System

Abstract

An object of a system according to an embodiment is to enable a mountain climber to easily determine optimum equipment, a route, and a schedule.SOLUTION: A system includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects mountain climbing route information, weather forecast, past mountain climbing history, skill level, healthcare data, mountain climbing wear, and equipment. The analysis unit analyzes the mountain climbing route information, weather forecast, past mountain climbing history, skill level, healthcare data, mountaineering wear, and equipment collected by the information collection unit. The proposing section proposes an optimum mountain climbing route and schedule on the basis of a result of the analysis performed by the analyzing section.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] With conventional technology, it is difficult for climbers to decide for themselves the best equipment, route, and schedule, which can lead to inadequate preparation and problems on-site.

[0005] The system according to the embodiment aims to enable climbers to easily determine the optimal equipment, route, and schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a suggestion unit. The information collection unit collects hiking trail information, weather forecasts, past hiking history, skill level, healthcare data, hiking wear, and equipment. The analysis unit analyzes the hiking trail information, weather forecasts, past hiking history, skill level, healthcare data, hiking wear, and equipment collected by the information collection unit. The suggestion unit suggests an optimal hiking route and schedule based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable climbers to easily determine the optimal equipment, route, and schedule. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The mountain climbing support system according to the embodiment of the present invention is a system that uses AI to suggest optimal equipment, climbing routes, and schedules for busy businessmen who suddenly decide to go mountain climbing. This allows the mountain climbing support system to proceed with preparations efficiently and safely.

[0029] A mountain climbing support system according to an embodiment includes an information collection unit, an analysis unit, and a suggestion unit. The information collection unit collects mountain trail information, weather forecasts, past mountain climbing history, skill level, health data, mountain climbing wear, and equipment. For example, the information collection unit acquires mountain trail information from YAMAP or the like to understand the difficulty level and current conditions of the mountain climbing route. The information collection unit also acquires weather forecasts from Mountain Climbing Weather and suggests equipment appropriate for the weather. The information collection unit also generates an appropriate route and schedule taking into account the user's past mountain climbing history and skill level. The analysis unit analyzes the mountain trail information, weather forecasts, past mountain climbing history, skill level, health data, mountain climbing wear, and equipment collected by the information collection unit. For example, the analysis unit proposes a reasonable plan based on the user's physical condition based on the collected data. The suggestion unit proposes an optimal mountain climbing route and schedule based on the results of the analysis by the analysis unit. For example, the suggestion unit selects a reasonable route based on the user's skill level and physical condition. The suggestion unit also proposes appropriate rest points and time allocations based on the weather and mountain trail conditions. This allows the mountain climbing support system to efficiently and safely prepare for a busy businessman who needs to go mountain climbing on short notice.

[0030] The information collection unit acquires hiking trail information and is able to grasp the difficulty and current conditions of the hiking route. The information collection unit acquires hiking trail information, for example, from YAMAP, and grasps the difficulty and current conditions of the hiking route. For example, the information collection unit acquires hiking trail map information and elevation data and evaluates the difficulty of the route. The information collection unit can also grasp the hiking trail congestion situation and weather changes in real time. This allows for the selection of an appropriate route by understanding the difficulty and current conditions of the hiking route.

[0031] The information collecting unit can obtain a weather forecast and suggest equipment appropriate for the weather. For example, the information collecting unit obtains a weather forecast from a mountain climbing weather site and suggests equipment appropriate for the weather. For example, the information collecting unit lists necessary equipment, such as rain gear and cold weather gear, based on the weather forecast. The information collecting unit can also analyze meteorological data such as temperature and wind speed and suggest appropriate equipment. This allows for appropriate preparation by suggesting equipment appropriate for the weather.

[0032] The information collection unit can generate an appropriate route and schedule by taking into account the user's past climbing history and skill level. The information collection unit can generate an appropriate route and schedule by taking into account, for example, the user's past climbing history and skill level. For example, the information collection unit can analyze data such as past climbing dates, routes, and required time to evaluate the user's skill level. The information collection unit can also select a reasonable route that takes into account the user's technical skill and physical strength. This makes it possible to generate an appropriate route and schedule by taking into account the user's past climbing history and skill level.

[0033] The information collecting unit can analyze healthcare data and propose a reasonable plan based on the user's physical condition. The information collecting unit can analyze healthcare data such as the user's heart rate, blood pressure, and body temperature, and propose a reasonable plan based on the user's physical condition. For example, the information collecting unit can set appropriate timing for rest and hydration based on fluctuations in heart rate and blood pressure. The information collecting unit can also analyze changes in body temperature and propose appropriate clothing and equipment. This allows for safe mountain climbing by proposing a reasonable plan based on the user's physical condition.

[0034] The suggestion unit can provide more detailed route information by analyzing reviews and photos posted by past climbers in addition to hiking trail information. The suggestion unit can, for example, analyze reviews and photos posted by past climbers in addition to hiking trail information to provide detailed route information. For example, the suggestion unit can provide information including dangerous areas and recommended scenic spots. The suggestion unit can also analyze reviews posted by past climbers to more accurately grasp the difficulty and required time of a route. Furthermore, the suggestion unit can analyze photos posted by climbers to provide visual information about the route. In this way, more detailed route information can be provided by analyzing reviews and photos posted by past climbers.

[0035] The suggestion unit can combine the user's past climbing history and physical condition data to suggest optimal rest points and hydration timings. The suggestion unit, for example, analyzes the user's past climbing history and current physical condition data to suggest optimal rest points. For example, the suggestion unit avoids points where the user was prone to fatigue in the past and sets rest times according to the user's physical condition. The suggestion unit can also suggest hydration timings based on the physical condition data. For example, the suggestion unit calculates the appropriate amount of hydration based on the user's weight and temperature and sets the timing. Furthermore, the suggestion unit can combine the user's past climbing history and physical condition data to suggest a reasonable schedule. In this way, by combining the user's past climbing history and physical condition data, it is possible to suggest optimal rest points and hydration timings.

