System

Abstract

An object of a system according to an embodiment is to automatically create a travel plan for solving an over-tourism problem while saving time and effort of a tourist.SOLUTION: A system includes a condition setting part, a travel plan generation part, a reservation procedure part, and a congestion analysis part. The condition setting unit receives a desired condition of a user. The travel plan creation unit creates a travel plan based on the desired condition received by the condition setting unit. The reservation procedure unit performs a reservation procedure based on the travel plan created by the travel plan creation unit. The congestion analysis unit analyzes a congestion situation of a tourist spot in real time, and proposes a travel destination so that tourists do not concentrate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of not being able to adequately automatically create travel plans that would reduce the effort required for tourists while also addressing the problem of overtourism.

[0005] The system according to the embodiment aims to automatically create travel plans that solve the problem of overtourism while reducing the hassle for tourists. [Means for solving the problem]

[0006] The system according to the embodiment includes a condition setting unit, a travel plan generation unit, a reservation procedure unit, and a congestion analysis unit. The condition setting unit accepts desired conditions from a user. The travel plan generation unit creates a travel plan based on the desired conditions accepted by the condition setting unit. The reservation procedure unit performs reservation procedures based on the travel plan created by the travel plan generation unit. The congestion analysis unit analyzes the congestion situation at tourist destinations in real time and suggests travel destinations that avoid tourist concentration. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create travel plans that solve the problem of overtourism while saving tourists time and effort. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The travel plan creation system according to an embodiment of the present invention is a system that eliminates the hassle of tourists' travels by allowing the generation AI to create travel plans and make reservations simply by setting conditions. As a result, the travel plan creation system takes into consideration the dispersion of tourists when selecting travel destinations, thereby solving the problem of overtourism.

[0029] A travel plan creation system according to an embodiment includes a condition setting unit, a travel plan generation unit, a reservation procedure unit, and a congestion analysis unit. The condition setting unit accepts a user's desired conditions. For example, the user inputs the user's travel destination, budget, schedule, and activities of interest. The travel plan generation unit creates a travel plan based on the desired conditions accepted by the condition setting unit. For example, the generation AI analyzes the user's desired conditions and proposes an optimal travel plan. The reservation procedure unit performs reservation procedures based on the travel plan created by the travel plan generation unit. For example, the generation AI automatically reserves accommodations and transportation, purchases tickets to tourist attractions, and so on. The congestion analysis unit analyzes the congestion situation at tourist attractions in real time and suggests travel destinations to avoid tourist concentration. For example, if a particular tourist attraction is crowded, the generation AI suggests other tourist attractions to disperse tourists. This allows the travel plan creation system according to an embodiment to reduce the effort required for tourists' travel and solve the problem of overtourism. For example, it can draw attention to tourist attractions with fewer tourists, thereby achieving a balanced overall tourist attraction population. Furthermore, users can easily complete their travel preparations and enjoy a comfortable trip.

[0030] The travel plan generation unit learns the user's past travel history and preferences, and can propose more personalized travel plans. For example, the generation AI in the travel plan generation unit analyzes the user's past travel history and learns the user's preferences based on data on places visited and activities participated in. For example, it refers to ratings of tourist spots and accommodations visited in the past. The travel plan generation unit also analyzes photos and reviews from the user's past trips to extract preference trends. For example, it will suggest tourist spots rich in nature to a user who likes natural scenery. The travel plan generation unit also combines the user's past travel history with their current interests, and the generation AI proposes the optimal travel plan. For example, it will suggest new tourist spots with an atmosphere similar to places visited in the past. This makes it possible to provide travel plans that meet the user's preferences.

[0031] The travel plan generation unit can analyze a user's social media posts and reviews and create a travel plan that reflects the user's hidden interests and preferences. For example, the travel plan generation unit uses a generation AI to analyze a user's social media posts and extract the user's interests from travel-related posts and photos. For example, it can identify interests based on specific hashtags or location information. The travel plan generation unit also analyzes travel reviews written by the user to identify highly rated activities and tourist destinations. For example, it can extract keywords frequently mentioned in reviews. The travel plan generation unit also analyzes the user's following relationships and like history on social media to identify tourist destinations and activities of interest. For example, it can refer to posts by travel influencers the user follows. This makes it possible to provide a travel plan that reflects the user's hidden interests and preferences.

[0032] The travel plan generation unit can propose healthy travel plans taking into account the user's health condition and fitness level. For example, the travel plan generation unit collects the user's health data, and the generation AI proposes healthy travel plans based on that data. For example, it proposes sightseeing routes that take walking distance and calorie consumption into consideration. The travel plan generation unit also works with a fitness app to create travel plans based on the user's exercise habits and health goals. For example, it proposes plans that include activities such as hiking and cycling. The travel plan generation unit also suggests relaxing travel destinations and activities based on the user's health condition. For example, it proposes plans that include hot springs and spa resorts. This makes it possible to provide travel plans that suit the user's health condition.

