be customized by reflecting the climber's past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's feedback data into AI, which can customize the acquisition method.

[0061] When collecting past cases of accidents, the collection unit can analyze the climber's past climbing history and select the optimal collection method. The collection unit selects the optimal collection method, for example, based on the risks the climber has encountered in the past. For example, the collection unit prioritizes collecting relevant cases of accidents from the climber's past climbing history. The collection unit can also analyze the climber's past climbing history and select the most efficient collection method. For example, when collecting past cases of accidents, the collection unit analyzes the climber's past climbing history and selects the optimal collection method. In this way, by analyzing the climber's past climbing history, the optimal collection method for collecting cases of accidents can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climber's past climbing history data into AI, which selects the optimal collection method.

[0062] The collection unit can filter past accident cases based on the climber's current climbing plan when collecting them. The collection unit, for example, prioritizes collecting relevant accident cases based on the climber's current climbing plan. For example, the collection unit collects accident cases in a specific area based on the climber's climbing route. The collection unit can also collect accident cases on a specific date based on the climber's climbing itinerary. For example, the collection unit filters past accident cases based on the climber's current climbing plan when collecting them. In this way, filtering based on the climber's current climbing plan can provide more relevant accident cases. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input climbing plan data into AI, which then performs the filtering.

[0063] The collection unit can select the optimal collection means according to the climber's input method when collecting past accident cases. For example, if the climber collects accident cases by voice, the collection unit provides information using voice recognition technology. For example, if the climber collects accident cases by text, the collection unit provides text-based information. Furthermore, if the climber collects accident cases by image, the collection unit can also provide visual information. For example, the collection unit selects the optimal collection means according to the climber's input method when collecting past accident cases. This allows for smooth collection of accident cases by selecting the optimal collection means according to the climber's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climber's input data into AI, which then selects the optimal collection means.

[0064] When collecting past cases of mountain climbers' accidents, the collection unit can prioritize collecting highly relevant cases by taking into account the geographical location information of the climbers. The collection unit, for example, prioritizes collecting related cases of mountain climbers' accidents based on the climbers' current location. For example, the collection unit prioritizes collecting cases of mountain climbers' accidents in a specific area based on the climbers' climbing route. The collection unit can also prioritize collecting cases of mountain climbers' accidents on a specific date based on the climbers' climbing schedule. For example, when collecting past cases of mountain climbers' accidents, the collection unit prioritizes collecting highly relevant cases by taking into account the climbers' geographical location information. In this way, by taking into account the climbers' geographical location information, highly relevant cases of mountain climbers' accidents can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climbers' geographical location information into AI, which can then prioritize collecting highly relevant cases.

[0065] The collection unit can analyze the social media activities of climbers when collecting past cases of mountaineering accidents and collect related cases. For example, the collection unit collects cases of mountaineering accidents related to places where climbers have checked in on social media. For example, the collection unit analyzes the content of climbers' social media posts and collects related cases of mountaineering accidents. The collection unit can also collect related cases of mountaineering accidents by referring to the activities of the climbers' friends on social media. For example, the collection unit analyzes the social media activities of climbers when collecting past cases of mountaineering accidents and collects related cases. In this way, related cases of mountaineering accidents can be collected by analyzing the climbers' social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the climbers' social media data into AI, which can collect related cases.

[0066] The collection unit can customize the collection method by reflecting the climber's past feedback when collecting past accident cases. The collection unit, for example, preferentially suggests collection methods that the climber has previously preferred. For example, the collection unit customizes the accident case collection interface based on the climber's past feedback. The collection unit can also optimize the accident case collection procedure by reflecting the climber's past feedback. For example, the collection unit customizes the collection method by reflecting the climber's past feedback when collecting past accident cases. In this way, the collection method for accident cases can be customized by reflecting the climber's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climber's feedback data into AI, which can customize the collection method.

[0067] The warning unit can adjust the level of detail of the warning based on the severity of the risk when issuing a warning. For example, the warning unit provides a detailed warning when the risk is high. For example, the warning unit provides a simple warning when the risk is low. The warning unit can also adjust the level of detail of the warning based on the severity of the risk. In this way, by adjusting the level of detail of the warning based on the severity of the risk, a more appropriate warning can be issued. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input risk assessment data into AI, and the AI ​​can adjust the level of detail of the warning.

[0068] When issuing a warning, the warning unit can apply different warning algorithms depending on the risk category. For example, the warning unit issues a weather-based warning for weather risk. For example, the warning unit issues a terrain-based warning for terrain risk. The warning unit can also issue a warning to prevent distress for distress risk. For example, the warning unit applies different warning algorithms depending on the risk category. This allows for more appropriate warnings to be issued by applying different warning algorithms depending on the risk category. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, AI, for example. For example, the warning unit can input risk category data into AI, and the AI ​​can apply different warning algorithms.

[0069] When issuing a warning, the warning unit can improve the accuracy of the warning by referring to the climber's past warning results. The warning unit improves the accuracy of the warning, for example, based on warnings the climber has received in the past. For example, the warning unit analyzes the climber's past warning results and optimizes the warning algorithm. The warning unit can also adjust the warning criteria by referring to the climber's past warning results. For example, the warning unit improves the accuracy of the warning by referring to the climber's past warning results. In this way, the accuracy of the warning is improved by referring to the climber's past warning results. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the climber's past warning data into AI, which can improve the accuracy of the warning.

[0070] When issuing a warning, the warning unit can determine the priority of the warning based on the time of risk submission. For example, if the risk is submitted early, the warning unit provides a detailed warning. For example, if the risk is submitted last minute, the warning unit provides a quick warning. The warning unit can also adjust the priority of the warning based on the time of risk submission. For example, the warning unit determines the priority of the warning based on the time of risk submission. This allows for more appropriate warnings by determining the priority of the warning based on the time of risk submission. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input risk submission time data into AI, which can then determine the priority.

[0071] The warning unit can adjust the order of warnings based on the relevance of risks when issuing a warning. For example, the warning unit prioritizes warnings when the risk is highly relevant to past distress cases. For example, the warning unit prioritizes warnings when the risk is highly relevant to weather information. The warning unit can also adjust the order of warnings based on the relevance of risks. For example, the warning unit adjusts the order of warnings based on the relevance of risks. In this way, by adjusting the order of warnings based on the relevance of risks, more appropriate warnings can be issued. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input risk relevance data into AI, which can adjust the order of warnings.

[0072] When issuing a warning, the warning unit can adjust the use of technical terms in the warning depending on the climber's level of expertise. For example, the warning unit provides a warning in simple language to a beginner climber. For example, the warning unit can provide a warning using standard technical terms to an intermediate climber. The warning unit can also provide a warning using detailed technical terms to an advanced climber. For example, the warning unit adjusts the use of technical terms in the warning depending on the climber's level of expertise. This allows for a more appropriate warning to be issued by adjusting the use of technical terms in the warning depending on the climber's level of expertise. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the climber's level of expertise data into AI, which can then adjust the use of technical terms.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The mountain climbing support system can further include a physical fitness acquisition unit that acquires the climber's physical fitness data. The physical fitness acquisition unit acquires data such as the climber's heart rate, oxygen saturation, and number of steps. This allows the climber's physical fitness status to be grasped in real time and appropriate advice to be provided. For example, the physical fitness acquisition unit can suggest taking a break if the climber's heart rate is high. It can also warn of the risk of altitude sickness if the oxygen saturation level drops. Furthermore, it can grasp the climber's progress based on the number of steps data and evaluate whether the climber is progressing as planned.

