system

Abstract

The system according to this embodiment aims to generate an optimal climbing plan tailored to the user's health condition, climbing experience, and climbing objectives, and to make reservations in bulk. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a reservation unit. The reception unit receives input from the user regarding their health status, climbing experience, and climbing purpose. The generation unit analyzes the information entered by the reception unit and generates an optimal climbing plan. The reservation unit makes a reservation based on the climbing plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that an optimal mountain climbing plan according to the user's health condition, mountain climbing experience, and mountain climbing purpose cannot be generated and reservations cannot be made in a batch.

[0005] The system according to the embodiment aims to generate an optimal mountain climbing plan according to the user's health condition, mountain climbing experience, and mountain climbing purpose and make reservations in a batch.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a reservation unit. The reception unit receives input from the user regarding their health status, climbing experience, and climbing objectives. The generation unit analyzes the information entered by the reception unit and generates an optimal climbing plan. The reservation unit makes a reservation based on the climbing plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate an optimal climbing plan tailored to the user's health condition, climbing experience, and climbing objectives, and make reservations in bulk. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The mountain climbing plan generation system according to an embodiment of the present invention is a system in which a generating AI generates an optimal mountain climbing plan, taking into account the user's health condition, mountain climbing experience, and climbing objectives. This mountain climbing plan generation system provides a system that proposes transportation, accommodation, and climbing routes all at once, and allows the user to make the necessary reservations in one stop. First, the user inputs their health condition, climbing experience, and climbing objectives. For example, the user inputs objectives such as "I want to relax," "I want to challenge myself," or "Family-friendly." This information is input into the generating AI. Next, the generating AI analyzes the input information and generates an optimal mountain climbing plan. Based on the user's health condition, climbing experience, and climbing objectives, the generating AI proposes transportation, accommodation, and climbing routes all at once. For example, if the user inputs "I want to relax," the generating AI will propose relaxing climbing routes and accommodations. Based on the generated mountain climbing plan, the user can make the necessary reservations in one stop. For example, the user can make reservations for transportation and accommodations proposed by the generating AI all at once. With this system, the user can easily create an optimal mountain climbing plan without the hassle of gathering information and making reservations. Furthermore, the generating AI can consider real-time weather forecasts and traffic information to suggest optimal routes and reservation changes. For example, if the weather deteriorates, the generating AI will suggest route changes or accommodation changes. In addition, the generating AI can learn from the user's behavior history and reservation history to provide more personalized suggestions. For example, it can make suggestions that match the user's preferences based on information about accommodations and transportation methods used in the past. This system aims to enable each climber to easily and safely enjoy the climbing experience that best suits them, thereby promoting a healthy lifestyle and raising environmental awareness. For example, it aims to raise environmental awareness by suggesting environmentally friendly transportation and accommodations. As a result, the climbing plan generation system can generate and book optimal climbing plans based on the user's health condition, climbing experience, and climbing objectives.

[0029] The mountain climbing plan generation system according to this embodiment comprises a reception unit, a generation unit, and a reservation unit. The reception unit takes input from the user's health status, mountain climbing experience, and purpose of climbing. The user's health status includes, but is not limited to, heart rate, blood pressure, and medical history. Mountain climbing experience includes, but is not limited to, the number of times a person has climbed in the past and the difficulty level of mountains climbed. The purpose of climbing includes, but is not limited to, recreation, training, and research. The reception unit allows the user to input purposes such as "I want to relax," "I want to challenge myself," or "Family-friendly." The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an optimal mountain climbing plan. The generation unit proposes transportation, accommodation, and climbing routes as a package, based on, for example, the user's health status, mountain climbing experience, and purpose of climbing. For example, if the user inputs "I want to relax," the generation unit proposes relaxing climbing routes and accommodations. For example, if the user inputs "I want to challenge myself," the generation unit proposes challenging climbing routes and accommodations. The generation unit, for example, if the user inputs "family-friendly," will suggest family-friendly hiking routes and accommodations. The booking unit will make reservations based on the hiking plans generated by the generation unit. The booking unit can, for example, make reservations for transportation and accommodations suggested by the generation AI all at once. The booking unit can, for example, make reservations for transportation. The booking unit can, for example, make reservations for accommodations. The booking unit can, for example, make reservations for hiking routes. In this way, the hiking plan generation system can generate and book the optimal hiking plan based on the user's health condition, hiking experience, and hiking purpose.

[0030] The reception desk inputs the user's health status, climbing experience, and climbing purpose. The user's health status includes, but is not limited to, heart rate, blood pressure, and medical history. Specifically, users can input detailed information about their health status through a dedicated application or website. Heart rate and blood pressure may be automatically obtained from devices such as smartwatches or fitness trackers. Regarding medical history, users input information about past illnesses and their current health status. This allows the reception desk to gain a detailed understanding of the user's health status and collect basic data to propose an appropriate climbing plan. Climbing experience includes, but is not limited to, the number of climbs and the difficulty level of the mountains climbed. Users can input the names of mountains they have climbed in the past, the number of climbs, and the difficulty level. This allows the reception desk to gain a detailed understanding of the user's climbing experience and collect basic data to propose an appropriate climbing plan. Climbing purpose includes, but is not limited to, recreation, training, and research. Users can freely input their climbing purpose, such as "I want to relax," "I want a challenge," or "It's family-friendly." This allows the reception desk to gather basic data to understand the user's climbing objectives in detail and propose appropriate climbing plans.

[0031] The generation unit uses a generation AI to analyze the information entered by the reception unit and generate the optimal climbing plan. The generation AI proposes transportation, accommodation, and climbing routes as a whole, based on the user's health condition, climbing experience, and climbing purpose. Specifically, the generation AI considers the user's health condition and proposes a manageable climbing route and appropriate rest points. For example, it proposes a relatively flat and low-impact route for users with high heart rates or blood pressure, and a route with nearby medical facilities for users with pre-existing conditions. Based on climbing experience, it proposes easy routes for beginners and more difficult routes for experienced users. For example, it proposes beginner-friendly routes or guided tours for users with little prior climbing experience, and challenging routes or long-distance trails for experienced users. Based on climbing purpose, it proposes scenic routes and accommodations near hot springs for users who want to relax, and more difficult routes and routes that offer a sense of accomplishment upon reaching the summit for users who want a challenge. For families, it proposes routes that are safe for children and accommodations with plenty of family-friendly activities. The generation AI comprehensively analyzes this information to generate the optimal climbing plan for the user.

[0032] The reservation department makes reservations based on the climbing plans generated by the generation department. Specifically, it can make reservations for transportation and accommodation suggested by the generation AI all at once. For example, if a user agrees to a suggested climbing plan, the reservation department automatically makes reservations for transportation. Transportation includes trains, buses, taxis, etc., and arranges the most suitable means of transport from the user's starting point to the trailhead. For accommodation reservations, it arranges hotels, inns, campsites, etc., according to the user's preferences. The reservation department can check the availability of accommodations in real time and make reservations for the most suitable accommodations. Furthermore, it can also make reservations for climbing routes as needed. For example, popular climbing routes or routes with restrictions for specific periods may require advance reservations. The reservation department takes this information into consideration and makes reservations for climbing routes on behalf of the user. This allows users to enjoy climbing smoothly without having to go through complicated procedures. In addition, the reservation department can also provide support such as confirming, changing, and canceling reservations. Even if a user wants to change their plans or needs to cancel at short notice, the reservation department can respond quickly and meet the user's needs. This allows the mountain climbing plan generation system to create and book the optimal mountain climbing plan based on the user's health condition, climbing experience, and climbing objectives.

