System

Abstract

An object of a system according to an embodiment is to propose an optimal itinerary on the basis of a traveler's preferences and budget.SOLUTION: A system includes an information input unit, an analysis unit, and a proposal unit. The information input unit inputs information on the traveler's preferences, budget, and area to visit. The analysis unit analyzes the information input by the information input unit. The suggestion unit suggests an optimal itinerary on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made travel planning complicated, making it difficult for travelers to find the best itinerary that suits their preferences and budget.

[0005] The system according to the embodiment aims to propose an optimal itinerary based on the traveler's preferences and budget. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an analysis unit, and a proposal unit. The information input unit inputs information about a traveler's preferences, budget, and desired areas to visit. The analysis unit analyzes the information input by the information input unit. The proposal unit proposes an optimal itinerary based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal itinerary based on the traveler's preferences and budget. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The travel assistant system according to an embodiment of the present invention is a system in which a traveler inputs information about their preferences, budget, and desired areas to visit, and the system analyzes the information using a generation AI to propose an optimal itinerary. As a result, the travel assistant system can propose an optimal itinerary based on the traveler's preferences and budget, and provide an efficient and comprehensive travel plan.

[0029] A travel assistant system according to an embodiment includes an information input unit, an analysis unit, and a suggestion unit. The information input unit inputs information about a traveler's preferences, budget, and desired regions. For example, a traveler may input that they like nature, have a budget of less than 100,000 yen, and want to visit Hokkaido. The information input unit can also save the information input by the traveler and reuse it later. The analysis unit analyzes the information input by the information input unit. For example, the generation AI selects optimal tourist spots and activities based on the traveler's preferences, budget, and desired regions. The analysis unit can also analyze the traveler's past travel history and social media posts to understand the traveler's interests. The suggestion unit proposes an optimal itinerary based on the results of the analysis by the analysis unit. For example, the generation AI proposes an itinerary that includes tourist spots and activities that allow travelers to enjoy Hokkaido's nature based on the traveler's preferences and budget. The suggestion unit can also customize the itinerary to suit the traveler's individual needs and make real-time changes. As a result, the travel assistant system according to an embodiment can propose optimal itineraries based on the traveler's preferences and budget, providing efficient and comprehensive travel plans.

[0030] The analysis unit analyzes a traveler's past travel history, learning their preferences and trends to reflect in their next travel plan. For example, the analysis unit collects data on places the traveler has visited and activities they have participated in in the past, and the generation AI analyzes that information. For example, the next travel plan can be customized based on ratings of tourist spots and accommodations visited in the past. The analysis unit also develops algorithms that learn preferences and trends based on the traveler's past travel history. For example, it can prioritize recommending tourist spots rich in nature to a traveler who loves nature. The analysis unit also analyzes a traveler's past travel history and builds a system that reflects this in the next travel plan. For example, it can suggest the next travel destination based on ratings and impressions of places visited in the past. This makes it possible to optimize the next travel plan based on the traveler's past travel history.

[0031] The analysis unit analyzes travelers' social media posts or photos to gain a more detailed understanding of their interests and concerns and reflect this in the plan. For example, the analysis unit analyzes travelers' social media posts and photos to develop an algorithm to understand travelers' interests and concerns. For example, it analyzes the content and tags of posted photos to identify travelers' preferences. The analysis unit also collects travelers' social media data, and the generation AI analyzes that data to reflect this in the travel plan. For example, it suggests the next travel destination based on the themes and locations that travelers frequently post about. The analysis unit also analyzes travelers' social media posts and photos to build a system that gains a detailed understanding of travelers' interests and concerns. For example, it customizes travel plans based on the results of analyzing posted photos. This makes it possible to understand travelers' interests and concerns in detail based on their social media posts and photos and reflect them in the plan.

[0032] The analysis unit can suggest appropriate activities and tourist spots by taking into account the traveler's health condition and fitness level. For example, the analysis unit collects the traveler's health condition and fitness level as input data, and the generation AI analyzes that information. For example, it suggests activities and tourist spots according to the health condition. The analysis unit also develops an algorithm that suggests appropriate activities and tourist spots by taking into account the traveler's fitness level. For example, it suggests hiking trails and walking tours according to physical strength. The analysis unit also builds a system that suggests appropriate activities and tourist spots based on the traveler's health condition and fitness level. For example, it suggests relaxation spots and spas according to the traveler's health condition. This makes it possible to suggest activities and tourist spots according to the traveler's health condition and fitness level.