[0036] The information collection unit can use a drone to grasp the conditions of the hiking trail in real time and provide the latest information. For example, the information collection unit uses a drone to photograph the conditions of the hiking trail in real time and provide the latest information. For example, the information collection unit checks the congestion status and dangerous areas of the hiking trail using drone footage. The information collection unit can also analyze aerial photography data taken by the drone and provide the latest information on the hiking trail. For example, the information collection unit identifies collapsed areas and newly appeared obstacles in real time. Furthermore, the information collection unit can use a drone to check the weather conditions of the hiking trail in real time and suggest equipment and routes depending on the weather. In this way, by using a drone, the latest conditions of the hiking trail can be grasped in real time and appropriate information can be provided.

[0037] The suggestion unit can generate a customizable mountain climbing plan tailored to the user's preferences based on the mountain climbing information. The suggestion unit, for example, analyzes the user's past mountain climbing history and preferences to generate a customizable mountain climbing plan. For example, the suggestion unit suggests a route that includes the user's favorite scenery or activities. The suggestion unit can also generate a customizable plan tailored to the user's skill level and physical condition based on the mountain climbing information. For example, the suggestion unit suggests a route for beginners or a schedule tailored to the user's physical strength. Furthermore, the suggestion unit can also build a system that customizes the mountain climbing plan in real time to suit the user's preferences. For example, the suggestion unit adjusts the plan according to changes in weather and physical condition. In this way, a customizable mountain climbing plan tailored to the user's preferences can be generated, enabling a more satisfying mountain climbing experience.

[0038] The suggestion unit can suggest equipment that uses the latest technology and materials. The suggestion unit, for example, suggests mountaineering equipment that uses the latest technology and materials. For example, the suggestion unit selects equipment that uses lightweight and durable materials and the latest waterproof technology. The suggestion unit can also suggest equipment that incorporates the latest technology and materials when selecting mountaineering equipment. For example, the suggestion unit selects equipment that uses materials with temperature regulating functions and high breathability. Furthermore, the suggestion unit can also build a system that suggests equipment that uses the latest technology and materials. For example, the suggestion unit selects equipment based on the latest research results and technological trends. This makes it possible to suggest more comfortable and functional equipment by using the latest technology and materials.

[0039] The suggestion unit can suggest multifunctional equipment that takes into consideration use in other outdoor activities. For example, when selecting mountain climbing equipment, the suggestion unit suggests multifunctional equipment that can also be used in other outdoor activities, such as camping and fishing. For example, the suggestion unit selects tents, cooking gear, etc. The suggestion unit can also suggest multifunctional equipment that takes into consideration use in other outdoor activities. For example, the suggestion unit selects equipment that can be used not only for mountain climbing but also for camping and fishing. Furthermore, the suggestion unit can build a system that suggests multifunctional equipment and select equipment that can be used in outdoor activities other than mountain climbing. For example, the suggestion unit suggests highly versatile equipment. In this way, highly versatile equipment can be suggested by taking into consideration use in other outdoor activities.

[0040] The suggestion unit can suggest customizable equipment that matches the user's fashion sense and preferences. The suggestion unit, for example, suggests customizable mountain climbing equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that allows the user to choose the color or design. The suggestion unit can also suggest customizable equipment that matches the user's preferences when selecting mountain climbing equipment. For example, the suggestion unit selects equipment based on a favorite brand or style. Furthermore, the suggestion unit can also build a system that suggests customizable equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that reflects the user's preferences. This allows for more satisfying mountain climbing by suggesting customizable equipment that matches the user's fashion sense and preferences.

[0041] The suggestion unit can analyze data of past climbers and suggest the most popular climbing routes. The suggestion unit can, for example, analyze data of past climbers and suggest the most popular climbing routes. For example, the suggestion unit selects routes with high reviews and ratings. The suggestion unit can also identify and suggest popular routes based on climber data. For example, the suggestion unit selects routes by analyzing the frequency of climbers' visits and rating scores. Furthermore, the suggestion unit can also build a system that analyzes data of past climbers and suggests popular routes. For example, the suggestion unit selects routes based on climbers' reviews and photos. In this way, the most popular climbing routes can be suggested by analyzing data of past climbers.

[0042] The suggestion unit can suggest optimal rest timings and meal timings based on the physical condition data. The suggestion unit suggests optimal rest timings based on, for example, the user's physical condition data. For example, the suggestion unit sets rest points by analyzing the heart rate and fatigue level. The suggestion unit can also suggest meal timings based on the physical condition data. For example, the suggestion unit sets meal timings by analyzing energy consumption and blood sugar levels. Furthermore, the suggestion unit can also suggest a reasonable schedule based on the user's physical condition data. For example, the suggestion unit sets rest and meal timings according to the user's physical condition. In this way, optimal rest and meal timings can be suggested based on the user's physical condition data.

[0043] The suggestion unit can suggest a mountain climbing plan that combines other outdoor activities. For example, when suggesting a mountain climbing route, the suggestion unit suggests a plan that combines outdoor activities such as bird watching or stargazing. For example, the suggestion unit selects a route that includes a specific observation point. The suggestion unit can also suggest a mountain climbing plan that combines other outdoor activities. For example, the suggestion unit suggests a plan that combines mountain climbing and camping. Furthermore, the suggestion unit can also build a system that suggests mountain climbing plans that combine outdoor activities. For example, the suggestion unit selects a route that includes activities that match the user's interests. This makes it possible to suggest a mountain climbing plan that allows for more diverse enjoyment by combining other outdoor activities.

[0044] The suggestion unit can generate a customizable mountain climbing schedule that matches the user's preferences. The suggestion unit generates, for example, a customizable mountain climbing schedule that matches the user's preferences. For example, the suggestion unit suggests a schedule that includes favorite activities and rest points. The suggestion unit can also generate a customizable plan that matches the user's preferences when proposing a mountain climbing schedule. For example, the suggestion unit suggests a schedule that matches the user's favorite time period and pace. Furthermore, the suggestion unit can also build a system that customizes the mountain climbing schedule in real time to match the user's preferences. For example, the suggestion unit adjusts the schedule according to changes in weather and physical condition. In this way, a customizable mountain climbing schedule that matches the user's preferences can be generated, enabling a more satisfying mountain climbing experience.