[0033] The information gathering unit can collect information to consider real-time weather forecasts and traffic information. For example, the information gathering unit collects weather forecast information such as temperature, probability of precipitation, and wind speed. For example, the information gathering unit collects traffic information such as traffic congestion, operating status, and transportation options. For example, the information gathering unit collects temperature changes in real time and reflects them in the climbing plan. For example, the information gathering unit collects probability of precipitation in real time and reflects it in the climbing plan. For example, the information gathering unit collects wind speed changes in real time and reflects it in the climbing plan. For example, the information gathering unit collects traffic congestion information in real time and reflects it in the climbing plan. For example, the information gathering unit collects operating status information in real time and reflects it in the climbing plan. For example, the information gathering unit collects transportation options in real time and reflects them in the climbing plan. As a result, the information gathering unit can provide more appropriate climbing plans by considering real-time weather forecasts and traffic information. Some or all of the processing described above in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input weather forecast information into AI, which can then analyze and incorporate it into the climbing plan.

[0034] The learning unit can learn the user's activity history and booking history. For example, the learning unit can learn activity history such as past hiking routes and places visited. For example, the learning unit can learn past accommodation and transportation booking history. For example, the learning unit can learn past hiking routes and suggest routes that suit the user's preferences. For example, the learning unit can learn places visited and suggest places that suit the user's preferences. For example, the learning unit can learn past accommodations and suggest accommodations that suit the user's preferences. For example, the learning unit can learn past transportation booking history and suggest transportations that suit the user's preferences. In this way, by learning the user's activity history and booking history, the learning unit can make more personalized suggestions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input activity history and booking history into AI, which can analyze it and make suggestions that suit the user's preferences.

[0035] The Environmental Consideration Department can propose environmentally friendly transportation and accommodation options. For example, the Environmental Consideration Department can propose environmentally friendly transportation options such as electric vehicles and public transport. For example, the Environmental Consideration Department can propose environmentally friendly accommodation options such as eco-hotels and facilities with green certification. For example, the Environmental Consideration Department can propose electric vehicles and provide environmentally friendly transportation options. For example, the Environmental Consideration Department can propose public transport and provide environmentally friendly transportation options. For example, the Environmental Consideration Department can propose eco-hotels and provide environmentally friendly accommodation options. For example, the Environmental Consideration Department can propose facilities with green certification and provide environmentally friendly accommodation options. In this way, the Environmental Consideration Department can raise environmental awareness by making environmentally friendly proposals. Some or all of the above processing in the Environmental Consideration Department may be performed using AI, for example, or without AI. For example, the Environmental Consideration Department can input information on transportation and accommodation options into AI, which can then analyze the data and make environmentally friendly proposals.

[0036] The generation unit can propose transportation, accommodation, and climbing routes in a single package based on the user's health condition, climbing experience, and climbing objectives. For example, the generation unit proposes the optimal transportation based on the user's health condition. For example, the generation unit proposes the optimal accommodation based on the user's climbing experience. For example, the generation unit proposes the optimal climbing route based on the user's climbing objectives. For example, the generation unit proposes transportation appropriate to the user's physical strength based on their health condition. For example, the generation unit proposes accommodation appropriate to the difficulty level based on the user's climbing experience. For example, the generation unit proposes climbing routes appropriate to the user's objectives. In this way, the generation unit can propose an optimal climbing plan in a single package based on the user's health condition, climbing experience, and climbing objectives. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's health condition, climbing experience, and climbing objectives into the generation AI, which analyzes the data and proposes an optimal climbing plan.

[0037] The reservation unit can make reservations for transportation and accommodation suggested by the generating AI in a batch. For example, the reservation unit can make reservations for transportation suggested by the generating AI. For example, the reservation unit can make reservations for accommodation suggested by the generating AI. For example, the reservation unit can make reservations for hiking routes suggested by the generating AI. For example, the reservation unit can make reservations for transportation in a batch. For example, the reservation unit can make reservations for accommodation in a batch. For example, the reservation unit can make reservations for hiking routes in a batch. This improves user convenience by allowing the reservation unit to make reservations for transportation and accommodation suggested by the generating AI in a batch. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input information on transportation and accommodation suggested by the generating AI into the AI, which can then analyze and make the reservations.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user's frequently entered health status, climbing experience, and climbing purpose. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the health status, climbing experience, and climbing purpose to be used at a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history into AI, which can then analyze and suggest the optimal input method.

[0039] The reception unit can simplify input by automatically acquiring the user's current location information when they input their health status, climbing experience, and climbing purpose. For example, when a user opens the app, the reception unit automatically acquires their current location and simplifies the input of their health status, climbing experience, and climbing purpose. For example, when a user enters a destination, the reception unit suggests the most suitable candidate location considering the distance from their current location. For example, when a user uses the app while on the move, the reception unit updates their current location in real time and simplifies the input of their health status, climbing experience, and climbing purpose. In this way, the reception unit can simplify input by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the current location information into AI, which can then analyze and simplify the input.

[0040] The reception desk can automatically suggest potential locations based on the user's past travel history when the user inputs their health status, climbing experience, and climbing purpose. For example, the reception desk can automatically display places the user has frequently visited in the past as potential locations. For example, the reception desk can predict places the user will visit on specific days of the week or times of day and suggest them as potential locations. For example, the reception desk can analyze the user's past travel patterns and suggest the most suitable potential locations. In this way, the reception desk can automatically suggest potential locations by referring to the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input past travel history into AI, which can then analyze and suggest potential locations.

[0041] The reception desk can refer to the user's calendar information when they input their health status, climbing experience, and climbing purpose, and make suggestions based on their schedule. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the health status, climbing experience, and climbing purpose. For example, the reception desk can suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can suggest the optimal route to match the schedule based on the user's calendar information. In this way, the reception desk can make suggestions based on the schedule by referring to the user's calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input calendar information into AI, and the AI ​​can analyze it and make suggestions based on the schedule.

[0042] The generation unit can propose the optimal route when generating a climbing plan, by referring to the user's past climbing history. For example, the generation unit proposes the optimal route based on routes the user has used in the past. For example, the generation unit proposes a route that avoids congestion based on the user's past climbing history. For example, the generation unit analyzes the user's past climbing history and proposes the most efficient route. In this way, the generation unit can propose the optimal route by referring to the user's past climbing history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input past climbing history into the generation AI, which can then analyze it and propose the optimal route.

[0043] The generation unit can optimize routes by considering real-time weather forecasts when generating climbing plans. For example, the generation unit proposes the optimal route based on real-time weather forecasts. For example, the generation unit proposes a route that avoids bad weather based on real-time weather forecasts. For example, the generation unit analyzes real-time weather forecasts and proposes the safest route. In this way, the generation unit can propose the optimal route by considering real-time weather forecasts. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input real-time weather forecasts into the generation AI, which can then analyze and propose the optimal route.

[0044] The generation unit can propose routes that take into account the user's current health condition when generating a hiking plan. For example, if the user is tired, the generation unit will propose the shortest route. If the user is seeking healthy exercise, the generation unit will propose a slightly longer route. If the user is feeling unwell, the generation unit will propose a route that includes rest stops. In this way, the generation unit can propose the optimal route by taking into account the user's current health condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's current health condition into the generation AI, which can then analyze it and propose the optimal route.

[0045] The generation unit can suggest the most suitable accommodation based on the user's climbing objectives when generating a climbing plan. For example, if the user wants to relax, the generation unit will suggest accommodation in a quiet environment. For example, if the user wants to challenge themselves, the generation unit will suggest accommodation close to the trailhead. For example, if the user wants a family-friendly climb, the generation unit will suggest accommodation with family-friendly facilities. In this way, the generation unit can provide a more appropriate plan by suggesting the most suitable accommodation based on the user's climbing objectives. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's climbing objectives into the generation AI, which can then analyze and suggest the most suitable accommodation.