[0033] The analysis unit can input traveler's dietary restrictions and allergy information and suggest restaurants and meal plans based on that information. For example, the analysis unit collects traveler's dietary restrictions and allergy information as input data, and the generation AI analyzes that information. For example, it suggests restaurants and meal plans that accommodate allergies. The analysis unit also develops an algorithm that suggests appropriate restaurants and meal plans based on traveler's dietary restrictions and allergy information. For example, it suggests restaurants that offer vegetarian or gluten-free options. The analysis unit also builds a system that suggests appropriate restaurants and meal plans based on traveler's dietary restrictions and allergy information. For example, it suggests safe meal plans based on allergy information. This makes it possible to suggest appropriate restaurants and meal plans based on traveler's dietary restrictions and allergy information.

[0034] The suggestion unit can suggest optimal tourist spots and events in real time according to the local weather and season. For example, the suggestion unit collects local weather data in real time, and the generation AI suggests optimal tourist spots and events based on that information. For example, indoor tourist spots are suggested on rainy days. The suggestion unit also collects seasonal tourist spot and event information, and the generation AI makes optimal suggestions based on that information. For example, cherry blossom viewing spots and cherry blossom festivals are suggested in spring. The suggestion unit also builds a system that suggests tourist spots and events in real time according to the local weather and season. For example, it dynamically adjusts sightseeing plans based on weather data. This makes it possible to suggest optimal tourist spots and events in real time according to the local weather and season.

[0035] The suggestion unit can analyze the congestion situation at tourist spots and suggest the optimal visiting time to avoid crowds. For example, the suggestion unit collects congestion data at tourist spots in real time, and the generation AI suggests the optimal visiting time based on that information. For example, it suggests a time period when the congestion is less. The suggestion unit also analyzes the congestion situation and develops an algorithm to suggest the optimal visiting time to allow travelers to enjoy sightseeing comfortably. For example, it makes congestion predictions based on past data. The suggestion unit also analyzes the congestion situation at tourist spots and builds a system to suggest the optimal visiting time to avoid crowds. For example, it dynamically adjusts the visiting time based on the congestion prediction data. This makes it possible to suggest the optimal visiting time to avoid crowds at tourist spots.

[0036] The suggestion unit can provide information about local culture and history and suggest guided tours to deepen travelers' understanding. For example, the suggestion unit collects information about local culture and history, and the generation AI suggests guided tours based on that information. For example, it suggests tours that include historical landmarks and cultural events. The suggestion unit also develops an algorithm that provides information about local culture and history according to travelers' interests. For example, it suggests guided tours based on specific themes. The suggestion unit also builds a system that provides information about local culture and history and suggests guided tours to deepen travelers' understanding. For example, it suggests tours that explain the cultural background. This makes it possible to provide information about local culture and history and suggest guided tours to deepen travelers' understanding.

[0037] The suggestion unit can suggest local activities and workshops, providing travelers with opportunities to learn through experience. For example, the suggestion unit collects information on local activities and workshops, and the generation AI makes suggestions based on that information. For example, it suggests local cooking classes and traditional craft workshops. The suggestion unit also develops an algorithm that suggests local activities and workshops based on travelers' interests. For example, it suggests experiential sightseeing plans. The suggestion unit also builds a system that suggests local activities and workshops, providing travelers with opportunities to learn through experience. For example, it suggests workshops where they can learn about local culture and techniques. This makes it possible to suggest local activities and workshops, providing travelers with opportunities to learn through experience.

[0038] The suggestion unit can suggest the optimal means of transportation and route to minimize travel time for travelers. For example, the suggestion unit collects the traveler's departure and destination as input data, and the generation AI suggests the optimal means of transportation and route based on that information. For example, it suggests the route that will take the shortest time to travel. The suggestion unit also develops an algorithm that suggests the optimal means of transportation and route to minimize travel time. For example, it suggests the optimal route taking traffic conditions and operation schedules into consideration. The suggestion unit also builds a system that suggests the optimal means of transportation and route to minimize travel time for travelers. For example, it dynamically adjusts the route based on real-time traffic information. This makes it possible to suggest the optimal means of transportation and route to minimize travel time for travelers.