[0045] The suggestion unit can analyze the profile information of climbers passing by and suggest appropriate greeting content. The suggestion unit, for example, analyzes the profile information of climbers passing by and suggests appropriate greeting content. For example, the suggestion unit sets greeting content according to age and gender. The suggestion unit can also build a system that suggests appropriate greeting content based on the climber's profile information. For example, the suggestion unit selects optimal greeting content based on past data. Furthermore, the suggestion unit can analyze the profile information of climbers passing by in real time and suggest appropriate greeting content. For example, the suggestion unit sets greeting content according to the climber's facial expression and attitude. In this way, appropriate greeting content can be suggested by analyzing the climber's profile information.

[0046] The suggestion unit can analyze the tone and tempo of the user's voice and generate an optimal greeting voice. The suggestion unit, for example, analyzes the tone and tempo of the user's voice and generates an optimal greeting voice. For example, the suggestion unit greets with a natural tone and tempo. The suggestion unit can also build a system that generates an optimal greeting voice based on the tone and tempo of the voice. For example, the suggestion unit generates a greeting voice that reflects the characteristics of the user's voice. Furthermore, the suggestion unit can analyze the tone and tempo of the user's voice in real time and generate an optimal greeting voice. For example, the suggestion unit adjusts the greeting voice according to changes in the user's voice while climbing a mountain. In this way, the optimal greeting voice can be generated by analyzing the tone and tempo of the user's voice.

[0047] The suggestion unit can also enable greetings in other languages, thereby facilitating communication with international climbers. The suggestion unit, for example, adds greetings in other languages ​​to the auto-konnichiwa function, thereby facilitating communication with international climbers. For example, the suggestion unit sets greetings in English and Chinese. The suggestion unit can also build a system that enables greetings in other languages, thereby facilitating communication with international climbers. For example, the suggestion unit provides a greeting according to the user's language setting. Furthermore, the suggestion unit can add multilingual support to the auto-konnichiwa function, thereby facilitating communication with international climbers. For example, the suggestion unit provides a greeting according to the climber's nationality. This allows greetings in other languages ​​to be provided, thereby facilitating communication with international climbers.

[0048] The suggestion unit can enable not only greetings but also simple conversations and information exchanges. For example, the suggestion unit adds a simple conversation and information exchange function to the auto-conversation function, thereby promoting communication between climbers. For example, the suggestion unit exchanges weather and route information. The suggestion unit can also build a system that enables not only greetings but also simple conversations and information exchanges. For example, the suggestion unit automatically asks and answers questions between climbers. Furthermore, the suggestion unit can add an information exchange function to the auto-conversation function, thereby promoting communication between climbers. For example, the suggestion unit exchanges information about the status of hiking trails and recommended spots. This allows not only greetings but also simple conversations and information exchanges, thereby promoting communication between climbers.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The suggestion unit can provide more detailed route information by analyzing reviews and photos posted by past climbers in addition to hiking trail information. For example, the suggestion unit can provide detailed route information by analyzing reviews and photos posted by past climbers in addition to hiking trail information. For example, the suggestion unit can provide information including dangerous areas and recommended scenic spots. The suggestion unit can also analyze reviews posted by past climbers to more accurately grasp the difficulty and required time of a route. Furthermore, the suggestion unit can analyze photos posted by climbers to provide visual information about the route. In this way, more detailed route information can be provided by analyzing reviews and photos posted by past climbers.

[0051] The suggestion unit can combine the user's past climbing history and physical condition data to suggest optimal rest points and hydration timings. For example, the suggestion unit analyzes the user's past climbing history and current physical condition data to suggest optimal rest points. For example, the suggestion unit avoids points where the user was prone to fatigue in the past and sets rest times according to their physical condition. The suggestion unit can also suggest hydration timings based on the physical condition data. For example, the suggestion unit calculates the appropriate amount of hydration based on the user's weight and temperature and sets the timing. Furthermore, the suggestion unit can combine the user's past climbing history and physical condition data to suggest a reasonable schedule. In this way, the suggestion unit can suggest optimal rest points and hydration timings by combining the user's past climbing history and physical condition data.

[0052] The information collection unit can use a drone to grasp the conditions of a hiking trail in real time and provide the latest information. For example, the information collection unit can use a drone to photograph the conditions of a hiking trail in real time and provide the latest information. For example, the information collection unit can check the congestion status and dangerous areas of a hiking trail using drone footage. The information collection unit can also analyze aerial photography data taken by a drone and provide the latest information on a hiking trail. For example, the information collection unit can grasp collapsed areas and newly appeared obstacles in real time. Furthermore, the information collection unit can use a drone to check the weather conditions of a hiking trail in real time and suggest equipment and routes depending on the weather. In this way, by using a drone, the latest conditions of a hiking trail can be grasped in real time and appropriate information can be provided.

[0053] The suggestion unit can generate a customizable mountain climbing schedule that matches the user's preferences. For example, the suggestion unit generates a customizable mountain climbing schedule that matches the user's preferences. For example, the suggestion unit suggests a schedule that includes favorite activities and rest points. The suggestion unit can also generate a customizable plan that matches the user's preferences when proposing a mountain climbing schedule. For example, the suggestion unit suggests a schedule that matches the user's favorite time period and pace. Furthermore, the suggestion unit can also build a system that customizes the mountain climbing schedule in real time to match the user's preferences. For example, the suggestion unit adjusts the schedule according to changes in weather and physical condition. In this way, a customizable mountain climbing schedule that matches the user's preferences can be generated, enabling a more satisfying mountain climbing experience.