[0046] The reservation department can suggest the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on the accommodations the user has used in the past. For example, the reservation department can suggest a reservation method that avoids congestion based on the user's past reservation history. For example, the reservation department can suggest the most efficient reservation method by analyzing the user's past reservation history. In this way, the reservation department can suggest the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input past reservation history into AI, which can then analyze and suggest the optimal reservation method.

[0047] The reservation unit can update the user's current location information in real time when making a reservation. For example, the reservation unit can update the user's current location in real time while they are traveling and suggest the optimal reservation method. For example, the reservation unit can update the user's current location in real time as they approach their destination and suggest the optimal reservation method. For example, if the user gets lost, the reservation unit can update their current location in real time and make another reservation. In this way, the reservation unit can make the optimal reservation by updating the user's current location information in real time. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the current location information into AI, which can then analyze and suggest the optimal reservation method.

[0048] The reservation department can suggest the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, the reservation department may prioritize suggesting accommodations close to the user's current location. For example, if the user is on the move, the reservation department may suggest the optimal reservation method considering the distance from the current location. In this way, the reservation department can suggest the optimal reservation method by taking into account the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or without AI. For example, the reservation department can input geographical location information into AI, which can then analyze and suggest the optimal reservation method.

[0049] The reservation department can analyze a user's social media activity during the reservation process and suggest relevant reservation options. For example, the reservation department can suggest optimal reservation options based on places the user has shared on social media. For example, the reservation department can predict places of interest based on the user's social media activity and suggest reservation options. For example, the reservation department can analyze a user's social media activity and suggest reservations for relevant events and activities. In this way, the reservation department can suggest relevant reservation options by analyzing the user's social media activity. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input social media activity into AI, which can then analyze and suggest relevant reservation options.

[0050] The information gathering unit can provide optimal information by referring to the user's past information gathering history when gathering information. For example, the information gathering unit can provide optimal information based on information the user has collected in the past. For example, the information gathering unit can provide relevant information from the user's past information gathering history. For example, the information gathering unit can analyze the user's past information gathering history and provide the most relevant information. In this way, the information gathering unit can provide optimal information by referring to the user's past information gathering history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history into AI, and the AI ​​can analyze it and provide optimal information.

[0051] The information gathering unit can provide information while considering real-time weather forecasts and traffic information. For example, the information gathering unit can provide optimal information based on real-time weather forecasts. For example, the information gathering unit can provide optimal information while considering real-time traffic information. For example, the information gathering unit can analyze real-time weather forecasts and traffic information and provide the most relevant information. In this way, the information gathering unit can provide optimal information by considering real-time weather forecasts and traffic information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input real-time weather forecasts and traffic information into AI, which can then analyze and provide optimal information.

[0052] The information gathering unit can provide optimal information by considering the user's geographical location information during information gathering. For example, the information gathering unit may prioritize providing information that is close to the user's current location. For example, the information gathering unit may prioritize providing information that is close to the user's destination. For example, if the user is on the move, the information gathering unit may provide optimal information by considering the distance from the current location. In this way, the information gathering unit can provide optimal information by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input geographical location information into AI, and the AI ​​can analyze it to provide optimal information.

[0053] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the most effective learning algorithm. In this way, the learning unit can optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI, which can then analyze and optimize the learning algorithm.

[0054] The learning unit can weight the training data based on the user's behavior history during training. For example, the learning unit can weight important data based on the user's behavior history. For example, the learning unit can weight highly relevant data from the user's behavior history. For example, the learning unit can analyze the user's behavior history and weight the most relevant data. This allows the learning unit to perform more appropriate training by weighting the training data based on the user's behavior history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the behavior history into AI, which can then analyze and weight the training data.

[0055] The learning unit can select training data based on the user's health status during training. For example, the learning unit can select the optimal training data based on the user's health status. For example, the learning unit can select highly relevant training data from the user's health status. For example, the learning unit can analyze the user's health status and select the most relevant training data. This allows the learning unit to perform more appropriate training by selecting training data based on the user's health status. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input health status data into AI, which can then analyze and select training data.

[0056] The environmental considerations unit can make optimal suggestions by referring to the user's past environmentally conscious actions when proposing environmental considerations. For example, the environmental considerations unit makes optimal suggestions based on the user's past environmentally conscious actions. For example, the environmental considerations unit makes relevant suggestions based on the user's past environmentally conscious actions. For example, the environmental considerations unit analyzes the user's past environmentally conscious actions and makes the most relevant suggestions. In this way, the environmental considerations unit can make optimal suggestions by referring to the user's past environmentally conscious actions. Some or all of the above processing in the environmental considerations unit may be performed using AI, for example, or without AI. For example, the environmental considerations unit can input past environmentally conscious actions into AI, which can then analyze and make optimal suggestions.

[0057] The Environmental Consideration Department can make proposals that take real-time environmental information into account when proposing environmental considerations. For example, the Environmental Consideration Department can make optimal proposals based on real-time environmental information. For example, the Environmental Consideration Department can make relevant proposals from real-time environmental information. For example, the Environmental Consideration Department can analyze real-time environmental information and make the most relevant proposals. In this way, the Environmental Consideration Department can make optimal proposals by taking real-time environmental information into account. Some or all of the above processing in the Environmental Consideration Department may be performed using AI, for example, or without AI. For example, the Environmental Consideration Department can input real-time environmental information into AI, which can then analyze and make optimal proposals.

[0058] The environmental considerations unit can make optimal suggestions by considering the user's geographical location when proposing environmentally friendly options. For example, the environmental considerations unit may prioritize environmentally friendly suggestions that are close to the user's current location. For example, if the user is on the move, the environmental considerations unit will make optimal suggestions by considering the distance from the current location. In this way, the environmental considerations unit can make optimal suggestions by considering the user's geographical location. Some or all of the above processing in the environmental considerations unit may be performed using AI, for example, or without AI. For example, the environmental considerations unit can input geographical location information into AI, which can then analyze and make optimal suggestions.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception section allows users to input their health status, climbing experience, and climbing goals, as well as their dietary restrictions and allergy information. For example, if a user has an allergy to a specific food, inputting this information allows the generation section to suggest accommodations and meal plans that take allergies into consideration. Similarly, if a user has specific dietary restrictions (vegetarian, vegan, gluten-free, etc.), the system can suggest appropriate meal plans based on this information. This allows users to enjoy climbing with peace of mind. Furthermore, the reception section inputs the user's dietary restrictions and allergy information into the learning section, which then uses this information to provide the user with the most suitable suggestions.

[0061] The information gathering unit can collect information about the user's health status and incorporate it into the climbing plan. For example, it can collect data on the user's heart rate and blood pressure, and adjust the difficulty of the climbing route based on this data. It can also suggest rest points and hydration timings according to the user's health status. Furthermore, the information gathering unit inputs data on the user's health status into the learning unit, which can then make optimal suggestions to the user based on this data. This allows users to enjoy climbing safely and healthily.

[0062] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as suggestions the user's health status, climbing experience, and climbing purpose that they have frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Based on the user's past input history, it can predict and suggest the health status, climbing experience, and climbing purpose to be used at a specific time of day. In this way, the reception desk can suggest the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history into AI, which can then analyze and suggest the optimal input method.

[0063] The information gathering unit can provide optimal information by referring to the user's past information gathering history when gathering information. For example, it can provide optimal information based on information the user has collected in the past. It can provide relevant information from the user's past information gathering history. It can analyze the user's past information gathering history and provide the most relevant information. In this way, the information gathering unit can provide optimal information by referring to the user's past information gathering history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history into AI, and the AI ​​can analyze it to provide optimal information.