[0039] The suggestion unit can suggest the optimal means of transportation based on the traveler's budget, maximizing cost performance. For example, the suggestion unit collects the traveler's budget information as input data, and the generation AI suggests the optimal means of transportation based on that information. For example, it suggests the most cost-effective means of transportation within the budget. The suggestion unit also develops an algorithm that suggests the optimal means of transportation based on the traveler's budget. For example, it compares multiple means of transportation and suggests the most economical option. The suggestion unit also builds a system that suggests the optimal means of transportation based on the traveler's budget, maximizing cost performance. For example, it suggests the most efficient way to travel within the budget. This makes it possible to suggest the optimal means of transportation based on the traveler's budget, maximizing cost performance.

[0040] The suggestion unit can take into account the traveler's consideration for the environment and suggest eco-friendly means of transportation. For example, the suggestion unit collects the traveler's consideration for the environment as input data, and the generation AI suggests eco-friendly means of transportation based on that information. For example, it suggests public transportation such as trains and buses. The suggestion unit also develops an algorithm that takes into account the traveler's consideration for the environment and suggests eco-friendly means of transportation. For example, it suggests means of transportation that minimize CO2 emissions. The suggestion unit also builds a system that takes into account the traveler's consideration for the environment and suggests eco-friendly means of transportation. For example, it prioritizes suggesting eco-friendly modes of travel. This makes it possible to suggest eco-friendly means of transportation based on the traveler's consideration for the environment.

[0041] The suggestion unit can suggest lounges and rest spots to improve the comfort of the traveler during travel. The suggestion unit, for example, develops an algorithm to suggest lounges and rest spots to improve the comfort of the traveler during travel. For example, it suggests lounges and rest spots that can be used during travel. The suggestion unit also builds a system to suggest lounges and rest spots to improve the comfort of the traveler during travel. For example, it suggests rest spots along the travel route. The suggestion unit also suggests lounges and rest spots in real time to improve the comfort of the traveler during travel. For example, it provides information on lounges and rest spots that can be used during travel. This makes it possible to suggest lounges and rest spots to improve the comfort of the traveler during travel.

[0042] The suggestion unit can analyze a traveler's past accommodation history, learn their preferences and trends, and reflect this in their next accommodation selection. For example, the suggestion unit collects a traveler's past accommodation history, and the generation AI analyzes that information to reflect this in their next accommodation selection. For example, suggestions are made based on the characteristics of accommodations that have been highly rated in the past. The suggestion unit also develops an algorithm that learns preferences and trends based on a traveler's past accommodation history. For example, it suggests similar accommodations to a traveler who prefers a particular hotel chain or type of accommodation. The suggestion unit also analyzes a traveler's past accommodation history and builds a system that reflects this in their next accommodation selection. For example, it suggests the most suitable accommodation based on the traveler's past accommodation history. This makes it possible to optimize the traveler's next accommodation selection based on the traveler's past accommodation history.

[0043] The suggestion unit can analyze accommodation reviews and ratings and suggest the most suitable accommodation to travelers. For example, the suggestion unit collects accommodation review and rating data, and the generation AI analyzes that information to suggest the most suitable accommodation to travelers. For example, it prioritizes suggestions of highly rated accommodation. The suggestion unit also analyzes accommodation reviews and ratings and develops an algorithm that suggests the most suitable accommodation to travelers. For example, it analyzes the content of reviews to suggest accommodation that meets the needs of travelers. The suggestion unit also analyzes accommodation reviews and ratings and builds a system that suggests the most suitable accommodation to travelers. For example, it suggests accommodation based on the sentiment score of the reviews. This makes it possible to suggest the most suitable accommodation to travelers based on the accommodation reviews and ratings.

[0044] The suggestion unit can suggest accommodations that meet the special requests of travelers. For example, the suggestion unit collects the special requests of travelers as input data, and the generation AI suggests accommodations that meet the requests based on that information. For example, it suggests accommodations that allow pets or are barrier-free. The suggestion unit also develops an algorithm that suggests accommodations that meet the special requests of travelers. For example, it suggests accommodations that offer special facilities or services. The suggestion unit also builds a system that suggests accommodations that meet the special requests of travelers. For example, it builds a database of accommodations that meet special requests and suggests the most suitable facility. This makes it possible to suggest accommodations that meet the special requests of travelers.