[0033] The travel plan generation unit can create an optimal travel plan for a family trip or a trip with a pet, taking into account the user's family composition and whether or not they have a pet. The travel plan generation unit, for example, takes into account the user's family composition and proposes a travel plan that the whole family can enjoy. For example, it creates a plan that includes activities for children and family-friendly accommodation. The travel plan generation unit also proposes pet-friendly accommodation and tourist spots to a user who wishes to travel with a pet. For example, it creates a plan that includes tourist spots that allow pets and activities specifically for pets. The travel plan generation unit also customizes the travel plan depending on the family composition and whether or not the user has a pet. For example, it proposes tourist spots and activities that can be enjoyed by multiple generations together. This makes it possible to provide an optimal plan for a family trip or a trip with a pet.

[0034] The reservation procedure section can search across multiple reservation sites and automatically select the most cost-effective reservation option. For example, the generation AI in the reservation procedure section can search across multiple reservation sites and automatically select the most cost-effective options for accommodation and transportation. For example, it can use a price comparison site to find the lowest price. The generation AI in the reservation procedure section can also consider the user's budget and suggest the optimal reservation option. For example, it can select accommodation and transportation that offer the best service within the budget. The generation AI in the reservation procedure section can also monitor price fluctuations in real time and make reservations at the best possible time. For example, it can automatically confirm reservations the moment prices drop. This makes it possible to provide the most cost-effective reservation options.

[0035] The reservation procedure unit learns the user's past reservation history and can prioritize reserving the user's preferred accommodations and means of transportation. In the reservation procedure unit, for example, the generation AI analyzes the user's past reservation history and identifies the user's preferred accommodations and means of transportation. For example, it prioritizes reserving accommodations that have been highly rated in the past. In addition, the reservation procedure unit uses the generation AI to suggest optimal reservation options based on the user's past reservation history. For example, it may prioritize selecting airlines or hotel chains that have been used in the past. In addition, the reservation procedure unit uses the generation AI to learn the user's preferences and suggest customized reservation options based on the user's past reservation history. For example, for a user who prefers a particular brand or service, it will suggest options for that brand. This makes it possible to provide reservation options that meet the user's preferences.

[0036] The reservation procedure unit can propose the most advantageous reservation option by taking into account the user's point program and member benefits. In the reservation procedure unit, for example, the generation AI analyzes the user's point program and member benefits and proposes the most advantageous reservation option. For example, it selects accommodation and transportation methods that will allow the user to accumulate the most points. In addition, the reservation procedure unit considers the user's member benefits and the generation AI proposes the optimal reservation option. For example, it prioritizes the selection of accommodation and transportation methods that offer member discounts. In addition, the reservation procedure unit makes the most of the user's point program and proposes the most advantageous reservation option. For example, it proposes free accommodation or upgrades by using points. This makes it possible to provide reservation options that make the most of the user's point program and member benefits.

[0037] The reservation procedure unit can automatically adjust the user's schedule and suggest the optimal travel itinerary. In the reservation procedure unit, for example, the generation AI analyzes the user's calendar and schedule and suggests the optimal travel itinerary. For example, the travel itinerary is adjusted taking work and school schedules into consideration. In addition, the reservation procedure unit automatically adjusts the optimal travel itinerary based on the user's schedule using the generation AI. For example, it suggests a travel plan by selecting a period without any important plans. In addition, in the reservation procedure unit, the generation AI analyzes the user's schedule in real time and suggests the optimal travel itinerary. For example, it creates a flexible travel plan that can accommodate sudden schedule changes. This makes it possible to provide the optimal travel itinerary that suits the user's schedule.

[0038] The congestion analysis unit analyzes past congestion data, predicts future congestion conditions, and can suggest travel destinations. For example, the generation AI in the congestion analysis unit analyzes past congestion data and develops an algorithm to predict future congestion conditions. For example, it predicts peak congestion times at specific tourist destinations based on past data and suggests travel destinations that avoid crowds. The congestion analysis unit also predicts future congestion conditions based on past congestion data and suggests optimal travel destinations to users. For example, it creates travel plans that avoid periods when congestion is expected. The congestion analysis unit also builds a system in which the generation AI analyzes past congestion data and predicts future congestion conditions. For example, it predicts congestion based on specific events or holidays and suggests travel destinations. This makes it possible to predict future congestion conditions and suggest optimal travel destinations.

[0039] The congestion analysis unit can analyze real-time traffic data and propose the smoothest route for travel. For example, the generation AI analyzes real-time traffic data to propose the smoothest route for travel. For example, the route is optimized based on information about traffic congestion and public transportation delays. The congestion analysis unit also analyzes real-time traffic data based on the user's current location and destination and proposes the optimal travel route. For example, it presents an alternative route to avoid congestion. The congestion analysis unit also builds a system in which the generation AI proposes the smoothest route for travel based on real-time traffic data. For example, it dynamically adjusts the route according to changes in traffic conditions. This makes it possible to provide the smoothest route for travel.