[0075] The mountain climbing support system can further include an experience assessment unit that analyzes the climber's past mountain climbing experience and adjusts the risk assessment based on the experience. For example, if the climber has a lot of past mountain climbing experience, the experience assessment unit sets a lenient risk assessment. For example, if the climber is a beginner, the experience assessment unit sets a strict risk assessment. Also, if the climber has a lot of experience in a particular area, the risk assessment for that area can be adjusted. In this way, by adjusting the risk assessment based on the climber's past experience, a more appropriate risk assessment can be performed.

[0076] The mountain climbing support system can further include a physical condition assessment unit that acquires the climber's physical condition data and adjusts the risk assessment based on the climber's physical condition. For example, the physical condition assessment unit sets a strict risk assessment if the climber is tired. For example, if the climber is in good health, the physical condition assessment unit sets a lenient risk assessment. Also, if the climber is in poor physical condition, the risk assessment can be performed quickly. In this way, a more appropriate risk assessment can be performed by adjusting the risk assessment according to the climber's physical condition.

[0077] The mountain climbing support system can further include a feedback suggestion unit that customizes the content of suggestions by reflecting the climber's past feedback. The feedback suggestion unit, for example, preferentially provides suggestion methods that the climber has previously preferred. For example, the suggestion interface is customized based on the climber's past feedback. The suggestion procedure can also be optimized by reflecting the climber's past feedback. In this way, the content of suggestions can be customized by reflecting the climber's past feedback.

[0078] The mountain climbing support system can further include a history suggestion unit that analyzes the climber's past climbing history and adjusts the content of suggestions based on the history. The history suggestion unit, for example, prioritizes suggestions based on routes that the climber has frequently used in the past. For example, it makes suggestions suitable for specific seasons or time periods based on the climber's past climbing history. It can also optimize suggestions for equipment and preparation based on the climber's past history. This allows the content of suggestions to be adjusted by analyzing the climber's past climbing history.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The reception unit receives input of a mountain climbing plan. The mountain climbing plan includes, but is not limited to, the starting point, destination, planned route, and climbing schedule. The reception unit stores the information entered by the climber in a database, for example, and uses it for subsequent processing. Step 2: The evaluation unit uses AI to analyze the mountain climbing plan accepted by the reception unit and evaluate the risks. The risk is evaluated based on, for example, weather information, topographical data, past cases of mountain climbing accidents, etc., but is not limited to these examples. For example, the evaluation unit obtains weather information and evaluates the possibility of the weather worsening on the planned mountain climbing day. The evaluation unit can also evaluate the risk based on past cases of mountain climbing accidents that occurred on the same route. Furthermore, the evaluation unit can analyze topographical data and evaluate the difficulty of the mountain climbing route. Step 3: The suggestion unit uses AI to suggest appropriate equipment and preparations based on the risks assessed by the evaluation unit. Examples of suggestions include, but are not limited to, suggesting the preparation of cold weather gear and rain gear when there is a possibility of worsening weather, or providing advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit suggests the preparation of cold weather gear and rain gear to a climber. The suggestion unit can also provide advice on appropriate equipment and physical training when the climbing route is steep.

[0081] (Example 2) A mountain climbing support system according to an embodiment of the present invention accepts input of a mountain climbing plan, and uses AI to evaluate risks and suggest appropriate equipment and preparation. The mountain climbing support system analyzes the mountain climbing plan input by the climber and evaluates risks based on weather information, terrain data, past mountain climbing accident cases, and other factors. Furthermore, the mountain climbing support system recommends appropriate equipment and preparation and provides necessary information to the climber. For example, a climber inputs a mountain climbing plan. For example, the climber inputs information such as the starting point, destination, planned route, and climbing schedule. This information is input into AI. Next, the mountain climbing support system uses AI to analyze the input mountain climbing plan. The AI ​​evaluates risks based on data such as weather information, terrain data, and past mountain climbing accident cases. For example, if there is a possibility that the weather will worsen on the planned climbing day, the AI ​​evaluates the risk and issues a warning to the climber. Furthermore, if there have been past mountain climbing accidents on the same route, the system evaluates risks based on that information. Furthermore, the mountain climbing support system uses AI to recommend appropriate equipment and preparation to the climber. For example, if there is a possibility that the weather will worsen, the AI ​​recommends that the climber prepare cold weather gear and rain gear. Additionally, if the climbing route is steep, the AI ​​will provide the climber with advice on appropriate equipment and physical training. This allows the climbing support system to ensure the climber's safety and prevent accidents. For example, when a climber makes a plan, the AI ​​can assess risks and suggest necessary preparations, allowing the climber to climb safely. Also, if the weather suddenly changes during a climb, the AI ​​can issue an advance warning, allowing the climber to take appropriate action.

[0082] A mountain climbing support system according to an embodiment includes a reception unit, an evaluation unit, and a suggestion unit. The reception unit accepts input of a mountain climbing plan. The mountain climbing plan may include, but is not limited to, a starting point, a destination, a planned route, and a mountain climbing schedule. The reception unit, for example, stores the information entered by the climber in a database and uses it for subsequent processing. The evaluation unit uses AI to analyze the mountain climbing plan accepted by the reception unit and evaluates risks. Risks are evaluated based on, for example, weather information, terrain data, and past mountain climbing accident cases, but are not limited to, examples. For example, the evaluation unit acquires weather information and evaluates the possibility of worsening weather on the planned mountain climbing day. The evaluation unit can also evaluate risks based on past mountain climbing accident cases on the same route. Furthermore, the evaluation unit can analyze terrain data and evaluate the difficulty of the mountain climbing route. The suggestion unit uses AI to suggest appropriate equipment and preparations based on the risks evaluated by the evaluation unit. The suggestions include, but are not limited to, for example, suggesting the preparation of cold weather gear and rain gear when there is a possibility of worsening weather, or providing advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit suggests the preparation of cold weather gear and rain gear to the climber. The suggestion unit can also provide advice on appropriate equipment and physical training when the climbing route is steep. As a result, the mountain climbing support system according to the embodiment can consistently perform processes from inputting a mountain climbing plan to risk assessment and suggesting appropriate equipment and preparation.

[0083] The mountain climbing support system includes an acquisition unit that acquires weather information. The acquisition unit acquires the weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed, for example. The acquisition unit acquires the weather information, for example, from a weather database. The acquisition unit can also acquire real-time weather observation data. For example, the acquisition unit acquires wide-area weather information using meteorological satellite data. The acquisition unit can also acquire detailed weather information using ground observation data. By acquiring the weather information, the accuracy of risk assessment is improved. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the weather information acquired from the weather database into AI, which can analyze the weather information and use it for risk assessment.