[0045] The suggestion unit can provide information about the area around the accommodation facility, thereby enriching the traveler's stay. For example, the suggestion unit collects information about the area around the accommodation facility, and the generation AI provides this information to the traveler. For example, it suggests restaurants and tourist spots around the accommodation facility. The suggestion unit also develops an algorithm that provides information about the area around the accommodation facility and enriches the traveler's stay. For example, it provides information about activities and events around the accommodation facility. The suggestion unit also builds a system that provides information about the area around the accommodation facility and enriches the traveler's stay. For example, it provides maps and guide information about the area around the accommodation facility. This allows the traveler to enjoy information about the area around the accommodation facility and enrich their stay.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can suggest appropriate activities and tourist spots by taking into account the traveler's health condition and fitness level. For example, the traveler's health condition and fitness level are collected as input data, and the generation AI analyzes that information. For example, activities and tourist spots are suggested according to the traveler's health condition. The analysis unit also develops an algorithm that suggests appropriate activities and tourist spots by taking into account the traveler's fitness level. For example, it suggests hiking trails and walking tours according to physical strength. The analysis unit also builds a system that suggests appropriate activities and tourist spots based on the traveler's health condition and fitness level. For example, it suggests relaxation spots and spas according to the traveler's health condition. This makes it possible to suggest activities and tourist spots according to the traveler's health condition and fitness level.

[0048] The suggestion unit can suggest optimal tourist spots and events in real time according to the local weather and season. For example, local weather data is collected in real time, and the generation AI suggests optimal tourist spots and events based on that information. For example, indoor tourist spots are suggested on rainy days. The suggestion unit also collects seasonal tourist spot and event information, and the generation AI makes optimal suggestions based on that information. For example, cherry blossom viewing spots and cherry blossom festivals are suggested in spring. The suggestion unit also builds a system that suggests tourist spots and events in real time according to the local weather and season. For example, it dynamically adjusts sightseeing plans based on weather data. This makes it possible to suggest optimal tourist spots and events in real time according to the local weather and season.

[0049] The suggestion unit can analyze the congestion situation at tourist spots and suggest the optimal visit time to avoid crowds. For example, it can collect congestion data at tourist spots in real time, and the generation AI can suggest the optimal visit time based on that information. For example, it can suggest a time period when the congestion is least. The suggestion unit also develops an algorithm that analyzes congestion and suggests the optimal visit time to allow travelers to enjoy sightseeing comfortably. For example, it can predict congestion based on past data. The suggestion unit also builds a system that analyzes congestion at tourist spots and suggests the optimal visit time to avoid crowds. For example, it can dynamically adjust the visit time based on the congestion prediction data. This makes it possible to suggest the optimal visit time to avoid crowded tourist spots.

[0050] The suggestion unit can provide information about local culture and history and suggest guided tours to deepen travelers' understanding. For example, information about local culture and history is collected, and the generation AI suggests guided tours based on that information. For example, it suggests tours that include historical landmarks and cultural events. The suggestion unit also develops an algorithm that provides information about local culture and history according to travelers' interests. For example, it suggests guided tours based on specific themes. The suggestion unit also builds a system that provides information about local culture and history and suggests guided tours to deepen travelers' understanding. For example, it suggests tours that explain the cultural background. This makes it possible to provide information about local culture and history and suggest guided tours to deepen travelers' understanding.

[0051] The suggestion unit can suggest the optimal means of transportation and route to minimize travel time for travelers. For example, the traveler's departure and destination are collected as input data, and the generation AI suggests the optimal means of transportation and route based on that information. For example, it suggests the route that will take the shortest time to travel. The suggestion unit also develops an algorithm to suggest the optimal means of transportation and route to minimize travel time. For example, it suggests the optimal route taking into account traffic conditions and operation schedules. The suggestion unit also builds a system to suggest the optimal means of transportation and route to minimize travel time for travelers. For example, it dynamically adjusts the route based on real-time traffic information. This makes it possible to suggest the optimal means of transportation and route to minimize travel time for travelers.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The information input unit inputs information about the traveler's preferences, budget, and desired area. For example, a traveler can input that they like nature, their budget is within 100,000 yen, and that the desired area is Hokkaido. The information input unit can also save the information the traveler inputs and reuse it later. Step 2: The analysis unit analyzes the information entered by the information input unit. For example, the generation AI selects the most suitable tourist spots and activities based on the traveler's preferences, budget, and information about the areas they want to visit. The analysis unit can also analyze the traveler's past travel history and social media posts to understand their interests. Step 3: The proposal unit proposes an optimal itinerary based on the results of the analysis by the analysis unit. For example, the generation AI proposes an itinerary that includes tourist spots and activities that allow travelers to enjoy Hokkaido's natural beauty, based on the traveler's preferences and budget. The proposal unit can also customize the itinerary to suit the individual needs of travelers and make real-time changes.