[0040] The congestion analysis unit can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, the generation AI in the congestion analysis unit analyzes congestion conditions in different seasons and time periods and suggests the optimal time to visit. For example, it suggests a time that avoids peak congestion at tourist destinations. The congestion analysis unit also analyzes congestion conditions in different seasons and time periods based on the user's desired visit time and suggests the optimal time to visit. For example, it suggests weekdays or off-seasons when congestion is less. The congestion analysis unit also builds a system in which the generation AI analyzes congestion conditions in different seasons and time periods and suggests the optimal time to visit. For example, it predicts congestion based on past data and suggests the best time to visit. This makes it possible to provide the optimal time to visit.

[0041] The congestion analysis unit can discover and suggest new, uncrowded but attractive tourist destinations based on the user's interests. For example, the generation AI analyzes the user's interests and discovers and suggests new, uncrowded but attractive tourist destinations. For example, it suggests unexplored tourist destinations based on the user's interests. The congestion analysis unit also proposes new, uncrowded but attractive tourist destinations based on the user's interests. For example, it suggests tourist destinations related to themes that the user has shown interest in. The congestion analysis unit also builds a system in which the generation AI analyzes the user's interests and discovers and suggests new, uncrowded but attractive tourist destinations. For example, it searches a database of tourist destinations based on the user's interests. This makes it possible to provide new, uncrowded but attractive tourist destinations.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The travel plan generation unit can propose healthy travel plans taking into account the user's health condition and fitness level. For example, the generation AI collects the user's health data and proposes healthy travel plans based on that data. For example, it proposes sightseeing routes that take walking distance and calorie consumption into consideration. The travel plan generation unit also works with a fitness app to create travel plans based on the user's exercise habits and health goals. For example, it proposes plans that include activities such as hiking and cycling. The travel plan generation unit also suggests relaxing travel destinations and activities based on the user's health condition. For example, it proposes plans that include hot springs and spa resorts. This makes it possible to provide travel plans that suit the user's health condition.

[0044] The travel plan generation unit can create an optimal travel plan for a family trip or a trip with a pet, taking into account the user's family composition and whether or not they have a pet. For example, the travel plan generation unit can propose a travel plan that the whole family can enjoy, taking into account the user's family composition. For example, the travel plan generation unit can create a plan that includes activities for children and family-friendly accommodations. The travel plan generation unit can also propose pet-friendly accommodations and tourist spots to a user who wishes to travel with a pet. For example, the travel plan generation unit can create a plan that includes tourist spots that allow pets and activities specifically for pets. The travel plan generation unit can also customize the travel plan depending on the family composition and whether or not the user has a pet. For example, the travel plan generation unit can propose tourist spots and activities that can be enjoyed by multiple generations together. This makes it possible to provide an optimal plan for a family trip or a trip with a pet.

[0045] The reservation process can search multiple reservation sites across the network and automatically select the most cost-effective reservation option. For example, the generation AI can search multiple reservation sites across the network and automatically select the most cost-effective accommodation and transportation options. For example, it can use a price comparison site to find the lowest price. The reservation process also allows the generation AI to consider the user's budget and suggest the optimal reservation option. For example, it can select accommodation and transportation options that offer the best service within the budget. The generation AI can also monitor price fluctuations in real time and make reservations at the best possible time. For example, it can automatically confirm reservations the moment the price drops. This allows the system to provide the most cost-effective reservation options.

[0046] The reservation procedure unit learns the user's past reservation history and can prioritize reservations for accommodations and transportation methods that the user prefers. For example, the generation AI analyzes the user's past reservation history to identify their preferred accommodations and transportation methods. For example, it will prioritize reservations for accommodations that have been highly rated in the past. The reservation procedure unit also uses the generation AI to suggest optimal reservation options based on the user's past reservation history. For example, it will prioritize selecting airlines and hotel chains that have been used in the past. The reservation procedure unit also uses the generation AI to learn the user's preferences and suggest customized reservation options based on the user's past reservation history. For example, for a user who prefers a particular brand or service, it will suggest options for that brand. This makes it possible to provide reservation options that meet the user's preferences.

[0047] The congestion analysis unit analyzes past congestion data, predicts future congestion levels, and can suggest travel destinations. For example, the generation AI analyzes past congestion data and develops an algorithm to predict future congestion levels. For example, it predicts peak congestion times at specific tourist destinations based on past data and suggests travel destinations that avoid crowds. The congestion analysis unit also uses the generation AI to predict future congestion levels based on past congestion data and suggests optimal travel destinations for users. For example, it creates travel plans that avoid periods when congestion is expected. The congestion analysis unit also uses the generation AI to analyze past congestion data and build a system that predicts future congestion levels. For example, it predicts congestion based on specific events or holidays and suggests travel destinations. This makes it possible to predict future congestion levels and suggest optimal travel destinations.