[0064] The generation unit can suggest routes while considering the user's current health condition when generating a hiking plan. For example, if the user is tired, it will suggest the shortest route. If the user is seeking healthy exercise, it will suggest a slightly longer route. If the user is feeling unwell, it will suggest a route that includes rest stops. In this way, the generation unit can suggest the optimal route by considering the user's current health condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's current health condition into the generation AI, which can then analyze it and suggest the optimal route.

[0065] The reservation department can suggest the optimal reservation method when a reservation is made, by referring to the user's past reservation history. For example, it can suggest the optimal reservation method based on the accommodations the user has used in the past. It can suggest a reservation method that avoids congestion based on the user's past reservation history. It can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, the reservation department can suggest the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input past reservation history into AI, which can then analyze and suggest the optimal reservation method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk enters the user's health status, climbing experience, and climbing purpose. The user's health status includes heart rate, blood pressure, and medical history. Climbing experience includes the number of times climbed in the past and the difficulty level of the mountains climbed. Climbing purpose includes recreation, training, research, etc. Users can enter purposes such as "I want to relax," "I want to challenge myself," or "It's family-friendly." Step 2: The generation unit analyzes the information entered by the reception unit and generates the optimal climbing plan. Using generation AI, the generation unit proposes transportation, accommodation, and climbing routes based on the user's health condition, climbing experience, and climbing purpose. For example, if the user enters "I want to relax," it will suggest relaxing climbing routes and accommodations; if the user enters "I want to challenge myself," it will suggest challenging climbing routes and accommodations; and if the user enters "family-friendly," it will suggest family-friendly climbing routes and accommodations. Step 3: The booking unit makes reservations based on the climbing plan generated by the generation unit. The booking unit can make reservations for transportation and accommodation suggested by the generation AI all at once. For example, it can make reservations for transportation, accommodation, and climbing routes.

[0068] (Example of form 2) The mountain climbing plan generation system according to an embodiment of the present invention is a system in which a generating AI generates an optimal mountain climbing plan, taking into account the user's health condition, mountain climbing experience, and climbing objectives. This mountain climbing plan generation system provides a system that proposes transportation, accommodation, and climbing routes all at once, and allows the user to make the necessary reservations in one stop. First, the user inputs their health condition, climbing experience, and climbing objectives. For example, the user inputs objectives such as "I want to relax," "I want to challenge myself," or "Family-friendly." This information is input into the generating AI. Next, the generating AI analyzes the input information and generates an optimal mountain climbing plan. Based on the user's health condition, climbing experience, and climbing objectives, the generating AI proposes transportation, accommodation, and climbing routes all at once. For example, if the user inputs "I want to relax," the generating AI will propose relaxing climbing routes and accommodations. Based on the generated mountain climbing plan, the user can make the necessary reservations in one stop. For example, the user can make reservations for transportation and accommodations proposed by the generating AI all at once. With this system, the user can easily create an optimal mountain climbing plan without the hassle of gathering information and making reservations. Furthermore, the generating AI can consider real-time weather forecasts and traffic information to suggest optimal routes and reservation changes. For example, if the weather deteriorates, the generating AI will suggest route changes or accommodation changes. In addition, the generating AI can learn from the user's behavior history and reservation history to provide more personalized suggestions. For example, it can make suggestions that match the user's preferences based on information about accommodations and transportation methods used in the past. This system aims to enable each climber to easily and safely enjoy the climbing experience that best suits them, thereby promoting a healthy lifestyle and raising environmental awareness. For example, it aims to raise environmental awareness by suggesting environmentally friendly transportation and accommodations. As a result, the climbing plan generation system can generate and book optimal climbing plans based on the user's health condition, climbing experience, and climbing objectives.

[0069] The mountain climbing plan generation system according to this embodiment comprises a reception unit, a generation unit, and a reservation unit. The reception unit takes input from the user's health status, mountain climbing experience, and purpose of climbing. The user's health status includes, but is not limited to, heart rate, blood pressure, and medical history. Mountain climbing experience includes, but is not limited to, the number of times a person has climbed in the past and the difficulty level of mountains climbed. The purpose of climbing includes, but is not limited to, recreation, training, and research. The reception unit allows the user to input purposes such as "I want to relax," "I want to challenge myself," or "Family-friendly." The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an optimal mountain climbing plan. The generation unit proposes transportation, accommodation, and climbing routes as a package, based on, for example, the user's health status, mountain climbing experience, and purpose of climbing. For example, if the user inputs "I want to relax," the generation unit proposes relaxing climbing routes and accommodations. For example, if the user inputs "I want to challenge myself," the generation unit proposes challenging climbing routes and accommodations. The generation unit, for example, if the user inputs "family-friendly," will suggest family-friendly hiking routes and accommodations. The booking unit will make reservations based on the hiking plans generated by the generation unit. The booking unit can, for example, make reservations for transportation and accommodations suggested by the generation AI all at once. The booking unit can, for example, make reservations for transportation. The booking unit can, for example, make reservations for accommodations. The booking unit can, for example, make reservations for hiking routes. In this way, the hiking plan generation system can generate and book the optimal hiking plan based on the user's health condition, hiking experience, and hiking purpose.

[0070] The reception desk inputs the user's health status, climbing experience, and climbing purpose. The user's health status includes, but is not limited to, heart rate, blood pressure, and medical history. Specifically, users can input detailed information about their health status through a dedicated application or website. Heart rate and blood pressure may be automatically obtained from devices such as smartwatches or fitness trackers. Regarding medical history, users input information about past illnesses and their current health status. This allows the reception desk to gain a detailed understanding of the user's health status and collect basic data to propose an appropriate climbing plan. Climbing experience includes, but is not limited to, the number of climbs and the difficulty level of the mountains climbed. Users can input the names of mountains they have climbed in the past, the number of climbs, and the difficulty level. This allows the reception desk to gain a detailed understanding of the user's climbing experience and collect basic data to propose an appropriate climbing plan. Climbing purpose includes, but is not limited to, recreation, training, and research. Users can freely input their climbing purpose, such as "I want to relax," "I want a challenge," or "It's family-friendly." This allows the reception desk to gather basic data to understand the user's climbing objectives in detail and propose appropriate climbing plans.

[0071] The generation unit uses a generation AI to analyze the information entered by the reception unit and generate the optimal climbing plan. The generation AI proposes transportation, accommodation, and climbing routes as a whole, based on the user's health condition, climbing experience, and climbing purpose. Specifically, the generation AI considers the user's health condition and proposes a manageable climbing route and appropriate rest points. For example, it proposes a relatively flat and low-impact route for users with high heart rates or blood pressure, and a route with nearby medical facilities for users with pre-existing conditions. Based on climbing experience, it proposes easy routes for beginners and more difficult routes for experienced users. For example, it proposes beginner-friendly routes or guided tours for users with little prior climbing experience, and challenging routes or long-distance trails for experienced users. Based on climbing purpose, it proposes scenic routes and accommodations near hot springs for users who want to relax, and more difficult routes and routes that offer a sense of accomplishment upon reaching the summit for users who want a challenge. For families, it proposes routes that are safe for children and accommodations with plenty of family-friendly activities. The generation AI comprehensively analyzes this information to generate the optimal climbing plan for the user.