[0054] The proposal unit can propose equipment that uses the latest technology and materials. For example, the proposal unit proposes mountain climbing equipment that uses the latest technology and materials. For example, the proposal unit selects equipment that uses lightweight and durable materials and the latest waterproof technology. The proposal unit can also propose equipment that incorporates the latest technology and materials when selecting mountain climbing equipment. For example, the proposal unit selects equipment that uses materials with temperature regulating functions and high breathability. Furthermore, the proposal unit can build a system that proposes equipment that uses the latest technology and materials. For example, the proposal unit selects equipment based on the latest research results and technological trends. This makes it possible to propose more comfortable and functional equipment by using the latest technology and materials.

[0055] The suggestion unit can suggest multifunctional equipment that takes into consideration use in other outdoor activities as well. For example, when selecting mountain climbing equipment, the suggestion unit suggests multifunctional equipment that can also be used in other outdoor activities such as camping and fishing. For example, the suggestion unit selects tents, cooking gear, etc. The suggestion unit can also suggest multifunctional equipment that takes into consideration use in other outdoor activities as well. For example, the suggestion unit selects equipment that can be used not only for mountain climbing but also for camping and fishing. Furthermore, the suggestion unit can build a system that suggests multifunctional equipment and select equipment that can be used in outdoor activities other than mountain climbing. For example, the suggestion unit suggests highly versatile equipment. In this way, highly versatile equipment can be suggested by taking into consideration use in other outdoor activities as well.

[0056] The suggestion unit can suggest customizable equipment that matches the user's fashion sense and preferences. For example, the suggestion unit suggests customizable mountain climbing equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that allows the user to choose the color or design. The suggestion unit can also suggest customizable equipment that matches the user's preferences when selecting mountain climbing equipment. For example, the suggestion unit selects equipment based on a favorite brand or style. Furthermore, the suggestion unit can also build a system that suggests customizable equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that reflects the user's preferences. This allows for more satisfying mountain climbing by suggesting customizable equipment that matches the user's fashion sense and preferences.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information collection unit collects hiking trail information, weather forecasts, past hiking history, skill level, health care data, hiking wear, and equipment. For example, the information collection unit obtains hiking trail information from YAMAP or similar sources to understand the difficulty of the hiking route and the current conditions. The information collection unit also obtains weather forecasts from Hiking Weather and suggests equipment appropriate for the weather. Furthermore, the information collection unit generates an appropriate route and schedule, taking into account the user's past hiking history and skill level. Step 2: The analysis unit analyzes the hiking trail information, weather forecast, past hiking history, skill level, health care data, hiking wear, and equipment collected by the information collection unit. For example, the analysis unit may propose a reasonable hiking plan based on the user's physical condition based on the collected data. Step 3: The suggestion unit proposes an optimal climbing route and schedule based on the results of the analysis by the analysis unit. For example, the suggestion unit selects a reasonable route taking into account the user's skill level and physical condition. The suggestion unit also suggests appropriate rest points and time allocations depending on the weather and trail conditions.

[0059] (Example 2) The mountain climbing support system according to the embodiment of the present invention is a system that uses AI to suggest optimal equipment, climbing routes, and schedules for busy businessmen who suddenly decide to go mountain climbing. This allows the mountain climbing support system to proceed with preparations efficiently and safely.

[0060] A mountain climbing support system according to an embodiment includes an information collection unit, an analysis unit, and a suggestion unit. The information collection unit collects mountain trail information, weather forecasts, past mountain climbing history, skill level, health data, mountain climbing wear, and equipment. For example, the information collection unit acquires mountain trail information from YAMAP or the like to understand the difficulty level and current conditions of the mountain climbing route. The information collection unit also acquires weather forecasts from Mountain Climbing Weather and suggests equipment appropriate for the weather. The information collection unit also generates an appropriate route and schedule taking into account the user's past mountain climbing history and skill level. The analysis unit analyzes the mountain trail information, weather forecasts, past mountain climbing history, skill level, health data, mountain climbing wear, and equipment collected by the information collection unit. For example, the analysis unit proposes a reasonable plan based on the user's physical condition based on the collected data. The suggestion unit proposes an optimal mountain climbing route and schedule based on the results of the analysis by the analysis unit. For example, the suggestion unit selects a reasonable route based on the user's skill level and physical condition. The suggestion unit also proposes appropriate rest points and time allocations based on the weather and mountain trail conditions. This allows the mountain climbing support system to efficiently and safely prepare for a busy businessman who needs to go mountain climbing on short notice.

[0061] The information collection unit acquires hiking trail information and is able to grasp the difficulty and current conditions of the hiking route. The information collection unit acquires hiking trail information, for example, from YAMAP, and grasps the difficulty and current conditions of the hiking route. For example, the information collection unit acquires hiking trail map information and elevation data and evaluates the difficulty of the route. The information collection unit can also grasp the hiking trail congestion situation and weather changes in real time. This allows for the selection of an appropriate route by understanding the difficulty and current conditions of the hiking route.

[0062] The information collecting unit can obtain a weather forecast and suggest equipment appropriate for the weather. For example, the information collecting unit obtains a weather forecast from a mountain climbing weather site and suggests equipment appropriate for the weather. For example, the information collecting unit lists necessary equipment, such as rain gear and cold weather gear, based on the weather forecast. The information collecting unit can also analyze meteorological data such as temperature and wind speed and suggest appropriate equipment. This allows for appropriate preparation by suggesting equipment appropriate for the weather.

[0063] The information collection unit can generate an appropriate route and schedule by taking into account the user's past climbing history and skill level. The information collection unit can generate an appropriate route and schedule by taking into account, for example, the user's past climbing history and skill level. For example, the information collection unit can analyze data such as past climbing dates, routes, and required time to evaluate the user's skill level. The information collection unit can also select a reasonable route that takes into account the user's technical skill and physical strength. This makes it possible to generate an appropriate route and schedule by taking into account the user's past climbing history and skill level.