[0048] The congestion analysis unit can analyze real-time traffic data and propose the smoothest route for travel. For example, the generation AI analyzes real-time traffic data and proposes the smoothest route for travel. For example, it optimizes the route based on information on traffic congestion and public transportation delays. The congestion analysis unit also analyzes real-time traffic data based on the user's current location and destination and proposes the optimal travel route. For example, it presents an alternative route to avoid congestion. The congestion analysis unit also builds a system in which the generation AI proposes the smoothest route for travel based on real-time traffic data. For example, it dynamically adjusts the route according to changes in traffic conditions. This makes it possible to provide the smoothest route for travel.

[0049] The congestion analysis unit can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, the generation AI can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, it can suggest a time that avoids peak congestion at tourist destinations. The congestion analysis unit can also analyze congestion conditions in different seasons and time periods based on the user's desired visit time and suggest the optimal time to visit. For example, it can suggest weekdays or off-seasons when congestion is less. The congestion analysis unit can also build a system in which the generation AI can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, it can predict congestion based on past data and suggest the best time to visit. This makes it possible to provide the optimal time to visit.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The condition setting unit accepts the user's desired conditions, such as the user's travel destination, budget, schedule, and activities of interest. Step 2: The travel plan generation unit creates a travel plan based on the desired conditions accepted by the condition setting unit. For example, the generation AI analyzes the user's desired conditions and proposes the optimal travel plan. Step 3: The reservation procedure unit performs the reservation procedure based on the travel plan created by the travel plan generation unit. For example, the generation AI automatically reserves accommodations and transportation, purchases tickets to tourist attractions, etc. Step 4: The congestion analysis unit analyzes the congestion situation at tourist destinations in real time and suggests travel destinations to avoid tourist concentration. For example, if a particular tourist destination is crowded, the generation AI will suggest other tourist destinations to disperse tourists.

[0052] (Example 2) The travel plan creation system according to an embodiment of the present invention is a system that eliminates the hassle of tourists' travels by allowing the generation AI to create travel plans and make reservations simply by setting conditions. As a result, the travel plan creation system takes into consideration the dispersion of tourists when selecting travel destinations, thereby solving the problem of overtourism.

[0053] A travel plan creation system according to an embodiment includes a condition setting unit, a travel plan generation unit, a reservation procedure unit, and a congestion analysis unit. The condition setting unit accepts a user's desired conditions. For example, the user inputs the user's travel destination, budget, schedule, and activities of interest. The travel plan generation unit creates a travel plan based on the desired conditions accepted by the condition setting unit. For example, the generation AI analyzes the user's desired conditions and proposes an optimal travel plan. The reservation procedure unit performs reservation procedures based on the travel plan created by the travel plan generation unit. For example, the generation AI automatically reserves accommodations and transportation, purchases tickets to tourist attractions, and so on. The congestion analysis unit analyzes the congestion situation at tourist attractions in real time and suggests travel destinations to avoid tourist concentration. For example, if a particular tourist attraction is crowded, the generation AI suggests other tourist attractions to disperse tourists. This allows the travel plan creation system according to an embodiment to reduce the effort required for tourists' travel and solve the problem of overtourism. For example, it can draw attention to tourist attractions with fewer tourists, thereby achieving a balanced overall tourist attraction population. Furthermore, users can easily complete their travel preparations and enjoy a comfortable trip.

[0054] The travel plan generation unit learns the user's past travel history and preferences, and can propose more personalized travel plans. For example, the generation AI in the travel plan generation unit analyzes the user's past travel history and learns the user's preferences based on data on places visited and activities participated in. For example, it refers to ratings of tourist spots and accommodations visited in the past. The travel plan generation unit also analyzes photos and reviews from the user's past trips to extract preference trends. For example, it will suggest tourist spots rich in nature to a user who likes natural scenery. The travel plan generation unit also combines the user's past travel history with their current interests, and the generation AI proposes the optimal travel plan. For example, it will suggest new tourist spots with an atmosphere similar to places visited in the past. This makes it possible to provide travel plans that meet the user's preferences.

[0055] The travel plan generation unit can analyze a user's social media posts and reviews and create a travel plan that reflects the user's hidden interests and preferences. For example, the travel plan generation unit uses a generation AI to analyze a user's social media posts and extract the user's interests from travel-related posts and photos. For example, it can identify interests based on specific hashtags or location information. The travel plan generation unit also analyzes travel reviews written by the user to identify highly rated activities and tourist destinations. For example, it can extract keywords frequently mentioned in reviews. The travel plan generation unit also analyzes the user's following relationships and like history on social media to identify tourist destinations and activities of interest. For example, it can refer to posts by travel influencers the user follows. This makes it possible to provide a travel plan that reflects the user's hidden interests and preferences.

[0056] The travel plan generation unit uses the emotion estimation function to analyze the emotions of the user when entering a travel plan and can propose a travel plan that elicits positive emotions. For example, the travel plan generation unit analyzes facial expressions and voice tone when the user enters a travel plan to estimate emotions. For example, if a smile or an excited voice is detected, the travel plan generation unit proposes a plan that reflects that emotion. The travel plan generation unit also uses the emotion estimation function to provide real-time feedback on the content entered by the user to elicit positive emotions. For example, encouraging messages are displayed for activities in which the user has shown interest. The travel plan generation unit also generates a travel plan that elicits positive emotions based on the user's emotion data. For example, travel destinations and activities that have elicited positive emotions in the past are preferentially suggested. This makes it possible to provide a travel plan that elicits positive emotions from the user.