[0084] The mountain climbing support system includes a collection unit that collects past cases of mountain accidents. The collection unit collects the past cases of mountain accidents. The cases of mountain accidents include, but are not limited to, causes of mountain accidents, locations of mountain accidents, and countermeasures. The collection unit, for example, collects the past cases of mountain accidents from a database. The collection unit can also collect the cases of mountain accidents by conducting field surveys. For example, the collection unit searches the database for past cases of mountain accidents and uses them for risk assessment. The collection unit can also conduct field surveys, collect the cases of mountain accidents, and store them in the database. In this way, collecting the past cases of mountain accidents improves the accuracy of risk assessment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the cases of mountain accidents collected from the database into AI, which can analyze the cases of mountain accidents and use them for risk assessment.

[0085] The mountain climbing support system includes a warning unit that issues a warning based on a risk assessment. The warning unit issues the warning based on the risk assessment. Examples of warnings include, but are not limited to, sudden changes in weather, dangerous areas in the terrain, and warnings based on past mountain climbing accidents. For example, the warning unit warns climbers of sudden changes in weather. The warning unit can also warn climbers of dangerous areas on the mountain climbing route. For example, the warning unit predicts sudden changes in weather and issues a warning to climbers. The warning unit can also identify dangerous areas in the terrain and warn climbers of the danger. In this way, issuing a warning based on the risk assessment can provide appropriate warnings to climbers. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the results of the risk assessment into AI, which can then generate and provide the warning to the climber.

[0086] The evaluation unit can evaluate risk based on weather information, terrain data, and past cases of mountain accidents. The evaluation unit evaluates risk based on weather information, for example. For example, the evaluation unit evaluates the possibility of worsening weather on the planned day of mountain climbing. The evaluation unit can also evaluate risk based on terrain data. For example, the evaluation unit analyzes the slope and geology of the mountain climbing route to evaluate risk. The evaluation unit can also evaluate risk based on past cases of mountain accidents. For example, the evaluation unit analyzes past cases of mountain accidents on the same route to evaluate risk. In this way, evaluating risk based on weather information, terrain data, and past cases of mountain accidents improves the accuracy of risk assessment. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input weather information, terrain data, and past cases of mountain accidents into AI, which then evaluates the risk.

[0087] The suggestion unit can suggest appropriate equipment and preparations based on the risk assessment. For example, the suggestion unit suggests preparing cold weather gear and rain gear when there is a possibility of worsening weather. For example, the suggestion unit suggests preparing cold weather gear and rain gear to the climber. The suggestion unit can also provide advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit provides advice on appropriate equipment and physical training to the climber. This improves the safety of the climber by suggesting appropriate equipment and preparations based on the risk assessment. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the results of the risk assessment into AI, which then suggests appropriate equipment and preparations.

[0088] The suggestion unit can suggest to the climber that they prepare cold weather gear and rain gear. For example, the suggestion unit suggests that they prepare cold weather gear and rain gear when there is a possibility that the weather will worsen. For example, the suggestion unit suggests that the climber prepare cold weather gear and rain gear. The suggestion unit can also suggest specific types and standards of cold weather gear and rain gear to the climber. For example, the suggestion unit suggests the material, durability, waterproofness, etc. of the cold weather gear. In this way, by suggesting the preparation of cold weather gear and rain gear, the safety of the climber is improved. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the results of the risk assessment into AI, which can then suggest the preparation of cold weather gear and rain gear.

[0089] The suggestion unit can provide advice on equipment and physical training when the climbing route is difficult. For example, the suggestion unit provides advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit provides advice on appropriate equipment and physical training to the climber. The suggestion unit can also suggest specific equipment and physical training content according to the difficulty of the climbing route. For example, the suggestion unit suggests equipment selection criteria and training methods according to the difficulty of the climbing route. This improves the safety of the climber by providing advice on appropriate equipment and physical training when the climbing route is steep. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the results of a risk assessment into AI, which can then provide advice on appropriate equipment and physical training.

[0090] The reception unit can estimate the climber's emotions and adjust the input interface for the mountain climbing plan based on the estimated emotions. For example, if the climber is nervous, the reception unit provides a simple and intuitive interface to minimize input steps. For example, if the climber is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, if the climber is in a hurry, the reception unit can prioritize voice input to enable the climber to quickly input the mountain climbing plan. For example, the reception unit can automatically set the mountain climbing plan when the climber simply inputs "from the trailhead to the summit of Mt. A" by voice. This allows the climber to smoothly input the mountain climbing plan by adjusting the input interface according to the climber's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the climber's emotional data into the generation AI, which can then infer the emotion and adjust the input interface.

[0091] The reception unit can analyze the climber's past climbing history and suggest the optimal input method. For example, the reception unit automatically displays as candidates climbing routes that the climber has frequently input in the past. For example, the reception unit prioritizes suggesting input methods (voice, text, etc.) that the climber has used in the past. The reception unit can also predict and suggest routes to be used in specific seasons or time periods based on the climber's past climbing history. For example, the reception unit analyzes the climber's past climbing history and suggests the optimal input method. In this way, the optimal input method can be suggested by analyzing the climber's past climbing history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's past climbing history into AI, which then suggests the optimal input method.

[0092] When inputting a mountain climbing plan, the reception unit can perform filtering based on the climber's current physical condition and health status. For example, if the climber is tired, the reception unit can prioritize suggesting a shorter and safer route. For example, if the climber is in good health, the reception unit can suggest a slightly more difficult route. Furthermore, if the climber is in poor health, the reception unit can also suggest a route that includes many rest points. For example, the reception unit performs filtering based on the climber's current physical condition and health status. In this way, by filtering according to the climber's physical condition and health status, a more appropriate mountain climbing plan can be proposed. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's physical condition data into AI, which then performs filtering.

[0093] When inputting a mountain climbing plan, the reception unit can select the optimal input means according to the climber's input method. For example, the reception unit can automatically set the mountain climbing plan by the climber simply inputting "from the trailhead of Mt. A to the summit" by voice. For example, the reception unit can easily set the mountain climbing plan by the climber performing specific gestures on the smartphone screen. The reception unit can also allow the climber to set the mountain climbing plan more intuitively by combining voice input and gesture input. For example, the reception unit selects the optimal input means according to the climber's input method. This allows the climber to smoothly input the mountain climbing plan by selecting the optimal input means according to the climber's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the climber's input data into AI, which then selects the optimal input means.

[0094] The reception unit can estimate the climber's emotions and prioritize the input mountain climbing plans based on the estimated emotions of the climber. For example, if the climber is nervous, the reception unit prioritizes inputting low-risk plans. For example, if the climber is relaxed, the reception unit prioritizes inputting detailed plans. Also, if the climber is in a hurry, the reception unit can prioritize inputting simple plans. For example, the reception unit prioritizes the input mountain climbing plans based on the climber's emotions. In this way, by prioritizing mountain climbing plans according to the climber's emotions, more appropriate plans can be processed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the climber's emotion data into a generation AI, which can estimate the emotions and determine the priority.