[0064] The information collecting unit can analyze healthcare data and propose a reasonable plan based on the user's physical condition. The information collecting unit can analyze healthcare data such as the user's heart rate, blood pressure, and body temperature, and propose a reasonable plan based on the user's physical condition. For example, the information collecting unit can set appropriate timing for rest and hydration based on fluctuations in heart rate and blood pressure. The information collecting unit can also analyze changes in body temperature and propose appropriate clothing and equipment. This allows for safe mountain climbing by proposing a reasonable plan based on the user's physical condition.

[0065] The suggestion unit can estimate the user's emotions and suggest climbing routes and schedules that will elicit positive emotions. For example, the generation AI estimates the user's emotions and suggests climbing routes and schedules that will elicit positive emotions. For example, the generation AI analyzes the user's past climbing history and current emotional state to suggest climbing routes that will elicit positive emotions. The generation AI can also suggest schedules to reduce stress based on the user's emotional data. Furthermore, the generation AI can use the emotion estimation function to suggest climbing activities that the user will enjoy most. This makes climbing more enjoyable by making suggestions that take the user's emotions into consideration.

[0066] The suggestion unit can provide more detailed route information by analyzing reviews and photos posted by past climbers in addition to hiking trail information. The suggestion unit can, for example, analyze reviews and photos posted by past climbers in addition to hiking trail information to provide detailed route information. For example, the suggestion unit can provide information including dangerous areas and recommended scenic spots. The suggestion unit can also analyze reviews posted by past climbers to more accurately grasp the difficulty and required time of a route. Furthermore, the suggestion unit can analyze photos posted by climbers to provide visual information about the route. In this way, more detailed route information can be provided by analyzing reviews and photos posted by past climbers.

[0067] The suggestion unit can combine the user's past climbing history and physical condition data to suggest optimal rest points and hydration timings. The suggestion unit, for example, analyzes the user's past climbing history and current physical condition data to suggest optimal rest points. For example, the suggestion unit avoids points where the user was prone to fatigue in the past and sets rest times according to the user's physical condition. The suggestion unit can also suggest hydration timings based on the physical condition data. For example, the suggestion unit calculates the appropriate amount of hydration based on the user's weight and temperature and sets the timing. Furthermore, the suggestion unit can combine the user's past climbing history and physical condition data to suggest a reasonable schedule. In this way, by combining the user's past climbing history and physical condition data, it is possible to suggest optimal rest points and hydration timings.

[0068] The information collection unit can use a drone to grasp the conditions of the hiking trail in real time and provide the latest information. For example, the information collection unit uses a drone to photograph the conditions of the hiking trail in real time and provide the latest information. For example, the information collection unit checks the congestion status and dangerous areas of the hiking trail using drone footage. The information collection unit can also analyze aerial photography data taken by the drone and provide the latest information on the hiking trail. For example, the information collection unit identifies collapsed areas and newly appeared obstacles in real time. Furthermore, the information collection unit can use a drone to check the weather conditions of the hiking trail in real time and suggest equipment and routes depending on the weather. In this way, by using a drone, the latest conditions of the hiking trail can be grasped in real time and appropriate information can be provided.

[0069] The suggestion unit can generate a customizable mountain climbing plan tailored to the user's preferences based on the mountain climbing information. The suggestion unit, for example, analyzes the user's past mountain climbing history and preferences to generate a customizable mountain climbing plan. For example, the suggestion unit suggests a route that includes the user's favorite scenery or activities. The suggestion unit can also generate a customizable plan tailored to the user's skill level and physical condition based on the mountain climbing information. For example, the suggestion unit suggests a route for beginners or a schedule tailored to the user's physical strength. Furthermore, the suggestion unit can also build a system that customizes the mountain climbing plan in real time to suit the user's preferences. For example, the suggestion unit adjusts the plan according to changes in weather and physical condition. In this way, a customizable mountain climbing plan tailored to the user's preferences can be generated, enabling a more satisfying mountain climbing experience.

[0070] The suggestion unit can use the emotion estimation function to suggest the mountain climbing activity that the user will enjoy most. The suggestion unit, for example, uses the emotion estimation function to suggest the mountain climbing activity that the user will enjoy most. For example, the suggestion unit selects photo spots and nature observation points based on past emotion data. The suggestion unit can also analyze the user's emotion data and suggest activities that will elicit positive emotions. For example, the suggestion unit selects routes that include relaxing places and interesting nature observation points. Furthermore, the suggestion unit can use the emotion estimation function to build a system that suggests the activity that the user will enjoy most in real time. For example, the suggestion unit adjusts the activity according to changes in emotion during mountain climbing. In this way, the emotion estimation function can be used to suggest the mountain climbing activity that the user will enjoy most.

[0071] The suggestion unit can suggest equipment that uses the latest technology and materials. The suggestion unit, for example, suggests mountaineering equipment that uses the latest technology and materials. For example, the suggestion unit selects equipment that uses lightweight and durable materials and the latest waterproof technology. The suggestion unit can also suggest equipment that incorporates the latest technology and materials when selecting mountaineering equipment. For example, the suggestion unit selects equipment that uses materials with temperature regulating functions and high breathability. Furthermore, the suggestion unit can also build a system that suggests equipment that uses the latest technology and materials. For example, the suggestion unit selects equipment based on the latest research results and technological trends. This makes it possible to suggest more comfortable and functional equipment by using the latest technology and materials.

[0072] The suggestion unit can suggest multifunctional equipment that takes into consideration use in other outdoor activities. For example, when selecting mountain climbing equipment, the suggestion unit suggests multifunctional equipment that can also be used in other outdoor activities, such as camping and fishing. For example, the suggestion unit selects tents, cooking gear, etc. The suggestion unit can also suggest multifunctional equipment that takes into consideration use in other outdoor activities. For example, the suggestion unit selects equipment that can be used not only for mountain climbing but also for camping and fishing. Furthermore, the suggestion unit can build a system that suggests multifunctional equipment and select equipment that can be used in outdoor activities other than mountain climbing. For example, the suggestion unit suggests highly versatile equipment. In this way, highly versatile equipment can be suggested by taking into consideration use in other outdoor activities.