[0057] The travel plan generation unit can propose healthy travel plans taking into account the user's health condition and fitness level. For example, the travel plan generation unit collects the user's health data, and the generation AI proposes healthy travel plans based on that data. For example, it proposes sightseeing routes that take walking distance and calorie consumption into consideration. The travel plan generation unit also works with a fitness app to create travel plans based on the user's exercise habits and health goals. For example, it proposes plans that include activities such as hiking and cycling. The travel plan generation unit also suggests relaxing travel destinations and activities based on the user's health condition. For example, it proposes plans that include hot springs and spa resorts. This makes it possible to provide travel plans that suit the user's health condition.

[0058] The travel plan generation unit can create an optimal travel plan for a family trip or a trip with a pet, taking into account the user's family composition and whether or not they have a pet. The travel plan generation unit, for example, takes into account the user's family composition and proposes a travel plan that the whole family can enjoy. For example, it creates a plan that includes activities for children and family-friendly accommodation. The travel plan generation unit also proposes pet-friendly accommodation and tourist spots to a user who wishes to travel with a pet. For example, it creates a plan that includes tourist spots that allow pets and activities specifically for pets. The travel plan generation unit also customizes the travel plan depending on the family composition and whether or not the user has a pet. For example, it proposes tourist spots and activities that can be enjoyed by multiple generations together. This makes it possible to provide an optimal plan for a family trip or a trip with a pet.

[0059] The travel plan generation unit uses the emotion estimation function to analyze the user's emotions in real time when selecting a travel plan, and can propose a travel plan that elicits the most positive response. For example, when the user selects a travel plan, the travel plan generation unit analyzes facial expressions and voice tone in real time to estimate emotions. For example, if a smile or an excited voice is detected, the travel plan generation unit proposes a plan that reflects that emotion. The travel plan generation unit also uses the emotion estimation function to provide real-time feedback on the plan selected by the user to elicit positive emotions. For example, it displays encouraging messages for activities in which the user has shown interest. The travel plan generation unit also generates a travel plan that elicits positive emotions based on the user's emotion data. For example, it prioritizes the suggestion of travel destinations and activities that have elicited positive emotions in the past. This makes it possible to provide a travel plan that elicits positive emotions from the user.

[0060] The reservation procedure section can search across multiple reservation sites and automatically select the most cost-effective reservation option. For example, the generation AI in the reservation procedure section can search across multiple reservation sites and automatically select the most cost-effective options for accommodation and transportation. For example, it can use a price comparison site to find the lowest price. The generation AI in the reservation procedure section can also consider the user's budget and suggest the optimal reservation option. For example, it can select accommodation and transportation that offer the best service within the budget. The generation AI in the reservation procedure section can also monitor price fluctuations in real time and make reservations at the best possible time. For example, it can automatically confirm reservations the moment prices drop. This makes it possible to provide the most cost-effective reservation options.

[0061] The reservation procedure unit learns the user's past reservation history and can prioritize reserving the user's preferred accommodations and means of transportation. In the reservation procedure unit, for example, the generation AI analyzes the user's past reservation history and identifies the user's preferred accommodations and means of transportation. For example, it prioritizes reserving accommodations that have been highly rated in the past. In addition, the reservation procedure unit uses the generation AI to suggest optimal reservation options based on the user's past reservation history. For example, it may prioritize selecting airlines or hotel chains that have been used in the past. In addition, the reservation procedure unit uses the generation AI to learn the user's preferences and suggest customized reservation options based on the user's past reservation history. For example, for a user who prefers a particular brand or service, it will suggest options for that brand. This makes it possible to provide reservation options that meet the user's preferences.

[0062] The reservation procedure unit can use the emotion estimation function to provide an interface for reducing stress when a user performs a reservation procedure. The reservation procedure unit, for example, uses the emotion estimation function to analyze stress in real time when a user performs a reservation procedure and provide an interface for reducing stress. For example, relaxing music is played when the user feels stressed. The reservation procedure unit also customizes the interface for the reservation procedure based on the user's emotion data. For example, if the user feels stressed, the reservation procedure unit simplifies the procedure steps. The reservation procedure unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when a user performs a reservation procedure. For example, encouraging messages and success stories are presented. This makes it possible to provide an interface that reduces the user's stress.

[0063] The reservation procedure unit can propose the most advantageous reservation option by taking into account the user's point program and member benefits. In the reservation procedure unit, for example, the generation AI analyzes the user's point program and member benefits and proposes the most advantageous reservation option. For example, it selects accommodation and transportation methods that will allow the user to accumulate the most points. In addition, the reservation procedure unit considers the user's member benefits and the generation AI proposes the optimal reservation option. For example, it prioritizes the selection of accommodation and transportation methods that offer member discounts. In addition, the reservation procedure unit makes the most of the user's point program and proposes the most advantageous reservation option. For example, it proposes free accommodation or upgrades by using points. This makes it possible to provide reservation options that make the most of the user's point program and member benefits.