[0054] (Example 2) The travel assistant system according to an embodiment of the present invention is a system in which a traveler inputs information about their preferences, budget, and desired areas to visit, and the system analyzes the information using a generation AI to propose an optimal itinerary. As a result, the travel assistant system can propose an optimal itinerary based on the traveler's preferences and budget, and provide an efficient and comprehensive travel plan.

[0055] A travel assistant system according to an embodiment includes an information input unit, an analysis unit, and a suggestion unit. The information input unit inputs information about a traveler's preferences, budget, and desired regions. For example, a traveler may input that they like nature, have a budget of less than 100,000 yen, and want to visit Hokkaido. The information input unit can also save the information input by the traveler and reuse it later. The analysis unit analyzes the information input by the information input unit. For example, the generation AI selects optimal tourist spots and activities based on the traveler's preferences, budget, and desired regions. The analysis unit can also analyze the traveler's past travel history and social media posts to understand the traveler's interests. The suggestion unit proposes an optimal itinerary based on the results of the analysis by the analysis unit. For example, the generation AI proposes an itinerary that includes tourist spots and activities that allow travelers to enjoy Hokkaido's nature based on the traveler's preferences and budget. The suggestion unit can also customize the itinerary to suit the traveler's individual needs and make real-time changes. As a result, the travel assistant system according to an embodiment can propose optimal itineraries based on the traveler's preferences and budget, providing efficient and comprehensive travel plans.

[0056] The analysis unit analyzes a traveler's past travel history, learning their preferences and trends to reflect in their next travel plan. For example, the analysis unit collects data on places the traveler has visited and activities they have participated in in the past, and the generation AI analyzes that information. For example, the next travel plan can be customized based on ratings of tourist spots and accommodations visited in the past. The analysis unit also develops algorithms that learn preferences and trends based on the traveler's past travel history. For example, it can prioritize recommending tourist spots rich in nature to a traveler who loves nature. The analysis unit also analyzes a traveler's past travel history and builds a system that reflects this in the next travel plan. For example, it can suggest the next travel destination based on ratings and impressions of places visited in the past. This makes it possible to optimize the next travel plan based on the traveler's past travel history.

[0057] The analysis unit analyzes travelers' social media posts or photos to gain a more detailed understanding of their interests and concerns and reflect this in the plan. For example, the analysis unit analyzes travelers' social media posts and photos to develop an algorithm to understand travelers' interests and concerns. For example, it analyzes the content and tags of posted photos to identify travelers' preferences. The analysis unit also collects travelers' social media data, and the generation AI analyzes that data to reflect this in the travel plan. For example, it suggests the next travel destination based on the themes and locations that travelers frequently post about. The analysis unit also analyzes travelers' social media posts and photos to build a system that gains a detailed understanding of travelers' interests and concerns. For example, it customizes travel plans based on the results of analyzing posted photos. This makes it possible to understand travelers' interests and concerns in detail based on their social media posts and photos and reflect them in the plan.

[0058] The analysis unit uses the emotion estimation function to analyze the emotions felt by travelers when they enter information and can propose plans that elicit positive emotions. For example, the analysis unit uses the emotion estimation function to analyze emotions in real time when travelers enter information. For example, it analyzes facial expressions and voices when they enter information and proposes plans that elicit positive emotions. The analysis unit also uses the emotion estimation function to analyze the emotions felt by travelers when they enter information and customizes plans based on the results. For example, it proposes tourist spots and activities that elicit positive emotions. The analysis unit also builds a system that analyzes the emotions felt by travelers when they enter information and proposes plans that elicit positive emotions. For example, it prioritizes the proposal of plans with a high emotion score. This makes it possible to analyze travelers' emotions and propose plans that elicit positive emotions.

[0059] The analysis unit can suggest appropriate activities and tourist spots by taking into account the traveler's health condition and fitness level. For example, the analysis unit collects the traveler's health condition and fitness level as input data, and the generation AI analyzes that information. For example, it suggests activities and tourist spots according to the health condition. The analysis unit also develops an algorithm that suggests appropriate activities and tourist spots by taking into account the traveler's fitness level. For example, it suggests hiking trails and walking tours according to physical strength. The analysis unit also builds a system that suggests appropriate activities and tourist spots based on the traveler's health condition and fitness level. For example, it suggests relaxation spots and spas according to the traveler's health condition. This makes it possible to suggest activities and tourist spots according to the traveler's health condition and fitness level.