[0073] The suggestion unit can suggest customizable equipment that matches the user's fashion sense and preferences. The suggestion unit, for example, suggests customizable mountain climbing equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that allows the user to choose the color or design. The suggestion unit can also suggest customizable equipment that matches the user's preferences when selecting mountain climbing equipment. For example, the suggestion unit selects equipment based on a favorite brand or style. Furthermore, the suggestion unit can also build a system that suggests customizable equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that reflects the user's preferences. This allows for more satisfying mountain climbing by suggesting customizable equipment that matches the user's fashion sense and preferences.

[0074] The suggestion unit can use the emotion estimation function to suggest an equipment combination that makes the user feel most comfortable. The suggestion unit, for example, uses the emotion estimation function to suggest an equipment combination that makes the user feel most comfortable. For example, the suggestion unit selects optimal equipment based on past emotion data. The suggestion unit can also analyze the user's emotion data and suggest an equipment combination that prioritizes comfort. For example, the suggestion unit suggests a combination of equipment with a high emotion score. Furthermore, the suggestion unit can use the emotion estimation function to build a system that suggests an equipment combination that makes the user feel most comfortable in real time. For example, the suggestion unit adjusts the equipment according to emotional changes during mountain climbing. In this way, the emotion estimation function can be used to suggest an equipment combination that makes the user feel most comfortable.

[0075] The suggestion unit can estimate the user's emotions and suggest mountain climbing routes and schedules that will elicit positive emotions. For example, the suggestion unit uses a generation AI to analyze the user's emotional data and suggest mountain climbing routes that will elicit positive emotions. For example, the suggestion unit selects routes that the user has previously enjoyed or routes that include scenic spots. The suggestion unit can also suggest schedules to reduce stress based on the user's emotional data. For example, the suggestion unit sets relaxing rest stops and rest times at scenic spots. Furthermore, the suggestion unit can use the emotion estimation function to suggest mountain climbing activities that the user will enjoy most. For example, the suggestion unit selects routes that include photo spots and nature observation points. This allows for more enjoyable mountain climbing through suggestions that take the user's emotions into consideration.

[0076] The suggestion unit can analyze data of past climbers and suggest the most popular climbing routes. The suggestion unit can, for example, analyze data of past climbers and suggest the most popular climbing routes. For example, the suggestion unit selects routes with high reviews and ratings. The suggestion unit can also identify and suggest popular routes based on climber data. For example, the suggestion unit selects routes by analyzing the frequency of climbers' visits and rating scores. Furthermore, the suggestion unit can also build a system that analyzes data of past climbers and suggests popular routes. For example, the suggestion unit selects routes based on climbers' reviews and photos. In this way, the most popular climbing routes can be suggested by analyzing data of past climbers.

[0077] The suggestion unit can suggest optimal rest timings and meal timings based on the physical condition data. The suggestion unit suggests optimal rest timings based on, for example, the user's physical condition data. For example, the suggestion unit sets rest points by analyzing the heart rate and fatigue level. The suggestion unit can also suggest meal timings based on the physical condition data. For example, the suggestion unit sets meal timings by analyzing energy consumption and blood sugar levels. Furthermore, the suggestion unit can also suggest a reasonable schedule based on the user's physical condition data. For example, the suggestion unit sets rest and meal timings according to the user's physical condition. In this way, optimal rest and meal timings can be suggested based on the user's physical condition data.

[0078] The suggestion unit can suggest a mountain climbing plan that combines other outdoor activities. For example, when suggesting a mountain climbing route, the suggestion unit suggests a plan that combines outdoor activities such as bird watching or stargazing. For example, the suggestion unit selects a route that includes a specific observation point. The suggestion unit can also suggest a mountain climbing plan that combines other outdoor activities. For example, the suggestion unit suggests a plan that combines mountain climbing and camping. Furthermore, the suggestion unit can also build a system that suggests mountain climbing plans that combine outdoor activities. For example, the suggestion unit selects a route that includes activities that match the user's interests. This makes it possible to suggest a mountain climbing plan that allows for more diverse enjoyment by combining other outdoor activities.

[0079] The suggestion unit can generate a customizable mountain climbing schedule that matches the user's preferences. The suggestion unit generates, for example, a customizable mountain climbing schedule that matches the user's preferences. For example, the suggestion unit suggests a schedule that includes favorite activities and rest points. The suggestion unit can also generate a customizable plan that matches the user's preferences when proposing a mountain climbing schedule. For example, the suggestion unit suggests a schedule that matches the user's favorite time period and pace. Furthermore, the suggestion unit can also build a system that customizes the mountain climbing schedule in real time to match the user's preferences. For example, the suggestion unit adjusts the schedule according to changes in weather and physical condition. In this way, a customizable mountain climbing schedule that matches the user's preferences can be generated, enabling a more satisfying mountain climbing experience.

[0080] The suggestion unit can use the emotion estimation function to suggest a route and schedule that the user will enjoy most. The suggestion unit, for example, uses the emotion estimation function to suggest a mountain climbing route that the user will enjoy most. For example, the suggestion unit selects an optimal route based on past emotion data. The suggestion unit can also analyze the user's emotion data and suggest a schedule that will elicit positive emotions. For example, the suggestion unit sets rest times at relaxing rest points and scenic spots. Furthermore, the suggestion unit can use the emotion estimation function to build a system that suggests a route and schedule that the user will enjoy most in real time. For example, the suggestion unit adjusts the route and schedule according to changes in emotion during mountain climbing. In this way, the emotion estimation function can suggest a route and schedule that the user will enjoy most.

[0081] The suggestion unit can analyze the profile information of climbers passing by and suggest appropriate greeting content. The suggestion unit, for example, analyzes the profile information of climbers passing by and suggests appropriate greeting content. For example, the suggestion unit sets greeting content according to age and gender. The suggestion unit can also build a system that suggests appropriate greeting content based on the climber's profile information. For example, the suggestion unit selects optimal greeting content based on past data. Furthermore, the suggestion unit can analyze the profile information of climbers passing by in real time and suggest appropriate greeting content. For example, the suggestion unit sets greeting content according to the climber's facial expression and attitude. In this way, appropriate greeting content can be suggested by analyzing the climber's profile information.