[0064] The reservation procedure unit can automatically adjust the user's schedule and suggest the optimal travel itinerary. In the reservation procedure unit, for example, the generation AI analyzes the user's calendar and schedule and suggests the optimal travel itinerary. For example, the travel itinerary is adjusted taking work and school schedules into consideration. In addition, the reservation procedure unit automatically adjusts the optimal travel itinerary based on the user's schedule using the generation AI. For example, it suggests a travel plan by selecting a period without any important plans. In addition, in the reservation procedure unit, the generation AI analyzes the user's schedule in real time and suggests the optimal travel itinerary. For example, it creates a flexible travel plan that can accommodate sudden schedule changes. This makes it possible to provide the optimal travel itinerary that suits the user's schedule.

[0065] The reservation procedure unit can use the emotion estimation function to analyze the emotions of a user when performing the reservation procedure in real time and suggest reservation options that elicit positive emotions. For example, the reservation procedure unit can use the emotion estimation function to analyze the emotions of a user when performing the reservation procedure in real time and suggest reservation options that elicit positive emotions. For example, if the user is excited, it can present a special offer. The reservation procedure unit also customizes the interface for the reservation procedure based on the user's emotion data. For example, if the user expresses positive emotions, it can display special offers or discounts. The reservation procedure unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user performs the reservation procedure. For example, it can present encouraging messages or success stories. This makes it possible to provide reservation options that elicit positive emotions in the user.

[0066] The congestion analysis unit analyzes past congestion data, predicts future congestion conditions, and can suggest travel destinations. For example, the generation AI in the congestion analysis unit analyzes past congestion data and develops an algorithm to predict future congestion conditions. For example, it predicts peak congestion times at specific tourist destinations based on past data and suggests travel destinations that avoid crowds. The congestion analysis unit also predicts future congestion conditions based on past congestion data and suggests optimal travel destinations to users. For example, it creates travel plans that avoid periods when congestion is expected. The congestion analysis unit also builds a system in which the generation AI analyzes past congestion data and predicts future congestion conditions. For example, it predicts congestion based on specific events or holidays and suggests travel destinations. This makes it possible to predict future congestion conditions and suggest optimal travel destinations.

[0067] The congestion analysis unit can analyze real-time traffic data and propose the smoothest route for travel. For example, the generation AI analyzes real-time traffic data to propose the smoothest route for travel. For example, the route is optimized based on information about traffic congestion and public transportation delays. The congestion analysis unit also analyzes real-time traffic data based on the user's current location and destination and proposes the optimal travel route. For example, it presents an alternative route to avoid congestion. The congestion analysis unit also builds a system in which the generation AI proposes the smoothest route for travel based on real-time traffic data. For example, it dynamically adjusts the route according to changes in traffic conditions. This makes it possible to provide the smoothest route for travel.

[0068] The congestion analysis unit uses the emotion estimation function to make suggestions to the user to avoid crowds, thereby providing a less stressful travel experience. The congestion analysis unit, for example, uses the emotion estimation function to make suggestions to the user to avoid crowds. For example, if the user feels stressed, it suggests uncrowded tourist spots. The congestion analysis unit also proposes a travel plan to avoid crowds based on the user's emotion data. For example, it prioritizes suggestions for tourist spots and activities that allow the user to relax. The congestion analysis unit also uses the emotion estimation function to make suggestions to the user to avoid crowds, thereby building a system that provides a less stressful travel experience. For example, it dynamically adjusts the travel plan according to changes in the user's emotions. This makes it possible to provide a travel experience that reduces the user's stress.

[0069] The congestion analysis unit can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, the generation AI in the congestion analysis unit analyzes congestion conditions in different seasons and time periods and suggests the optimal time to visit. For example, it suggests a time that avoids peak congestion at tourist destinations. The congestion analysis unit also analyzes congestion conditions in different seasons and time periods based on the user's desired visit time and suggests the optimal time to visit. For example, it suggests weekdays or off-seasons when congestion is less. The congestion analysis unit also builds a system in which the generation AI analyzes congestion conditions in different seasons and time periods and suggests the optimal time to visit. For example, it predicts congestion based on past data and suggests the best time to visit. This makes it possible to provide the optimal time to visit.

[0070] The congestion analysis unit can discover and suggest new, uncrowded but attractive tourist destinations based on the user's interests. For example, the generation AI analyzes the user's interests and discovers and suggests new, uncrowded but attractive tourist destinations. For example, it suggests unexplored tourist destinations based on the user's interests. The congestion analysis unit also proposes new, uncrowded but attractive tourist destinations based on the user's interests. For example, it suggests tourist destinations related to themes that the user has shown interest in. The congestion analysis unit also builds a system in which the generation AI analyzes the user's interests and discovers and suggests new, uncrowded but attractive tourist destinations. For example, it searches a database of tourist destinations based on the user's interests. This makes it possible to provide new, uncrowded but attractive tourist destinations.