[0095] When inputting a mountain climbing plan, the reception unit can prioritize inputting highly relevant plans by taking into account the geographical location information of the climber. The reception unit, for example, prioritizes suggesting mountain climbing routes close to the climber's current location. For example, the reception unit can suggest relevant mountain climbing plans based on places the climber has visited in the past. The reception unit can also prioritize suggesting mountain climbing routes that are easily accessible from the climber's current location. For example, the reception unit prioritizes inputting highly relevant plans by taking into account the geographical location information of the climber. In this way, highly relevant plans can be prioritized by taking into account the geographical location information of the climber. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the climber to AI, and the AI ​​can prioritize inputting highly relevant plans.

[0096] When inputting a mountain climbing plan, the reception unit can analyze the climber's social media activity and input the related plan. For example, the reception unit can suggest a mountain climbing plan based on the location where the climber checked in on social media. For example, the reception unit can analyze the content of the climber's social media posts and suggest the related mountain climbing plan. The reception unit can also suggest the related mountain climbing plan by referring to the activity of the climber's friends on social media. For example, the reception unit can analyze the climber's social media activity and input the related plan. In this way, the related plan can be input by analyzing the climber's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's social media data into AI, which then inputs the related plan.

[0097] When inputting a mountain climbing plan, the reception unit can customize the input method by reflecting the climber's past feedback. For example, the reception unit preferentially suggests input methods that the climber has previously preferred. For example, the reception unit customizes the input interface based on the climber's past feedback. The reception unit can also optimize the input procedure by reflecting the climber's past feedback. For example, the reception unit customizes the input method by reflecting the climber's past feedback. In this way, the input method can be customized by reflecting the climber's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the climber's feedback data into AI, which can customize the input method.

[0098] The evaluation unit can estimate the climber's emotions and adjust the risk assessment criteria based on the estimated emotions of the climber. For example, if the climber is nervous, the evaluation unit sets the risk assessment criteria stricter. For example, if the climber is relaxed, the evaluation unit sets the risk assessment criteria leniently. Also, if the climber is in a hurry, the evaluation unit can quickly set the risk assessment criteria. For example, the evaluation unit adjusts the risk assessment criteria based on the climber's emotions. This allows for more appropriate risk assessment by adjusting the risk assessment criteria according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the climber's emotion data into the generation AI, which can estimate the emotion and adjust the risk assessment criteria.

[0099] The evaluation unit can adjust the accuracy of the evaluation based on the level of detail of the mountain climbing plan when assessing risks. For example, if the mountain climbing plan is detailed, the evaluation unit increases the accuracy of the risk assessment. For example, if the mountain climbing plan is simple, the evaluation unit decreases the accuracy of the risk assessment. The evaluation unit can also adjust the criteria for risk assessment according to the level of detail of the mountain climbing plan. For example, the evaluation unit adjusts the accuracy of the evaluation based on the level of detail of the mountain climbing plan. This allows for a more appropriate risk assessment by adjusting the accuracy of the evaluation according to the level of detail of the mountain climbing plan. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input detailed data of the mountain climbing plan into AI, which then adjusts the accuracy of the evaluation.

[0100] When assessing risk, the evaluation unit can apply different evaluation algorithms depending on the category of the climbing route. For example, the evaluation unit applies a simple risk evaluation algorithm to a route for beginners. For example, the evaluation unit applies a standard risk evaluation algorithm to a route for intermediate climbers. The evaluation unit can also apply a detailed risk evaluation algorithm to a route for advanced climbers. For example, the evaluation unit applies different evaluation algorithms depending on the category of the climbing route. In this way, more appropriate risk assessment can be performed by applying different evaluation algorithms depending on the category of the climbing route. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input climbing route category data into AI, and the AI ​​can apply different evaluation algorithms.

[0101] When assessing risks, the evaluation unit can improve the accuracy of the assessment by referring to the climber's past evaluation results. The evaluation unit improves the accuracy of the assessment, for example, based on risks the climber has encountered in the past. For example, the evaluation unit analyzes the climber's past evaluation results and optimizes the risk assessment algorithm. The evaluation unit can also adjust the risk assessment criteria by referring to the climber's past evaluation results. For example, the evaluation unit improves the accuracy of the assessment by referring to the climber's past evaluation results. In this way, the accuracy of the risk assessment is improved by referring to the climber's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the climber's past evaluation data into AI, which can improve the accuracy of the assessment.

[0102] The evaluation unit can estimate the climber's emotions and determine the priority of risk assessment based on the estimated emotions of the climber. For example, if the climber is nervous, the evaluation unit prioritizes high-risk items in the evaluation. For example, if the climber is relaxed, the evaluation unit prioritizes low-risk items. Furthermore, if the climber is in a hurry, the evaluation unit can prioritize items that require quick evaluation. For example, the evaluation unit determines the priority of risk assessment based on the climber's emotions. This allows for more appropriate risk assessment by determining the priority of risk assessment according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the climber's emotion data into the generation AI, which can then estimate the emotions and determine the priority.

[0103] The evaluation unit can determine the priority of the evaluation based on the time of submission of the mountain climbing plan during risk evaluation. For example, if the mountain climbing plan is submitted early, the evaluation unit performs a detailed risk evaluation. For example, if the mountain climbing plan is submitted at the last minute, the evaluation unit performs a quick risk evaluation. The evaluation unit can also adjust the priority of the evaluation based on the time of submission of the mountain climbing plan. For example, the evaluation unit determines the priority of the evaluation based on the time of submission of the mountain climbing plan. In this way, by determining the priority of the evaluation based on the time of submission of the mountain climbing plan, more appropriate risk evaluation can be performed. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the time of submission of the mountain climbing plan into AI, and the AI ​​can determine the priority.

[0104] The evaluation unit can adjust the order of evaluation based on the relevance of the mountain climbing plan during risk evaluation. For example, the evaluation unit prioritizes evaluation if the mountain climbing plan is highly relevant to past mountain climbing accident cases. For example, the evaluation unit prioritizes evaluation if the mountain climbing plan is highly relevant to weather information. The evaluation unit can also adjust the order of evaluation according to the relevance of the mountain climbing plan. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the mountain climbing plan. In this way, by adjusting the order of evaluation according to the relevance of the mountain climbing plan, more appropriate risk evaluation can be performed. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input relevance data of the mountain climbing plan into AI, and the AI ​​can adjust the order of evaluation.

[0105] When assessing risks, the evaluation unit can adjust the level of detail of the evaluation according to the expertise level of the climber. For example, the evaluation unit provides a detailed risk evaluation to a beginner climber. For example, the evaluation unit provides a standard risk evaluation to an intermediate climber. The evaluation unit can also provide a simplified risk evaluation to an advanced climber. For example, the evaluation unit adjusts the level of detail of the evaluation according to the expertise level of the climber. In this way, a more appropriate risk assessment can be performed by adjusting the level of detail of the evaluation according to the expertise level of the climber. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the expertise level data of the climber into AI, which can adjust the level of detail of the evaluation.