[0082] The suggestion unit can analyze the tone and tempo of the user's voice and generate an optimal greeting voice. The suggestion unit, for example, analyzes the tone and tempo of the user's voice and generates an optimal greeting voice. For example, the suggestion unit greets with a natural tone and tempo. The suggestion unit can also build a system that generates an optimal greeting voice based on the tone and tempo of the voice. For example, the suggestion unit generates a greeting voice that reflects the characteristics of the user's voice. Furthermore, the suggestion unit can analyze the tone and tempo of the user's voice in real time and generate an optimal greeting voice. For example, the suggestion unit adjusts the greeting voice according to changes in the user's voice while climbing a mountain. In this way, the optimal greeting voice can be generated by analyzing the tone and tempo of the user's voice.

[0083] The suggestion unit can also enable greetings in other languages, thereby facilitating communication with international climbers. The suggestion unit, for example, adds greetings in other languages ​​to the auto-konnichiwa function, thereby facilitating communication with international climbers. For example, the suggestion unit sets greetings in English and Chinese. The suggestion unit can also build a system that enables greetings in other languages, thereby facilitating communication with international climbers. For example, the suggestion unit provides a greeting according to the user's language setting. Furthermore, the suggestion unit can add multilingual support to the auto-konnichiwa function, thereby facilitating communication with international climbers. For example, the suggestion unit provides a greeting according to the climber's nationality. This allows greetings in other languages ​​to be provided, thereby facilitating communication with international climbers.

[0084] The suggestion unit can enable not only greetings but also simple conversations and information exchanges. For example, the suggestion unit adds a simple conversation and information exchange function to the auto-conversation function, thereby promoting communication between climbers. For example, the suggestion unit exchanges weather and route information. The suggestion unit can also build a system that enables not only greetings but also simple conversations and information exchanges. For example, the suggestion unit automatically asks and answers questions between climbers. Furthermore, the suggestion unit can add an information exchange function to the auto-conversation function, thereby promoting communication between climbers. For example, the suggestion unit exchanges information about the status of hiking trails and recommended spots. This allows not only greetings but also simple conversations and information exchanges, thereby promoting communication between climbers.

[0085] The suggestion unit can use the emotion estimation function to suggest the timing and content of the greeting that will make the user most relaxed. For example, the suggestion unit uses the emotion estimation function to suggest the timing and content of the greeting that will make the user most relaxed. For example, the suggestion unit greets the user when the user is relaxed. The suggestion unit also suggests relaxing greeting content based on the user's emotion data. For example, the suggestion unit prioritizes using greeting content with a high emotion score. Furthermore, the suggestion unit can also use the emotion estimation function to build a system that suggests the timing and content of the greeting that will make the user most relaxed in real time. For example, the suggestion unit adjusts the greeting according to emotional changes while climbing a mountain. In this way, the emotion estimation function can be used to suggest the timing and content of the greeting that will make the user most relaxed.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The suggestion unit can estimate the user's emotions and suggest climbing routes and schedules that will elicit positive emotions. For example, the suggestion unit allows the generation AI to estimate the user's emotions and suggest climbing routes and schedules that will elicit positive emotions. For example, the generation AI analyzes the user's past climbing history and current emotional state to suggest climbing routes that will elicit positive emotions. The generation AI can also suggest schedules to reduce stress based on the user's emotional data. Furthermore, the generation AI can use the emotion estimation function to suggest climbing activities that the user will enjoy most. This makes climbing more enjoyable by making suggestions that take the user's emotions into consideration.

[0088] The suggestion unit can provide more detailed route information by analyzing reviews and photos posted by past climbers in addition to hiking trail information. For example, the suggestion unit can provide detailed route information by analyzing reviews and photos posted by past climbers in addition to hiking trail information. For example, the suggestion unit can provide information including dangerous areas and recommended scenic spots. The suggestion unit can also analyze reviews posted by past climbers to more accurately grasp the difficulty and required time of a route. Furthermore, the suggestion unit can analyze photos posted by climbers to provide visual information about the route. In this way, more detailed route information can be provided by analyzing reviews and photos posted by past climbers.

[0089] The suggestion unit can combine the user's past climbing history and physical condition data to suggest optimal rest points and hydration timings. For example, the suggestion unit analyzes the user's past climbing history and current physical condition data to suggest optimal rest points. For example, the suggestion unit avoids points where the user was prone to fatigue in the past and sets rest times according to their physical condition. The suggestion unit can also suggest hydration timings based on the physical condition data. For example, the suggestion unit calculates the appropriate amount of hydration based on the user's weight and temperature and sets the timing. Furthermore, the suggestion unit can combine the user's past climbing history and physical condition data to suggest a reasonable schedule. In this way, the suggestion unit can suggest optimal rest points and hydration timings by combining the user's past climbing history and physical condition data.

[0090] The information collection unit can use a drone to grasp the conditions of a hiking trail in real time and provide the latest information. For example, the information collection unit can use a drone to photograph the conditions of a hiking trail in real time and provide the latest information. For example, the information collection unit can check the congestion status and dangerous areas of a hiking trail using drone footage. The information collection unit can also analyze aerial photography data taken by a drone and provide the latest information on a hiking trail. For example, the information collection unit can grasp collapsed areas and newly appeared obstacles in real time. Furthermore, the information collection unit can use a drone to check the weather conditions of a hiking trail in real time and suggest equipment and routes depending on the weather. In this way, by using a drone, the latest conditions of a hiking trail can be grasped in real time and appropriate information can be provided.

[0091] The suggestion unit can generate a customizable mountain climbing schedule that matches the user's preferences. For example, the suggestion unit generates a customizable mountain climbing schedule that matches the user's preferences. For example, the suggestion unit suggests a schedule that includes favorite activities and rest points. The suggestion unit can also generate a customizable plan that matches the user's preferences when proposing a mountain climbing schedule. For example, the suggestion unit suggests a schedule that matches the user's favorite time period and pace. Furthermore, the suggestion unit can also build a system that customizes the mountain climbing schedule in real time to match the user's preferences. For example, the suggestion unit adjusts the schedule according to changes in weather and physical condition. In this way, a customizable mountain climbing schedule that matches the user's preferences can be generated, enabling a more satisfying mountain climbing experience.