[0071] The congestion analysis unit can use the emotion estimation function to make suggestions to the user to avoid crowds and suggest travel destinations that will elicit positive emotions. The congestion analysis unit, for example, uses the emotion estimation function to make suggestions to the user to avoid crowds and suggest travel destinations that will elicit positive emotions. For example, it suggests tourist spots where the user can relax. The congestion analysis unit also suggests travel destinations to avoid crowds based on the user's emotion data. For example, it prioritizes suggesting tourist spots where the user has expressed positive emotions. The congestion analysis unit also uses the emotion estimation function to build a system that makes suggestions to the user to avoid crowds and suggests travel destinations that will elicit positive emotions. For example, it dynamically adjusts travel destinations according to changes in the user's emotions. This makes it possible to provide travel destinations that will elicit positive emotions from the user.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The travel plan generation unit can propose healthy travel plans taking into account the user's health condition and fitness level. For example, the generation AI collects the user's health data and proposes healthy travel plans based on that data. For example, it proposes sightseeing routes that take walking distance and calorie consumption into consideration. The travel plan generation unit also works with a fitness app to create travel plans based on the user's exercise habits and health goals. For example, it proposes plans that include activities such as hiking and cycling. The travel plan generation unit also suggests relaxing travel destinations and activities based on the user's health condition. For example, it proposes plans that include hot springs and spa resorts. This makes it possible to provide travel plans that suit the user's health condition.

[0074] The travel plan generation unit can create an optimal travel plan for a family trip or a trip with a pet, taking into account the user's family composition and whether or not they have a pet. For example, the travel plan generation unit can propose a travel plan that the whole family can enjoy, taking into account the user's family composition. For example, the travel plan generation unit can create a plan that includes activities for children and family-friendly accommodations. The travel plan generation unit can also propose pet-friendly accommodations and tourist spots to a user who wishes to travel with a pet. For example, the travel plan generation unit can create a plan that includes tourist spots that allow pets and activities specifically for pets. The travel plan generation unit can also customize the travel plan depending on the family composition and whether or not the user has a pet. For example, the travel plan generation unit can propose tourist spots and activities that can be enjoyed by multiple generations together. This makes it possible to provide an optimal plan for a family trip or a trip with a pet.

[0075] The travel plan generation unit uses the emotion estimation function to analyze the emotions of the user when entering a travel plan and can propose a travel plan that elicits positive emotions. For example, when the user enters a travel plan, the unit analyzes facial expressions and voice tone to estimate emotions. For example, if a smile or an excited voice is detected, the unit proposes a plan that reflects those emotions. The travel plan generation unit also uses the emotion estimation function to provide real-time feedback on the content entered by the user to elicit positive emotions. For example, it displays encouraging messages for activities in which the user has shown interest. The travel plan generation unit also generates a travel plan that elicits positive emotions based on the user's emotion data. For example, it prioritizes the suggestion of travel destinations and activities that have elicited positive emotions in the past. This makes it possible to provide a travel plan that elicits positive emotions from the user.

[0076] The reservation process can search multiple reservation sites across the network and automatically select the most cost-effective reservation option. For example, the generation AI can search multiple reservation sites across the network and automatically select the most cost-effective accommodation and transportation options. For example, it can use a price comparison site to find the lowest price. The reservation process also allows the generation AI to consider the user's budget and suggest the optimal reservation option. For example, it can select accommodation and transportation options that offer the best service within the budget. The generation AI can also monitor price fluctuations in real time and make reservations at the best possible time. For example, it can automatically confirm reservations the moment the price drops. This allows the system to provide the most cost-effective reservation options.

[0077] The reservation procedure unit learns the user's past reservation history and can prioritize reservations for accommodations and transportation methods that the user prefers. For example, the generation AI analyzes the user's past reservation history to identify their preferred accommodations and transportation methods. For example, it will prioritize reservations for accommodations that have been highly rated in the past. The reservation procedure unit also uses the generation AI to suggest optimal reservation options based on the user's past reservation history. For example, it will prioritize selecting airlines and hotel chains that have been used in the past. The reservation procedure unit also uses the generation AI to learn the user's preferences and suggest customized reservation options based on the user's past reservation history. For example, for a user who prefers a particular brand or service, it will suggest options for that brand. This makes it possible to provide reservation options that meet the user's preferences.

[0078] The reservation procedure unit can use the emotion estimation function to provide an interface for reducing stress when a user performs a reservation procedure. For example, the emotion estimation function can be used to analyze the stress a user experiences when performing a reservation procedure in real time, and an interface for reducing stress can be provided. For example, relaxing music can be played when the user feels stressed. The reservation procedure unit can also customize the interface for the reservation procedure based on the user's emotion data. For example, if the user feels stressed, the reservation procedure unit can simplify the procedure steps. The reservation procedure unit can also use the emotion estimation function to provide an interface for eliciting positive emotions when the user performs a reservation procedure. For example, encouraging messages or success stories can be presented. This makes it possible to provide an interface that reduces the user's stress.