[0106] The suggestion unit can estimate the climber's emotions and adjust the way the suggestions are expressed based on the estimated emotions of the climber. For example, if the climber is nervous, the suggestion unit can provide simple, highly visible suggestions. For example, if the climber is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the climber is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. For example, the suggestion unit can adjust the way the suggestions are expressed based on the climber's emotions. This allows for more appropriate suggestions to be made by adjusting the way the suggestions are expressed according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without using an AI. For example, the suggestion unit can input the climber's emotion data into the generation AI, which can then estimate the emotion and adjust the way the suggestions are expressed.

[0107] The suggestion unit can adjust the level of detail of the proposal based on the importance of the risk when making a proposal. For example, when the risk is high, the suggestion unit provides a detailed proposal. For example, when the risk is low, the suggestion unit provides a simple proposal. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the risk. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the risk. In this way, by adjusting the level of detail of the proposal based on the importance of the risk, a more appropriate proposal can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input risk assessment data to AI, and the AI ​​can adjust the level of detail of the proposal.

[0108] When making a proposal, the proposal unit can apply different proposal algorithms depending on the risk category. For example, for weather risk, the proposal unit proposes equipment appropriate for the weather. For example, for terrain risk, the proposal unit proposes equipment appropriate for the terrain. Furthermore, for distress risk, the proposal unit can also propose equipment for preventing distress. For example, the proposal unit applies different proposal algorithms depending on the risk category. In this way, more appropriate proposals can be made by applying different proposal algorithms depending on the risk category. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk category data into AI, and the AI ​​can apply different proposal algorithms.

[0109] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the climber's past proposal results. The suggestion unit improves the accuracy of the proposal, for example, based on proposals the climber has received in the past. For example, the suggestion unit analyzes the climber's past proposal results and optimizes the proposal algorithm. The suggestion unit can also adjust the criteria for the proposal by referring to the climber's past proposal results. For example, the suggestion unit improves the accuracy of the proposal by referring to the climber's past proposal results. In this way, the accuracy of the proposal is improved by referring to the climber's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the climber's past proposal data into AI, which can improve the accuracy of the proposal.

[0110] The suggestion unit can estimate the climber's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the climber is nervous, the suggestion unit provides a short and to-the-point suggestion. For example, if the climber is relaxed, the suggestion unit provides a longer suggestion with detailed explanations. The suggestion unit can also provide a quick and concise suggestion if the climber is in a hurry. For example, the suggestion unit adjusts the length of the suggestion based on the climber's emotions. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the climber's emotion data into the generation AI, which then estimates the emotion and adjusts the length of the suggestion.

[0111] The proposal unit can determine the priority of the proposal based on the time of risk submission when making a proposal. For example, if a risk is submitted early, the proposal unit provides a detailed proposal. For example, if a risk is submitted at the last minute, the proposal unit provides a quick proposal. The proposal unit can also adjust the priority of the proposal based on the time of risk submission. For example, the proposal unit determines the priority of the proposal based on the time of risk submission. This allows for more appropriate proposals to be made by determining the priority of the proposal based on the time of risk submission. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk submission time data into AI, which can then determine the priority.

[0112] When making a proposal, the suggestion unit can adjust the order of the proposals based on the relevance of the risks. For example, the suggestion unit prioritizes the proposal when the risk is highly relevant to past accident cases. For example, the suggestion unit prioritizes the proposal when the risk is highly relevant to weather information. The suggestion unit can also adjust the order of the proposals based on the relevance of the risks. For example, the suggestion unit adjusts the order of the proposals based on the relevance of the risks. In this way, by adjusting the order of the proposals based on the relevance of the risks, more appropriate proposals can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input risk relevance data into AI, which can adjust the order of the proposals.

[0113] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the expertise level of the climber. For example, the suggestion unit provides suggestions in simple language to a beginner climber. For example, the suggestion unit can provide suggestions using standard technical terminology to an intermediate climber. The suggestion unit can also provide suggestions using detailed technical terminology to an advanced climber. For example, the suggestion unit adjusts the use of technical terminology in the suggestion according to the expertise level of the climber. This allows for more appropriate suggestions to be made by adjusting the use of technical terminology in the suggestion according to the expertise level of the climber. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the expertise level data of the climber into AI, which can adjust the use of technical terminology.

[0114] The acquisition unit can estimate the climber's emotions and adjust the timing of weather information acquisition based on the estimated emotions of the climber. For example, if the climber is nervous, the acquisition unit can frequently acquire weather information to provide the latest information. For example, if the climber is relaxed, the acquisition unit can acquire weather information at a normal frequency. Furthermore, if the climber is in a hurry, the acquisition unit can quickly acquire and provide weather information. For example, the acquisition unit can adjust the timing of weather information acquisition based on the climber's emotions. This allows for more appropriate information to be provided by adjusting the timing of weather information acquisition according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using AI, for example, or without AI. For example, the acquisition unit can input the climber's emotion data into the generation AI, which can then estimate the emotion and adjust the acquisition timing.

[0115] When acquiring weather information, the acquisition unit can analyze the climber's past climbing history and select the optimal acquisition method. The acquisition unit selects the optimal method based on, for example, weather information acquisition methods used by the climber in the past. For example, the acquisition unit adjusts the weather information acquisition frequency based on the climber's past climbing history. The acquisition unit can also analyze the climber's past climbing history and select the most efficient weather information acquisition method. For example, the acquisition unit analyzes the climber's past climbing history when acquiring weather information and selects the optimal acquisition method. In this way, the optimal weather information acquisition method can be selected by analyzing the climber's past climbing history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's past climbing history data into AI, which selects the optimal acquisition method.

[0116] When acquiring weather information, the acquisition unit can perform filtering based on the climber's current climbing plan. The acquisition unit, for example, prioritizes acquiring relevant weather information based on the climber's current climbing plan. For example, the acquisition unit acquires weather information for a specific area based on the climber's climbing route. The acquisition unit can also acquire weather information for a specific date based on the climber's climbing itinerary. For example, the acquisition unit performs filtering based on the climber's current climbing plan when acquiring weather information. In this way, filtering based on the climber's current climbing plan can provide more relevant weather information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input climbing plan data into AI, which then performs filtering.

[0117] When acquiring weather information, the acquisition unit can select the optimal acquisition means according to the climber's input method. For example, if the climber acquires weather information by voice, the acquisition unit provides the information using voice recognition technology. For example, if the climber acquires weather information by text, the acquisition unit provides text-based information. Furthermore, if the climber acquires weather information by image, the acquisition unit can also provide visual information. For example, when acquiring weather information, the acquisition unit selects the optimal acquisition means according to the climber's input method. This allows for smooth acquisition of weather information by selecting the optimal acquisition means according to the climber's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's input data into AI, which then selects the optimal acquisition means.

[0118] The acquisition unit can estimate the climber's emotions and determine the priority of weather information to be acquired based on the estimated emotions of the climber. For example, if the climber is nervous, the acquisition unit prioritizes acquiring important weather information. For example, if the climber is relaxed, the acquisition unit acquires normal weather information. Furthermore, if the climber is in a hurry, the acquisition unit can prioritize acquiring weather information that needs to be acquired quickly. For example, the acquisition unit determines the priority of weather information to be acquired based on the climber's emotions. In this way, by determining the priority of weather information according to the climber's emotions, more important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the climber's emotion data into a generation AI, which can estimate the emotions and determine the priority.