[0092] The suggestion unit can use the emotion estimation function to suggest the mountain climbing activity that the user will enjoy most. For example, the suggestion unit can use the emotion estimation function to suggest the mountain climbing activity that the user will enjoy most. For example, the suggestion unit can select photo spots and nature observation points based on past emotion data. The suggestion unit can also analyze the user's emotion data and suggest activities that will elicit positive emotions. For example, the suggestion unit can select routes that include relaxing places and interesting nature observation points. Furthermore, the suggestion unit can use the emotion estimation function to build a system that suggests the activity that the user will enjoy most in real time. For example, the suggestion unit can adjust the activity according to emotional changes during mountain climbing. In this way, the emotion estimation function can be used to suggest the mountain climbing activity that the user will enjoy most.

[0093] The proposal unit can propose equipment that uses the latest technology and materials. For example, the proposal unit proposes mountain climbing equipment that uses the latest technology and materials. For example, the proposal unit selects equipment that uses lightweight and durable materials and the latest waterproof technology. The proposal unit can also propose equipment that incorporates the latest technology and materials when selecting mountain climbing equipment. For example, the proposal unit selects equipment that uses materials with temperature regulating functions and high breathability. Furthermore, the proposal unit can build a system that proposes equipment that uses the latest technology and materials. For example, the proposal unit selects equipment based on the latest research results and technological trends. This makes it possible to propose more comfortable and functional equipment by using the latest technology and materials.

[0094] The suggestion unit can suggest multifunctional equipment that takes into consideration use in other outdoor activities as well. For example, when selecting mountain climbing equipment, the suggestion unit suggests multifunctional equipment that can also be used in other outdoor activities such as camping and fishing. For example, the suggestion unit selects tents, cooking gear, etc. The suggestion unit can also suggest multifunctional equipment that takes into consideration use in other outdoor activities as well. For example, the suggestion unit selects equipment that can be used not only for mountain climbing but also for camping and fishing. Furthermore, the suggestion unit can build a system that suggests multifunctional equipment and select equipment that can be used in outdoor activities other than mountain climbing. For example, the suggestion unit suggests highly versatile equipment. In this way, highly versatile equipment can be suggested by taking into consideration use in other outdoor activities as well.

[0095] The suggestion unit can suggest customizable equipment that matches the user's fashion sense and preferences. For example, the suggestion unit suggests customizable mountain climbing equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that allows the user to choose the color or design. The suggestion unit can also suggest customizable equipment that matches the user's preferences when selecting mountain climbing equipment. For example, the suggestion unit selects equipment based on a favorite brand or style. Furthermore, the suggestion unit can also build a system that suggests customizable equipment that matches the user's fashion sense and preferences. For example, the suggestion unit selects equipment that reflects the user's preferences. This allows for more satisfying mountain climbing by suggesting customizable equipment that matches the user's fashion sense and preferences.

[0096] The suggestion unit can use the emotion estimation function to suggest an equipment combination that makes the user feel most comfortable. For example, the suggestion unit uses the emotion estimation function to suggest an equipment combination that makes the user feel most comfortable. For example, the suggestion unit selects optimal equipment based on past emotion data. The suggestion unit can also analyze the user's emotion data to suggest an equipment combination that prioritizes comfort. For example, the suggestion unit suggests a combination of equipment with a high emotion score. Furthermore, the suggestion unit can use the emotion estimation function to build a system that suggests an equipment combination that makes the user feel most comfortable in real time. For example, the suggestion unit adjusts the equipment according to emotional changes during mountain climbing. In this way, the emotion estimation function can be used to suggest an equipment combination that makes the user feel most comfortable.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The information collection unit collects hiking trail information, weather forecasts, past hiking history, skill level, health care data, hiking wear, and equipment. For example, the information collection unit obtains hiking trail information from YAMAP or similar sources to understand the difficulty of the hiking route and the current conditions. The information collection unit also obtains weather forecasts from Hiking Weather and suggests equipment appropriate for the weather. Furthermore, the information collection unit generates an appropriate route and schedule, taking into account the user's past hiking history and skill level. Step 2: The analysis unit analyzes the hiking trail information, weather forecast, past hiking history, skill level, health care data, hiking wear, and equipment collected by the information collection unit. For example, the analysis unit may propose a reasonable hiking plan based on the user's physical condition based on the collected data. Step 3: The suggestion unit proposes an optimal climbing route and schedule based on the results of the analysis by the analysis unit. For example, the suggestion unit selects a reasonable route taking into account the user's skill level and physical condition. The suggestion unit also suggests appropriate rest points and time allocations depending on the weather and trail conditions.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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, to avoid confusion and 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.

[0165] 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. [Explanation of symbols]

[0166] 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 system characterized by comprising: an information collection unit that collects hiking trail information, weather forecasts, past hiking history, skill level, health care data, hiking wear, and equipment; an analysis unit that analyzes the hiking trail information, weather forecasts, past hiking history, skill level, health care data, hiking wear, and equipment collected by the information collection unit; and a proposal unit that proposes an optimal hiking route and schedule based on the results of the analysis by the analysis unit.

2. The system according to claim 1, characterized in that the information collection unit uses a drone to grasp the condition of the hiking trail in real time and provide the latest information.

3. 2. The system according to claim 1, wherein the suggestion unit generates a customizable mountain climbing plan that matches the user's preferences based on the mountain climbing information.

4. The system according to claim 1 , wherein the suggestion unit analyzes the past equipment use history and suggests the most comfortable equipment.

5. The system described in claim 1, characterized in that the suggestion unit uses a generation AI to estimate the user's emotions and adjust the timing and content of the greeting to elicit positive emotions.

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