[0079] The congestion analysis unit analyzes past congestion data, predicts future congestion levels, and can suggest travel destinations. For example, the generation AI analyzes past congestion data and develops an algorithm to predict future congestion levels. For example, it predicts peak congestion times at specific tourist destinations based on past data and suggests travel destinations that avoid crowds. The congestion analysis unit also uses the generation AI to predict future congestion levels based on past congestion data and suggests optimal travel destinations for users. For example, it creates travel plans that avoid periods when congestion is expected. The congestion analysis unit also uses the generation AI to analyze past congestion data and build a system that predicts future congestion levels. For example, it predicts congestion based on specific events or holidays and suggests travel destinations. This makes it possible to predict future congestion levels and suggest optimal travel destinations.

[0080] The congestion analysis unit can analyze real-time traffic data and propose the smoothest route for travel. For example, the generation AI analyzes real-time traffic data and proposes the smoothest route for travel. For example, it optimizes the route based on information on traffic congestion and public transportation delays. The congestion analysis unit also analyzes real-time traffic data based on the user's current location and destination and proposes the optimal travel route. For example, it presents an alternative route to avoid congestion. The congestion analysis unit also builds a system in which the generation AI proposes the smoothest route for travel based on real-time traffic data. For example, it dynamically adjusts the route according to changes in traffic conditions. This makes it possible to provide the smoothest route for travel.

[0081] The congestion analysis unit uses the emotion estimation function to make suggestions to the user to avoid crowds, thereby providing a less stressful travel experience. For example, the emotion estimation function is used to make suggestions to the user to avoid crowds. For example, if the user feels stressed, the congestion analysis unit may suggest less crowded tourist spots. The congestion analysis unit may also suggest a travel plan to avoid crowds based on the user's emotion data. For example, the congestion analysis unit may prioritize suggesting tourist spots and activities that allow the user to relax. The congestion analysis unit may also use the emotion estimation function to make suggestions to the user to avoid crowds, thereby building a system that provides a less stressful travel experience. For example, the travel plan may be dynamically adjusted according to changes in the user's emotions. This may provide a travel experience that reduces the user's stress.

[0082] The congestion analysis unit can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, the generation AI can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, it can suggest a time that avoids peak congestion at tourist destinations. The congestion analysis unit can also analyze congestion conditions in different seasons and time periods based on the user's desired visit time and suggest the optimal time to visit. For example, it can suggest weekdays or off-seasons when congestion is less. The congestion analysis unit can also build a system in which the generation AI can analyze congestion conditions in different seasons and time periods and suggest the optimal time to visit. For example, it can predict congestion based on past data and suggest the best time to visit. This makes it possible to provide the optimal time to visit.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The condition setting unit accepts the user's desired conditions, such as the user's travel destination, budget, schedule, and activities of interest. Step 2: The travel plan generation unit creates a travel plan based on the desired conditions accepted by the condition setting unit. For example, the generation AI analyzes the user's desired conditions and proposes the optimal travel plan. Step 3: The reservation procedure unit performs the reservation procedure based on the travel plan created by the travel plan generation unit. For example, the generation AI automatically reserves accommodations and transportation, purchases tickets to tourist attractions, etc. Step 4: The congestion analysis unit analyzes the congestion situation at tourist destinations in real time and suggests travel destinations to avoid tourist concentration. For example, if a particular tourist destination is crowded, the generation AI will suggest other tourist destinations to disperse tourists.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a condition setting unit that accepts desired conditions from a user; a travel plan creation unit that creates a travel plan based on the desired conditions received by the condition setting unit; a reservation procedure unit that performs reservation procedures based on the travel plan created by the travel plan creation unit; A congestion analysis unit that analyzes the congestion situation at tourist spots in real time and suggests travel destinations to avoid tourist concentration. A system characterized by:

2. The travel plan generation unit Learn about the user's past travel history and preferences to suggest more personalized travel plans 2. The system of claim 1.

3. The travel plan generation unit Analyzing the user's social media posts and reviews, and creating the travel plan that reflects the user's hidden interests and preferences 2. The system of claim 1.

4. The travel plan generation unit Analyzing the emotions of the user when inputting a travel plan and proposing the travel plan that elicits positive emotions 2. The system of claim 1.

5. The travel plan generation unit Taking into account the user's health condition and fitness level, the travel plan is proposed as healthy.

2. The system of claim 1.

6. The travel plan generation unit The travel plan that is best suited for a family trip or a trip accompanied by a pet is created by taking into consideration the user's family structure and whether or not they have pets.

2. The system of claim 1.

7. The travel plan generation unit Analyzing the user's emotions in real time when selecting a travel plan, and suggesting the travel plan that elicits the most positive response 2. The system of claim 1.

8. The reservation procedure unit Search across multiple booking sites and automatically select the most cost-effective booking option 2. The system of claim 1.

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