[0072] The reservation department makes reservations based on the climbing plans generated by the generation department. Specifically, it can make reservations for transportation and accommodation suggested by the generation AI all at once. For example, if a user agrees to a suggested climbing plan, the reservation department automatically makes reservations for transportation. Transportation includes trains, buses, taxis, etc., and arranges the most suitable means of transport from the user's starting point to the trailhead. For accommodation reservations, it arranges hotels, inns, campsites, etc., according to the user's preferences. The reservation department can check the availability of accommodations in real time and make reservations for the most suitable accommodations. Furthermore, it can also make reservations for climbing routes as needed. For example, popular climbing routes or routes with restrictions for specific periods may require advance reservations. The reservation department takes this information into consideration and makes reservations for climbing routes on behalf of the user. This allows users to enjoy climbing smoothly without having to go through complicated procedures. In addition, the reservation department can also provide support such as confirming, changing, and canceling reservations. Even if a user wants to change their plans or needs to cancel at short notice, the reservation department can respond quickly and meet the user's needs. This allows the mountain climbing plan generation system to create and book the optimal mountain climbing plan based on the user's health condition, climbing experience, and climbing objectives.

[0073] The information gathering unit can collect information to consider real-time weather forecasts and traffic information. For example, the information gathering unit collects weather forecast information such as temperature, probability of precipitation, and wind speed. For example, the information gathering unit collects traffic information such as traffic congestion, operating status, and transportation options. For example, the information gathering unit collects temperature changes in real time and reflects them in the climbing plan. For example, the information gathering unit collects probability of precipitation in real time and reflects it in the climbing plan. For example, the information gathering unit collects wind speed changes in real time and reflects it in the climbing plan. For example, the information gathering unit collects traffic congestion information in real time and reflects it in the climbing plan. For example, the information gathering unit collects operating status information in real time and reflects it in the climbing plan. For example, the information gathering unit collects transportation options in real time and reflects them in the climbing plan. As a result, the information gathering unit can provide more appropriate climbing plans by considering real-time weather forecasts and traffic information. Some or all of the processing described above in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input weather forecast information into AI, which can then analyze and incorporate it into the climbing plan.

[0074] The learning unit can learn the user's activity history and booking history. For example, the learning unit can learn activity history such as past hiking routes and places visited. For example, the learning unit can learn past accommodation and transportation booking history. For example, the learning unit can learn past hiking routes and suggest routes that suit the user's preferences. For example, the learning unit can learn places visited and suggest places that suit the user's preferences. For example, the learning unit can learn past accommodations and suggest accommodations that suit the user's preferences. For example, the learning unit can learn past transportation booking history and suggest transportations that suit the user's preferences. In this way, by learning the user's activity history and booking history, the learning unit can make more personalized suggestions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input activity history and booking history into AI, which can analyze it and make suggestions that suit the user's preferences.

[0075] The Environmental Consideration Department can propose environmentally friendly transportation and accommodation options. For example, the Environmental Consideration Department can propose environmentally friendly transportation options such as electric vehicles and public transport. For example, the Environmental Consideration Department can propose environmentally friendly accommodation options such as eco-hotels and facilities with green certification. For example, the Environmental Consideration Department can propose electric vehicles and provide environmentally friendly transportation options. For example, the Environmental Consideration Department can propose public transport and provide environmentally friendly transportation options. For example, the Environmental Consideration Department can propose eco-hotels and provide environmentally friendly accommodation options. For example, the Environmental Consideration Department can propose facilities with green certification and provide environmentally friendly accommodation options. In this way, the Environmental Consideration Department can raise environmental awareness by making environmentally friendly proposals. Some or all of the above processing in the Environmental Consideration Department may be performed using AI, for example, or without AI. For example, the Environmental Consideration Department can input information on transportation and accommodation options into AI, which can then analyze the data and make environmentally friendly proposals.

[0076] The generation unit can propose transportation, accommodation, and climbing routes in a single package based on the user's health condition, climbing experience, and climbing objectives. For example, the generation unit proposes the optimal transportation based on the user's health condition. For example, the generation unit proposes the optimal accommodation based on the user's climbing experience. For example, the generation unit proposes the optimal climbing route based on the user's climbing objectives. For example, the generation unit proposes transportation appropriate to the user's physical strength based on their health condition. For example, the generation unit proposes accommodation appropriate to the difficulty level based on the user's climbing experience. For example, the generation unit proposes climbing routes appropriate to the user's objectives. In this way, the generation unit can propose an optimal climbing plan in a single package based on the user's health condition, climbing experience, and climbing objectives. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's health condition, climbing experience, and climbing objectives into the generation AI, which analyzes the data and proposes an optimal climbing plan.

[0077] The reservation unit can make reservations for transportation and accommodation suggested by the generating AI in a batch. For example, the reservation unit can make reservations for transportation suggested by the generating AI. For example, the reservation unit can make reservations for accommodation suggested by the generating AI. For example, the reservation unit can make reservations for hiking routes suggested by the generating AI. For example, the reservation unit can make reservations for transportation in a batch. For example, the reservation unit can make reservations for accommodation in a batch. For example, the reservation unit can make reservations for hiking routes in a batch. This improves user convenience by allowing the reservation unit to make reservations for transportation and accommodation suggested by the generating AI in a batch. Some or all of the above processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input information on transportation and accommodation suggested by the generating AI into the AI, which can then analyze and make the reservations.

[0078] The reception desk can estimate the user's emotions and adjust the input method for health status, climbing experience, and climbing objectives based on the emotional data. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of health status, climbing experience, and climbing objectives. This allows the reception desk to provide more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotional data into a generative AI, which can analyze and adjust the input method.

[0079] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user's frequently entered health status, climbing experience, and climbing purpose. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the health status, climbing experience, and climbing purpose to be used at a specific time period based on the user's past input history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history into AI, which can then analyze and suggest the optimal input method.

[0080] The reception unit can simplify input by automatically acquiring the user's current location information when they input their health status, climbing experience, and climbing purpose. For example, when a user opens the app, the reception unit automatically acquires their current location and simplifies the input of their health status, climbing experience, and climbing purpose. For example, when a user enters a destination, the reception unit suggests the most suitable candidate location considering the distance from their current location. For example, when a user uses the app while on the move, the reception unit updates their current location in real time and simplifies the input of their health status, climbing experience, and climbing purpose. In this way, the reception unit can simplify input by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the current location information into AI, which can then analyze and simplify the input.

[0081] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the emotion data. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, the reception unit can enable more comfortable input by adjusting the design of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into a generative AI, which can then analyze it and adjust the design of the input interface.

[0082] The reception desk can automatically suggest potential locations based on the user's past travel history when the user inputs their health status, climbing experience, and climbing purpose. For example, the reception desk can automatically display places the user has frequently visited in the past as potential locations. For example, the reception desk can predict places the user will visit on specific days of the week or times of day and suggest them as potential locations. For example, the reception desk can analyze the user's past travel patterns and suggest the most suitable potential locations. In this way, the reception desk can automatically suggest potential locations by referring to the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input past travel history into AI, which can then analyze and suggest potential locations.

[0083] The reception desk can refer to the user's calendar information when they input their health status, climbing experience, and climbing purpose, and make suggestions based on their schedule. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the health status, climbing experience, and climbing purpose. For example, the reception desk can suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can suggest the optimal route to match the schedule based on the user's calendar information. In this way, the reception desk can make suggestions based on the schedule by referring to the user's calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input calendar information into AI, and the AI ​​can analyze it and make suggestions based on the schedule.

[0084] The generation unit can estimate the user's emotions and adjust the method of generating the climbing plan using the generation AI based on the emotion data. For example, if the user is relaxed, the generation AI will generate a climbing plan that proceeds at a leisurely pace. If the user is in a hurry, the generation AI will generate a climbing plan that emphasizes the shortest route. If the user is excited, the generation AI will generate a climbing plan that includes visually stimulating effects. In this way, the generation unit can provide a more appropriate plan by adjusting the method of generating the climbing plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which will analyze it and adjust the method of generating the climbing plan.