[0119] When acquiring weather information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the geographical location information of the climber. The acquisition unit, for example, prioritizes acquiring relevant weather information based on the climber's current location. For example, the acquisition unit prioritizes acquiring weather information for a specific area based on the climber's climbing route. The acquisition unit can also prioritize acquiring weather information for a specific date based on the climber's climbing itinerary. For example, the acquisition unit prioritizes acquiring highly relevant information by taking into account the geographical location information of the climber when acquiring weather information. In this way, by taking into account the geographical location information of the climber, highly relevant weather information can be prioritized. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the climber into AI, which then prioritizes acquiring highly relevant information.

[0120] The acquisition unit can analyze the climber's social media activity and acquire related information when acquiring weather information. For example, the acquisition unit acquires weather information related to a location where the climber has checked in on social media. For example, the acquisition unit analyzes the climber's social media posts to acquire related weather information. The acquisition unit can also acquire related weather information by referring to the activity of the climber's friends on social media. For example, the acquisition unit analyzes the climber's social media activity and acquires related information when acquiring weather information. In this way, related weather information can be acquired by analyzing the climber's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's social media data into AI, which then acquires related information.

[0121] When acquiring weather information, the acquisition unit can customize the acquisition method by reflecting the climber's past feedback. For example, the acquisition unit preferentially suggests weather information acquisition methods that the climber has previously preferred. For example, the acquisition unit customizes the weather information acquisition interface based on the climber's past feedback. The acquisition unit can also optimize the weather information acquisition procedure by reflecting the climber's past feedback. For example, the acquisition unit customizes the acquisition method by reflecting the climber's past feedback when acquiring weather information. In this way, the weather information acquisition method can be customized by reflecting the climber's past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the climber's feedback data into AI, which can customize the acquisition method.

[0122] The collection unit can estimate the climber's emotions and adjust the collection method of past accident cases based on the estimated climber's emotions. For example, if the climber is nervous, the collection unit collects and provides detailed accident cases. For example, if the climber is relaxed, the collection unit collects and provides simple accident cases. Furthermore, if the climber is in a hurry, the collection unit can prioritize collecting accident cases that need to be collected quickly. For example, the collection unit adjusts the collection method of past accident cases based on the climber's emotions. In this way, by adjusting the collection method of past accident cases according to the climber's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the climber's emotion data into the generation AI, which can estimate the emotion and adjust the collection method.

[0123] When collecting past cases of accidents, the collection unit can analyze the climber's past climbing history and select the optimal collection method. The collection unit selects the optimal collection method, for example, based on the risks the climber has encountered in the past. For example, the collection unit prioritizes collecting relevant cases of accidents from the climber's past climbing history. The collection unit can also analyze the climber's past climbing history and select the most efficient collection method. For example, when collecting past cases of accidents, the collection unit analyzes the climber's past climbing history and selects the optimal collection method. In this way, by analyzing the climber's past climbing history, the optimal collection method for collecting cases of accidents can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climber's past climbing history data into AI, which selects the optimal collection method.

[0124] The collection unit can filter past accident cases based on the climber's current climbing plan when collecting them. The collection unit, for example, prioritizes collecting relevant accident cases based on the climber's current climbing plan. For example, the collection unit collects accident cases in a specific area based on the climber's climbing route. The collection unit can also collect accident cases on a specific date based on the climber's climbing itinerary. For example, the collection unit filters past accident cases based on the climber's current climbing plan when collecting them. In this way, filtering based on the climber's current climbing plan can provide more relevant accident cases. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input climbing plan data into AI, which then performs the filtering.

[0125] The collection unit can select the optimal collection means according to the climber's input method when collecting past accident cases. For example, if the climber collects accident cases by voice, the collection unit provides information using voice recognition technology. For example, if the climber collects accident cases by text, the collection unit provides text-based information. Furthermore, if the climber collects accident cases by image, the collection unit can also provide visual information. For example, the collection unit selects the optimal collection means according to the climber's input method when collecting past accident cases. This allows for smooth collection of accident cases by selecting the optimal collection means according to the climber's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climber's input data into AI, which then selects the optimal collection means.

[0126] The collection unit can estimate the climber's emotions and determine the priority of the distress cases to be collected based on the estimated emotions of the climber. For example, if the climber is nervous, the collection unit prioritizes collecting important distress cases. For example, if the climber is relaxed, the collection unit collects normal distress cases. Furthermore, if the climber is in a hurry, the collection unit can prioritize collecting distress cases that need to be collected quickly. For example, the collection unit determines the priority of the distress cases to be collected based on the climber's emotions. In this way, by prioritizing distress cases according to the climber's emotions, more important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the climber's emotion data into a generation AI, which can estimate the emotions and determine the priority.

[0127] When collecting past cases of mountain climbers' accidents, the collection unit can prioritize collecting highly relevant cases by taking into account the geographical location information of the climbers. The collection unit, for example, prioritizes collecting related cases of mountain climbers' accidents based on the climbers' current location. For example, the collection unit prioritizes collecting cases of mountain climbers' accidents in a specific area based on the climbers' climbing route. The collection unit can also prioritize collecting cases of mountain climbers' accidents on a specific date based on the climbers' climbing schedule. For example, when collecting past cases of mountain climbers' accidents, the collection unit prioritizes collecting highly relevant cases by taking into account the climbers' geographical location information. In this way, by taking into account the climbers' geographical location information, highly relevant cases of mountain climbers' accidents can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climbers' geographical location information into AI, which can then prioritize collecting highly relevant cases.

[0128] The collection unit can analyze the social media activities of climbers when collecting past cases of mountaineering accidents and collect related cases. For example, the collection unit collects cases of mountaineering accidents related to places where climbers have checked in on social media. For example, the collection unit analyzes the content of climbers' social media posts and collects related cases of mountaineering accidents. The collection unit can also collect related cases of mountaineering accidents by referring to the activities of the climbers' friends on social media. For example, the collection unit analyzes the social media activities of climbers when collecting past cases of mountaineering accidents and collects related cases. In this way, related cases of mountaineering accidents can be collected by analyzing the climbers' social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the climbers' social media data into AI, which can collect related cases.

[0129] The collection unit can customize the collection method by reflecting the climber's past feedback when collecting past accident cases. The collection unit, for example, preferentially suggests collection methods that the climber has previously preferred. For example, the collection unit customizes the accident case collection interface based on the climber's past feedback. The collection unit can also optimize the accident case collection procedure by reflecting the climber's past feedback. For example, the collection unit customizes the collection method by reflecting the climber's past feedback when collecting past accident cases. In this way, the collection method for accident cases can be customized by reflecting the climber's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the climber's feedback data into AI, which can customize the collection method.