[0085] The generation unit can propose the optimal route when generating a climbing plan, by referring to the user's past climbing history. For example, the generation unit proposes the optimal route based on routes the user has used in the past. For example, the generation unit proposes a route that avoids congestion based on the user's past climbing history. For example, the generation unit analyzes the user's past climbing history and proposes the most efficient route. In this way, the generation unit can propose the optimal route by referring to the user's past climbing history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input past climbing history into the generation AI, which can then analyze it and propose the optimal route.

[0086] The generation unit can optimize routes by considering real-time weather forecasts when generating climbing plans. For example, the generation unit proposes the optimal route based on real-time weather forecasts. For example, the generation unit proposes a route that avoids bad weather based on real-time weather forecasts. For example, the generation unit analyzes real-time weather forecasts and proposes the safest route. In this way, the generation unit can propose the optimal route by considering real-time weather forecasts. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input real-time weather forecasts into the generation AI, which can then analyze and propose the optimal route.

[0087] The generation unit can estimate the user's emotions, and the generating AI can adjust the level of detail in the hiking plan based on the emotion data. For example, if the user is in a hurry, the generating AI will generate a short, concise hiking plan. If the user is relaxed, the generating AI will generate a hiking plan with detailed explanations. If the user is excited, the generating AI will generate a hiking plan with visually stimulating effects. In this way, the generation unit can provide a more appropriate plan by adjusting the level of detail in the hiking plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generating AI. For example, the generation unit can input the user's emotion data into the generating AI, which can analyze it and adjust the level of detail in the hiking plan.

[0088] The generation unit can propose routes that take into account the user's current health condition when generating a hiking plan. For example, if the user is tired, the generation unit will propose the shortest route. If the user is seeking healthy exercise, the generation unit will propose a slightly longer route. If the user is feeling unwell, the generation unit will propose a route that includes rest stops. In this way, the generation unit can propose the optimal route by taking into account the user's current health condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's current health condition into the generation AI, which can then analyze it and propose the optimal route.

[0089] The generation unit can suggest the most suitable accommodation based on the user's climbing objectives when generating a climbing plan. For example, if the user wants to relax, the generation unit will suggest accommodation in a quiet environment. For example, if the user wants to challenge themselves, the generation unit will suggest accommodation close to the trailhead. For example, if the user wants a family-friendly climb, the generation unit will suggest accommodation with family-friendly facilities. In this way, the generation unit can provide a more appropriate plan by suggesting the most suitable accommodation based on the user's climbing objectives. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's climbing objectives into the generation AI, which can then analyze and suggest the most suitable accommodation.

[0090] The reservation unit can estimate the user's emotions and adjust the display method of the reservation procedure based on the emotion data. For example, if the user is nervous, the reservation unit provides a simple and highly visible display method. For example, if the user is relaxed, the reservation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the reservation unit provides a display method that gets straight to the point. In this way, the reservation unit can make reservations more comfortable by adjusting the display method of the reservation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user emotion data into a generative AI, which can then analyze it and adjust the display method of the reservation procedure.

[0091] The reservation department can suggest the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on the accommodations the user has used in the past. For example, the reservation department can suggest a reservation method that avoids congestion based on the user's past reservation history. For example, the reservation department can suggest the most efficient reservation method by analyzing the user's past reservation history. In this way, the reservation department can suggest the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input past reservation history into AI, which can then analyze and suggest the optimal reservation method.

[0092] The reservation unit can update the user's current location information in real time when making a reservation. For example, the reservation unit can update the user's current location in real time while they are traveling and suggest the optimal reservation method. For example, the reservation unit can update the user's current location in real time as they approach their destination and suggest the optimal reservation method. For example, if the user gets lost, the reservation unit can update their current location in real time and make another reservation. In this way, the reservation unit can make the optimal reservation by updating the user's current location information in real time. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the current location information into AI, which can then analyze and suggest the optimal reservation method.

[0093] The reservation system can estimate the user's emotions and determine reservation priorities based on the emotion data. For example, if the user is nervous, the reservation system will prioritize important reservations. If the user is relaxed, the reservation system will prioritize reservations containing detailed information. If the user is in a hurry, the reservation system will make reservations quickly. This allows the reservation system to make more appropriate reservations by prioritizing reservations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input user emotion data into a generative AI, which can then analyze it to determine reservation priorities.

[0094] The reservation department can suggest the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, the reservation department may prioritize suggesting accommodations close to the user's current location. For example, if the user is on the move, the reservation department may suggest the optimal reservation method considering the distance from the current location. In this way, the reservation department can suggest the optimal reservation method by taking into account the user's geographical location information. Some or all of the above processing in the reservation department may be performed using AI, for example, or without AI. For example, the reservation department can input geographical location information into AI, which can then analyze and suggest the optimal reservation method.

[0095] The reservation department can analyze a user's social media activity during the reservation process and suggest relevant reservation options. For example, the reservation department can suggest optimal reservation options based on places the user has shared on social media. For example, the reservation department can predict places of interest based on the user's social media activity and suggest reservation options. For example, the reservation department can analyze a user's social media activity and suggest reservations for relevant events and activities. In this way, the reservation department can suggest relevant reservation options by analyzing the user's social media activity. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input social media activity into AI, which can then analyze and suggest relevant reservation options.

[0096] The information gathering unit can estimate the user's emotions and adjust the display method of the collected information based on the emotion data. For example, if the user is nervous, the information gathering unit provides a simple and highly visible display method. For example, if the user is relaxed, the information gathering unit provides a display method that includes detailed information. For example, if the user is in a hurry, the information gathering unit provides a display method that gets straight to the point. In this way, the information gathering unit can provide more appropriate information by adjusting the display method of the collected information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's emotion data into the generative AI, which can then analyze it and adjust the display method of the collected information.

[0097] The information gathering unit can provide optimal information by referring to the user's past information gathering history when gathering information. For example, the information gathering unit can provide optimal information based on information the user has collected in the past. For example, the information gathering unit can provide relevant information from the user's past information gathering history. For example, the information gathering unit can analyze the user's past information gathering history and provide the most relevant information. In this way, the information gathering unit can provide optimal information by referring to the user's past information gathering history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history into AI, and the AI ​​can analyze it and provide optimal information.

[0098] The information gathering unit can provide information while considering real-time weather forecasts and traffic information. For example, the information gathering unit can provide optimal information based on real-time weather forecasts. For example, the information gathering unit can provide optimal information while considering real-time traffic information. For example, the information gathering unit can analyze real-time weather forecasts and traffic information and provide the most relevant information. In this way, the information gathering unit can provide optimal information by considering real-time weather forecasts and traffic information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input real-time weather forecasts and traffic information into AI, which can then analyze and provide optimal information.

[0099] The information gathering unit can estimate the user's emotions and determine the priority of information gathering based on the emotion data. For example, if the user is stressed, the information gathering unit will prioritize providing important information. For example, if the user is relaxed, the information gathering unit will prioritize providing detailed information. For example, if the user is in a hurry, the information gathering unit will provide information quickly. This allows the information gathering unit to provide more appropriate information by determining the priority of information gathering according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's emotion data into the generative AI, which can then analyze it to determine the priority of information gathering.

[0100] The information gathering unit can provide optimal information by considering the user's geographical location information during information gathering. For example, the information gathering unit may prioritize providing information that is close to the user's current location. For example, the information gathering unit may prioritize providing information that is close to the user's destination. For example, if the user is on the move, the information gathering unit may provide optimal information by considering the distance from the current location. In this way, the information gathering unit can provide optimal information by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input geographical location information into AI, and the AI ​​can analyze it to provide optimal information.

[0101] The learning unit can estimate the user's emotions and select training data based on the emotion data. For example, if the user is relaxed, the learning unit prioritizes learning behavioral data in a relaxed state. For example, if the user is tense, the learning unit prioritizes learning behavioral data in a tense state. For example, if the user is excited, the learning unit prioritizes learning behavioral data in an excited state. This allows the learning unit to perform more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's emotion data into a generative AI, which can then analyze it and select training data.

[0102] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the most effective learning algorithm. In this way, the learning unit can optimize the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI, which can then analyze and optimize the learning algorithm.

[0103] The learning unit can weight the training data based on the user's behavior history during training. For example, the learning unit can weight important data based on the user's behavior history. For example, the learning unit can weight highly relevant data from the user's behavior history. For example, the learning unit can analyze the user's behavior history and weight the most relevant data. This allows the learning unit to perform more appropriate training by weighting the training data based on the user's behavior history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the behavior history into AI, which can then analyze and weight the training data.

[0104] The learning unit can estimate the user's emotions and adjust the learning frequency based on the emotion data. For example, the learning unit increases the learning frequency when the user is relaxed. For example, the learning unit decreases the learning frequency when the user is tense. For example, the learning unit adjusts the learning frequency when the user is excited. This allows the learning unit to perform more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's emotion data into the generative AI, which can then analyze it and adjust the learning frequency.

[0105] The learning unit can select training data based on the user's health status during training. For example, the learning unit can select the optimal training data based on the user's health status. For example, the learning unit can select highly relevant training data from the user's health status. For example, the learning unit can analyze the user's health status and select the most relevant training data. This allows the learning unit to perform more appropriate training by selecting training data based on the user's health status. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input health status data into AI, which can then analyze and select training data.

[0106] The environmental considerations unit can estimate the user's emotions and adjust its approach to suggesting environmentally friendly solutions based on the emotion data. For example, if the user is relaxed, the environmental considerations unit will prioritize environmentally friendly suggestions. If the user is stressed, the environmental considerations unit will provide concise environmentally friendly suggestions. If the user is excited, the environmental considerations unit will visually highlight environmentally friendly suggestions. This allows the environmental considerations unit to provide more appropriate suggestions by adjusting its approach to environmental considerations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the environmental considerations unit may be performed using AI or not. For example, the environmental considerations unit can input user emotion data into a generative AI, which can then analyze and adjust its approach to suggesting environmentally friendly solutions.

[0107] The environmental considerations unit can make optimal suggestions by referring to the user's past environmentally conscious actions when proposing environmental considerations. For example, the environmental considerations unit makes optimal suggestions based on the user's past environmentally conscious actions. For example, the environmental considerations unit makes relevant suggestions based on the user's past environmentally conscious actions. For example, the environmental considerations unit analyzes the user's past environmentally conscious actions and makes the most relevant suggestions. In this way, the environmental considerations unit can make optimal suggestions by referring to the user's past environmentally conscious actions. Some or all of the above processing in the environmental considerations unit may be performed using AI, for example, or without AI. For example, the environmental considerations unit can input past environmentally conscious actions into AI, which can then analyze and make optimal suggestions.

[0108] The Environmental Consideration Department can make proposals that take real-time environmental information into account when proposing environmental considerations. For example, the Environmental Consideration Department can make optimal proposals based on real-time environmental information. For example, the Environmental Consideration Department can make relevant proposals from real-time environmental information. For example, the Environmental Consideration Department can analyze real-time environmental information and make the most relevant proposals. In this way, the Environmental Consideration Department can make optimal proposals by taking real-time environmental information into account. Some or all of the above processing in the Environmental Consideration Department may be performed using AI, for example, or without AI. For example, the Environmental Consideration Department can input real-time environmental information into AI, which can then analyze and make optimal proposals.

[0109] The environmental considerations unit can estimate the user's emotions and determine the priority of environmental considerations based on the emotion data. For example, if the user is relaxed, the environmental considerations unit will prioritize environmentally friendly suggestions. For example, if the user is stressed, the environmental considerations unit will provide concise environmentally friendly suggestions. For example, if the user is excited, the environmental considerations unit will visually highlight environmentally friendly suggestions. This allows the environmental considerations unit to provide more appropriate suggestions by determining the priority of environmental considerations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environmental considerations unit may be performed using AI or not using AI. For example, the environmental considerations unit can input user emotion data into a generative AI, which can then analyze it to determine the priority of environmental considerations.

[0110] The environmental considerations unit can make optimal suggestions by considering the user's geographical location when proposing environmentally friendly options. For example, the environmental considerations unit may prioritize environmentally friendly suggestions that are close to the user's current location. For example, if the user is on the move, the environmental considerations unit will make optimal suggestions by considering the distance from the current location. In this way, the environmental considerations unit can make optimal suggestions by considering the user's geographical location. Some or all of the above processing in the environmental considerations unit may be performed using AI, for example, or without AI. For example, the environmental considerations unit can input geographical location information into AI, which can then analyze and make optimal suggestions.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception section allows users to input their health status, climbing experience, and climbing goals, as well as their dietary restrictions and allergy information. For example, if a user has an allergy to a specific food, inputting this information allows the generation section to suggest accommodations and meal plans that take allergies into consideration. Similarly, if a user has specific dietary restrictions (vegetarian, vegan, gluten-free, etc.), the system can suggest appropriate meal plans based on this information. This allows users to enjoy climbing with peace of mind. Furthermore, the reception section inputs the user's dietary restrictions and allergy information into the learning section, which then uses this information to provide the user with the most suitable suggestions.

[0113] The information gathering unit can collect information about the user's health status and incorporate it into the climbing plan. For example, it can collect data on the user's heart rate and blood pressure, and adjust the difficulty of the climbing route based on this data. It can also suggest rest points and hydration timings according to the user's health status. Furthermore, the information gathering unit inputs data on the user's health status into the learning unit, which can then make optimal suggestions to the user based on this data. This allows users to enjoy climbing safely and healthily.

[0114] The learning unit can estimate the user's emotions and select training data based on the emotion data. For example, if the user is relaxed, it will prioritize learning behavioral data from a relaxed state. If the user is tense, it will prioritize learning behavioral data from a tense state. If the user is excited, it will prioritize learning behavioral data from an excited state. This allows the learning unit to select training data according to the user's emotions, enabling more appropriate learning. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input the user's emotion data into a generative AI, which can then analyze and select training data.

[0115] The environmental considerations unit can estimate the user's emotions and adjust its approach to suggesting environmentally friendly solutions based on the emotion data. For example, if the user is relaxed, it prioritizes environmentally friendly suggestions. If the user is stressed, it provides concise environmentally friendly suggestions. If the user is excited, it visually highlights environmentally friendly suggestions. This allows the environmental considerations unit to provide more appropriate suggestions by adjusting its approach according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the environmental considerations unit may be performed using AI or not. For example, the environmental considerations unit can input user emotion data into a generative AI, which can then analyze and adjust its approach to suggesting environmentally friendly solutions.

[0116] The generation unit can estimate the user's emotions and adjust the method of generating the hiking plan using the generation AI based on the emotion data. For example, if the user is relaxed, the generation AI will generate a hiking plan that proceeds at a leisurely pace. If the user is in a hurry, the generation AI will generate a hiking plan that emphasizes the shortest route. If the user is excited, the generation AI will generate a hiking plan that includes visually stimulating effects. In this way, the generation unit can provide a more appropriate plan by adjusting the method of generating the hiking plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze it and adjust the method of generating the hiking plan.

[0117] The reservation unit can estimate the user's emotions and adjust the display method of the reservation procedure based on the emotion data. For example, if the user is nervous, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. In this way, the reservation unit can make reservations more comfortable by adjusting the display method of the reservation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input the user's emotion data into a generative AI, which can then analyze it and adjust the display method of the reservation procedure.