[0130] The warning unit can estimate the climber's emotions and adjust the way the warning is presented based on the estimated emotions. For example, if the climber is nervous, the warning unit provides a simple, highly visible warning. For example, if the climber is relaxed, the warning unit provides a warning with detailed information. Furthermore, if the climber is in a hurry, the warning unit can provide a warning that focuses on the main points. For example, the warning unit adjusts the way the warning is presented based on the climber's emotions. This allows for a more appropriate warning to be provided by adjusting the way the warning is presented according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the warning unit can be performed using AI, for example, or without AI. For example, the warning unit can input the climber's emotion data into the generation AI, which can then estimate the emotion and adjust the way the warning is presented.

[0131] The warning unit can adjust the level of detail of the warning based on the severity of the risk when issuing a warning. For example, the warning unit provides a detailed warning when the risk is high. For example, the warning unit provides a simple warning when the risk is low. The warning unit can also adjust the level of detail of the warning based on the severity of the risk. In this way, by adjusting the level of detail of the warning based on the severity of the risk, a more appropriate warning can be issued. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input risk assessment data into AI, and the AI ​​can adjust the level of detail of the warning.

[0132] When issuing a warning, the warning unit can apply different warning algorithms depending on the risk category. For example, the warning unit issues a weather-based warning for weather risk. For example, the warning unit issues a terrain-based warning for terrain risk. The warning unit can also issue a warning to prevent distress for distress risk. For example, the warning unit applies different warning algorithms depending on the risk category. This allows for more appropriate warnings to be issued by applying different warning algorithms depending on the risk category. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, AI, for example. For example, the warning unit can input risk category data into AI, and the AI ​​can apply different warning algorithms.

[0133] When issuing a warning, the warning unit can improve the accuracy of the warning by referring to the climber's past warning results. The warning unit improves the accuracy of the warning, for example, based on warnings the climber has received in the past. For example, the warning unit analyzes the climber's past warning results and optimizes the warning algorithm. The warning unit can also adjust the warning criteria by referring to the climber's past warning results. For example, the warning unit improves the accuracy of the warning by referring to the climber's past warning results. In this way, the accuracy of the warning is improved by referring to the climber's past warning results. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the climber's past warning data into AI, which can improve the accuracy of the warning.

[0134] The warning unit can estimate the climber's emotions and adjust the length of the warning based on the estimated emotions. For example, if the climber is nervous, the warning unit provides a short and to-the-point warning. For example, if the climber is relaxed, the warning unit provides a longer warning with detailed explanations. The warning unit can also provide a quick and concise warning if the climber is in a hurry. For example, the warning unit adjusts the length of the warning based on the climber's emotions. This allows for a more appropriate warning by adjusting the length of the warning according to the climber's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit can be performed using AI, for example, or without AI. For example, the warning unit can input the climber's emotion data into the generation AI, which can estimate the emotion and adjust the length of the warning.

[0135] When issuing a warning, the warning unit can determine the priority of the warning based on the time of risk submission. For example, if the risk is submitted early, the warning unit provides a detailed warning. For example, if the risk is submitted last minute, the warning unit provides a quick warning. The warning unit can also adjust the priority of the warning based on the time of risk submission. For example, the warning unit determines the priority of the warning based on the time of risk submission. This allows for more appropriate warnings by determining the priority of the warning based on the time of risk submission. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input risk submission time data into AI, which can then determine the priority.

[0136] The warning unit can adjust the order of warnings based on the relevance of risks when issuing a warning. For example, the warning unit prioritizes warnings when the risk is highly relevant to past distress cases. For example, the warning unit prioritizes warnings when the risk is highly relevant to weather information. The warning unit can also adjust the order of warnings based on the relevance of risks. For example, the warning unit adjusts the order of warnings based on the relevance of risks. In this way, by adjusting the order of warnings based on the relevance of risks, more appropriate warnings can be issued. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input risk relevance data into AI, which can adjust the order of warnings.

[0137] When issuing a warning, the warning unit can adjust the use of technical terms in the warning depending on the climber's level of expertise. For example, the warning unit provides a warning in simple language to a beginner climber. For example, the warning unit can provide a warning using standard technical terms to an intermediate climber. The warning unit can also provide a warning using detailed technical terms to an advanced climber. For example, the warning unit adjusts the use of technical terms in the warning depending on the climber's level of expertise. This allows for a more appropriate warning to be issued by adjusting the use of technical terms in the warning depending on the climber's level of expertise. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input the climber's level of expertise data into AI, which can then adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, suggestion unit, acquisition unit, collection unit, and warning unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can accept input of a mountain climbing plan using the reception device 38 of the smart device 14. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates risks using AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate equipment and preparations. The acquisition unit acquires weather information via the communication I / F 26 of the data processing device 12. The collection unit collects past cases of mountain accidents from the database 24 of the data processing device 12. The warning unit issues a warning based on the risk assessment by the specific processing unit 290 of the data processing device 12. The reception unit can estimate the climber's emotions using the control unit 46A of the smart device 14 and adjust the input interface. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, suggestion unit, acquisition unit, collection unit, and warning unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can accept input of a mountain climbing plan using the microphone 238 of the smart glasses 214. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates risks using AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate equipment and preparations. The acquisition unit acquires weather information via the communication I / F 26 of the data processing device 12. The collection unit collects past cases of disaster from the database 24 of the data processing device 12. The warning unit issues a warning based on the risk assessment by the specific processing unit 290 of the data processing device 12. The reception unit can estimate the climber's emotions using the control unit 46A of the smart glasses 214 and adjust the input interface. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, suggestion unit, acquisition unit, collection unit, and warning unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can accept input of a mountain climbing plan using the microphone 238 of the headset-type terminal 314. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates risks using AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate equipment and preparations. The acquisition unit acquires weather information via the communication I / F 26 of the data processing device 12. The collection unit collects past cases of mountain accidents from the database 24 of the data processing device 12. The warning unit issues a warning based on the risk assessment by the specific processing unit 290 of the data processing device 12. The reception unit can estimate the climber's emotions using the control unit 46A of the headset-type terminal 314 and adjust the input interface. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, suggestion unit, acquisition unit, collection unit, and warning unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can accept input of a mountain climbing plan using the microphone 238 of the robot 414. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates risks using AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate equipment and preparations. The acquisition unit acquires weather information via the communication I / F 26 of the data processing device 12. The collection unit collects past cases of mountain accidents from the database 24 of the data processing device 12. The warning unit issues a warning based on the risk assessment by the specific processing unit 290 of the data processing device 12. The reception unit can estimate the climber's emotions using the control unit 46A of the robot 414 and adjust the input interface.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The mountain climbing support system can further include a physical fitness acquisition unit that acquires the climber's physical fitness data. The physical fitness acquisition unit acquires data such as the climber's heart rate, oxygen saturation, and number of steps. This allows the climber's physical fitness status to be grasped in real time and appropriate advice to be provided. For example, the physical fitness acquisition unit can suggest taking a break if the climber's heart rate is high. It can also warn of the risk of altitude sickness if the oxygen saturation level drops. Furthermore, it can grasp the climber's progress based on the number of steps data and evaluate whether the climber is progressing as planned.

[0140] The mountain climbing support system can further include an emotion evaluation unit that estimates the emotions of the climber and adjusts the risk assessment of the mountain climbing plan based on the estimated emotions. For example, the emotion evaluation unit sets a strict risk assessment if the climber is feeling anxious. For example, if the climber is relaxed, the emotion evaluation unit sets a lenient risk assessment. Also, if the climber is excited, the emotion evaluation unit can quickly perform a risk assessment. In this way, a more appropriate risk assessment can be performed by adjusting the risk assessment according to the climber's emotions.

[0141] The mountain climbing support system can further include an experience assessment unit that analyzes the climber's past mountain climbing experience and adjusts the risk assessment based on the experience. For example, if the climber has a lot of past mountain climbing experience, the experience assessment unit sets a lenient risk assessment. For example, if the climber is a beginner, the experience assessment unit sets a strict risk assessment. Also, if the climber has a lot of experience in a particular area, the risk assessment for that area can be adjusted. In this way, by adjusting the risk assessment based on the climber's past experience, a more appropriate risk assessment can be performed.

[0142] The mountain climbing support system can further include an emotion suggestion unit that estimates the emotion of the climber and adjusts the proposed mountain climbing plan based on the estimated emotion. For example, if the climber is feeling anxious, the emotion suggestion unit provides a simple and highly visible suggestion. For example, if the climber is relaxed, the suggestion unit provides a suggestion that includes detailed information. Also, if the climber is in a hurry, the suggestion unit can provide a suggestion that focuses on the main points. In this way, more appropriate suggestions can be made by adjusting the suggestion content according to the climber's emotion.

[0143] The mountain climbing support system can further include a physical condition assessment unit that acquires the climber's physical condition data and adjusts the risk assessment based on the climber's physical condition. For example, the physical condition assessment unit sets a strict risk assessment if the climber is tired. For example, if the climber is in good health, the physical condition assessment unit sets a lenient risk assessment. Also, if the climber is in poor physical condition, the risk assessment can be performed quickly. In this way, a more appropriate risk assessment can be performed by adjusting the risk assessment according to the climber's physical condition.

[0144] The mountain climbing support system can further include an emotion warning unit that estimates the emotion of the climber and adjusts the way the warning is expressed based on the estimated emotion. For example, if the climber is nervous, the emotion warning unit provides a simple, highly visible warning. For example, if the climber is relaxed, the emotion warning unit provides a warning with detailed information. Also, if the climber is in a hurry, the emotion warning unit can provide a warning that focuses on the main points. In this way, by adjusting the way the warning is expressed based on the emotion of the climber, more appropriate warnings can be provided.

[0145] The mountain climbing support system can further include a feedback suggestion unit that customizes the content of suggestions by reflecting the climber's past feedback. The feedback suggestion unit, for example, preferentially provides suggestion methods that the climber has previously preferred. For example, the suggestion interface is customized based on the climber's past feedback. The suggestion procedure can also be optimized by reflecting the climber's past feedback. In this way, the content of suggestions can be customized by reflecting the climber's past feedback.

[0146] The mountain climbing support system can further include an emotion / weather acquisition unit that estimates the emotion of the climber and adjusts the timing of weather information acquisition based on the estimated emotion. For example, if the climber is nervous, the emotion / weather acquisition unit acquires weather information frequently to provide the latest information. For example, if the climber is relaxed, the weather information is acquired at a normal frequency. Also, if the climber is in a hurry, the weather information can be acquired and provided quickly. In this way, more appropriate information can be provided by adjusting the timing of weather information acquisition according to the climber's emotion.

[0147] The mountain climbing support system can further include a history suggestion unit that analyzes the climber's past climbing history and adjusts the content of suggestions based on the history. The history suggestion unit, for example, prioritizes suggestions based on routes that the climber has frequently used in the past. For example, it makes suggestions suitable for specific seasons or time periods based on the climber's past climbing history. It can also optimize suggestions for equipment and preparation based on the climber's past history. This allows the content of suggestions to be adjusted by analyzing the climber's past climbing history.

[0148] The mountain climbing support system can further include an emotion collection unit that estimates the emotions of the climber and determines the priority of distress cases to be collected based on the estimated emotions. For example, if the climber is nervous, the emotion collection unit will prioritize collecting important distress cases. For example, if the climber is relaxed, the emotion collection unit will collect ordinary distress cases. Also, if the climber is in a hurry, the emotion collection unit can prioritize collecting distress cases that need to be collected quickly. In this way, by determining the priority of distress cases according to the climber's emotions, more important information can be provided preferentially.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The reception unit receives input of a mountain climbing plan. The mountain climbing plan includes, but is not limited to, the starting point, destination, planned route, and climbing schedule. The reception unit stores the information entered by the climber in a database, for example, and uses it for subsequent processing. Step 2: The evaluation unit uses AI to analyze the mountain climbing plan accepted by the reception unit and evaluate the risks. The risk is evaluated based on, for example, weather information, topographical data, past cases of mountain climbing accidents, etc., but is not limited to these examples. For example, the evaluation unit obtains weather information and evaluates the possibility of the weather worsening on the planned mountain climbing day. The evaluation unit can also evaluate the risk based on past cases of mountain climbing accidents that occurred on the same route. Furthermore, the evaluation unit can analyze topographical data and evaluate the difficulty of the mountain climbing route. Step 3: The suggestion unit uses AI to suggest appropriate equipment and preparations based on the risks assessed by the evaluation unit. Examples of suggestions include, but are not limited to, suggesting the preparation of cold weather gear and rain gear when there is a possibility of worsening weather, or providing advice on appropriate equipment and physical training when the climbing route is steep. For example, the suggestion unit suggests the preparation of cold weather gear and rain gear to a climber. The suggestion unit can also provide advice on appropriate equipment and physical training when the climbing route is steep.

[0151] 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.

[0152] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0162] 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.

[0163] 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.

[0164] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0174] 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.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0178] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0179] 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.

[0180] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0190] 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.

[0191] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0192] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0194] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0195] 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.

[0196] 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.

[0197] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0198] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0199] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0200] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[0201] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0202] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0203] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0204] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0205] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0206] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0207] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0209] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0211] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0212] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0213] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0214] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0215] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0216] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0217] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0218] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0219] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0220] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0221] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a mountain climbing plan; an evaluation unit that analyzes the mountain climbing plan accepted by the acceptance unit and evaluates risks; a proposal unit that proposes equipment and preparations based on the risk evaluated by the evaluation unit. A system characterized by:

2. Equipped with an acquisition unit that acquires weather information 2. The system of claim 1.

3. Equipping a collection department to collect past cases of accidents 2. The system of claim 1.

4. Equipped with a warning unit that issues a warning based on risk assessment 2. The system of claim 1.

5. The evaluation unit Risk assessment based on weather information, terrain data, and past accident cases 2. The system of claim 1.

6. The proposal unit Recommend appropriate equipment and preparations based on risk assessment 2. The system of claim 1.

7. The proposal unit Advise climbers to prepare cold weather gear and rain gear.

2. The system of claim 1.

8. The proposal unit If the climbing route is difficult, we will provide advice on equipment and physical training.

2. The system of claim 1.

9. The reception unit Estimating the climber's emotions and adjusting the input interface for mountain climbing plans based on the estimated emotions 2. The system of claim 1.

Images