[0118] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as suggestions the user's health status, climbing experience, and climbing purpose that they have frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Based on the user's past input history, it can predict and suggest the health status, climbing experience, and climbing purpose to be used at a specific time of day. In this way, the reception desk can suggest the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history into AI, which can then analyze and suggest the optimal input method.

[0119] The information gathering unit can provide optimal information by referring to the user's past information gathering history when gathering information. For example, it can provide optimal information based on information the user has collected in the past. It can provide relevant information from the user's past information gathering history. It can analyze the user's past information gathering history and provide the most relevant information. In this way, the information gathering unit can provide optimal information by referring to the user's past information gathering history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history into AI, and the AI ​​can analyze it to provide optimal information.

[0120] The generation unit can suggest routes while considering the user's current health condition when generating a hiking plan. For example, if the user is tired, it will suggest the shortest route. If the user is seeking healthy exercise, it will suggest a slightly longer route. If the user is feeling unwell, it will suggest a route that includes rest stops. In this way, the generation unit can suggest the optimal route by considering the user's current health condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's current health condition into the generation AI, which can then analyze it and suggest the optimal route.

[0121] The reservation department can suggest the optimal reservation method when a reservation is made, by referring to the user's past reservation history. For example, it can suggest the optimal reservation method based on the accommodations the user has used in the past. It can suggest a reservation method that avoids congestion based on the user's past reservation history. It can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, the reservation department can suggest the optimal reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input past reservation history into AI, which can then analyze and suggest the optimal reservation method.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk enters the user's health status, climbing experience, and climbing purpose. The user's health status includes heart rate, blood pressure, and medical history. Climbing experience includes the number of times climbed in the past and the difficulty level of the mountains climbed. Climbing purpose includes recreation, training, research, etc. Users can enter purposes such as "I want to relax," "I want to challenge myself," or "It's family-friendly." Step 2: The generation unit analyzes the information entered by the reception unit and generates the optimal climbing plan. Using generation AI, the generation unit proposes transportation, accommodation, and climbing routes based on the user's health condition, climbing experience, and climbing purpose. For example, if the user enters "I want to relax," it will suggest relaxing climbing routes and accommodations; if the user enters "I want to challenge myself," it will suggest challenging climbing routes and accommodations; and if the user enters "family-friendly," it will suggest family-friendly climbing routes and accommodations. Step 3: The booking unit makes reservations based on the climbing plan generated by the generation unit. The booking unit can make reservations for transportation and accommodation suggested by the generation AI all at once. For example, it can make reservations for transportation, accommodation, and climbing routes.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, information collection unit, learning unit, and environmental consideration unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and takes input of the user's health status, climbing experience, and climbing purpose. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal climbing plan using generation AI. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14 and makes a reservation based on the generated climbing plan. The information collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects real-time weather forecasts and traffic information. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and reservation history. The environmental consideration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes environmentally friendly transportation and accommodation options. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, information collection unit, learning unit, and environmental consideration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes input of the user's health status, climbing experience, and climbing purpose. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal climbing plan using generation AI. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and makes a reservation based on the generated climbing plan. The information collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects real-time weather forecasts and traffic information. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and reservation history. The environmental consideration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes suggestions for environmentally friendly transportation and accommodation. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, information collection unit, learning unit, and environmental consideration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and takes input of the user's health status, climbing experience, and climbing purpose. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal climbing plan using generation AI. The reservation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and makes a reservation based on the generated climbing plan. The information collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects real-time weather forecasts and traffic information. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and reservation history. The environmental consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes environmentally friendly transportation and accommodation options. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, information collection unit, learning unit, and environmental consideration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes input of the user's health status, climbing experience, and climbing purpose. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal climbing plan using generation AI. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 and makes a reservation based on the generated climbing plan. The information collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects real-time weather forecasts and traffic information. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns the user's behavior history and reservation history. The environmental consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes environmentally friendly transportation and accommodation options. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A reception area where users enter their health status, climbing experience, and climbing purpose, A generation unit analyzes the information entered by the reception unit and generates an optimal climbing plan, The system includes a reservation unit that makes reservations based on the climbing plans generated by the generation unit. A system characterized by the following features. (Note 2) It will also be equipped with an information gathering unit to take into account real-time weather forecasts and traffic information. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes a learning unit that learns from the user's behavior history and reservation history. The system described in Appendix 1, characterized by the features described herein. (Note 4) We will further develop an environmental considerations department that proposes environmentally friendly transportation and accommodation options. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the user's health status, climbing experience, and climbing goals, the system provides comprehensive suggestions for transportation, accommodation, and climbing routes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reservation section is, This service allows you to book transportation and accommodation options suggested by the AI ​​in one go. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input methods for health status, climbing experience, and climbing goals based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input their health status, climbing experience, and climbing purpose, the system automatically retrieves their current location information to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their health status, climbing experience, and climbing purpose, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their health status, climbing experience, and climbing goals, the system will refer to their calendar information to provide suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the method of generating climbing plans using AI based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a hiking plan, the system will suggest the optimal route based on the user's past hiking history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a hiking plan, the route is optimized by taking real-time weather forecasts into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The AI ​​estimates the user's emotions and adjusts the level of detail in the hiking plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a hiking plan, the system suggests routes that take into account the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a climbing plan, the system suggests the most suitable accommodation based on the user's climbing objectives. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, The system estimates the user's emotions and adjusts how the reservation process is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, When you make a reservation, we will suggest the most suitable reservation method based on your past reservation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, When making a reservation, the user's current location information is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When making a reservation, we will suggest the optimal reservation method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's social media activity and suggests relevant reservation options. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information gathering unit, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned information gathering unit, When gathering information, we refer to the user's past information gathering history to provide the most relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned information gathering unit, When gathering information, we provide information while taking into account real-time weather forecasts and traffic information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned information gathering unit, It estimates the user's emotions and determines the priority of information gathering based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned information gathering unit, When collecting information, we provide the most relevant information by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the training data is weighted based on the user's behavior history. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned learning unit, During training, training data is selected based on the user's health status. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned environmentally friendly section is We estimate the user's emotions and adjust the method of proposing environmentally conscious solutions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned environmentally friendly section is When proposing environmentally conscious solutions, we refer to the user's past environmentally conscious actions to provide the most suitable suggestions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned environmentally friendly section is When proposing environmentally conscious solutions, we will take real-time environmental information into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned environmentally friendly section is It estimates user emotions and determines environmental priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned environmentally friendly section is When proposing environmentally conscious solutions, we take the user's geographical location into consideration to provide the most optimal proposal. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their health status, climbing experience, and climbing purpose, A generation unit analyzes the information entered by the reception unit and generates an optimal climbing plan, The system includes a reservation unit that makes reservations based on the climbing plans generated by the generation unit. A system characterized by the following features.

2. It will also be equipped with an information gathering unit to take into account real-time weather forecasts and traffic information. The system according to feature 1.

3. It also includes a learning unit that learns from the user's behavior history and reservation history. The system according to feature 1.

4. We will further develop an environmental considerations department that proposes environmentally friendly transportation and accommodation options. The system according to feature 1.

5. The generating unit is Based on the user's health status, climbing experience, and climbing goals, the system provides comprehensive suggestions for transportation, accommodation, and climbing routes. The system according to feature 1.

6. The aforementioned reservation section is, The AI ​​generates and suggests transportation and accommodation options, which can then be booked all at once. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input methods for health status, climbing experience, and climbing goals based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users input their health status, climbing experience, and climbing purpose, the system automatically retrieves their current location information to simplify the input process. The system according to feature 1.

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