[0060] The analysis unit can input traveler's dietary restrictions and allergy information and suggest restaurants and meal plans based on that information. For example, the analysis unit collects traveler's dietary restrictions and allergy information as input data, and the generation AI analyzes that information. For example, it suggests restaurants and meal plans that accommodate allergies. The analysis unit also develops an algorithm that suggests appropriate restaurants and meal plans based on traveler's dietary restrictions and allergy information. For example, it suggests restaurants that offer vegetarian or gluten-free options. The analysis unit also builds a system that suggests appropriate restaurants and meal plans based on traveler's dietary restrictions and allergy information. For example, it suggests safe meal plans based on allergy information. This makes it possible to suggest appropriate restaurants and meal plans based on traveler's dietary restrictions and allergy information.

[0061] The analysis unit can use the emotion estimation function to suggest a relaxation plan to reduce the stress and anxiety felt by travelers when entering information. For example, the analysis unit uses the emotion estimation function to analyze stress and anxiety in real time when travelers enter information. For example, it analyzes facial expressions and voices when entering information and suggests a relaxation plan. The analysis unit also uses the emotion estimation function to analyze the stress and anxiety felt by travelers when entering information and customizes a relaxation plan based on the results. For example, it suggests tourist spots and activities that will help travelers relax. The analysis unit also builds a system that analyzes the stress and anxiety felt by travelers when entering information and suggests a relaxation plan. For example, it preferentially suggests a relaxation plan if the emotion score is low. This makes it possible to suggest a relaxation plan to reduce travelers' stress and anxiety.

[0062] The suggestion unit can suggest optimal tourist spots and events in real time according to the local weather and season. For example, the suggestion unit collects local weather data in real time, and the generation AI suggests optimal tourist spots and events based on that information. For example, indoor tourist spots are suggested on rainy days. The suggestion unit also collects seasonal tourist spot and event information, and the generation AI makes optimal suggestions based on that information. For example, cherry blossom viewing spots and cherry blossom festivals are suggested in spring. The suggestion unit also builds a system that suggests tourist spots and events in real time according to the local weather and season. For example, it dynamically adjusts sightseeing plans based on weather data. This makes it possible to suggest optimal tourist spots and events in real time according to the local weather and season.

[0063] The suggestion unit can analyze the congestion situation at tourist spots and suggest the optimal visiting time to avoid crowds. For example, the suggestion unit collects congestion data at tourist spots in real time, and the generation AI suggests the optimal visiting time based on that information. For example, it suggests a time period when the congestion is less. The suggestion unit also analyzes the congestion situation and develops an algorithm to suggest the optimal visiting time to allow travelers to enjoy sightseeing comfortably. For example, it makes congestion predictions based on past data. The suggestion unit also analyzes the congestion situation at tourist spots and builds a system to suggest the optimal visiting time to avoid crowds. For example, it dynamically adjusts the visiting time based on the congestion prediction data. This makes it possible to suggest the optimal visiting time to avoid crowds at tourist spots.

[0064] The suggestion unit can use the emotion estimation function to predict emotions felt by travelers at spots they visit and suggest spots that will provide a positive experience. For example, the suggestion unit uses the emotion estimation function to develop an algorithm that predicts emotions felt by travelers at spots they visit. For example, it calculates an emotion score based on past data. The suggestion unit also builds a system that predicts emotions felt by travelers at spots they visit and suggests spots that will provide a positive experience. For example, it prioritizes suggesting spots with high emotion scores. The suggestion unit also uses the emotion estimation function to predict emotions felt by travelers at spots they visit in real time and suggests spots that will provide a positive experience. For example, it customizes a sightseeing plan based on the emotion score. This makes it possible to predict emotions felt by travelers at spots they visit and suggest spots that will provide a positive experience.

[0065] The suggestion unit can provide information about local culture and history and suggest guided tours to deepen travelers' understanding. For example, the suggestion unit collects information about local culture and history, and the generation AI suggests guided tours based on that information. For example, it suggests tours that include historical landmarks and cultural events. The suggestion unit also develops an algorithm that provides information about local culture and history according to travelers' interests. For example, it suggests guided tours based on specific themes. The suggestion unit also builds a system that provides information about local culture and history and suggests guided tours to deepen travelers' understanding. For example, it suggests tours that explain the cultural background. This makes it possible to provide information about local culture and history and suggest guided tours to deepen travelers' understanding.

[0066] The suggestion unit can suggest local activities and workshops, providing travelers with opportunities to learn through experience. For example, the suggestion unit collects information on local activities and workshops, and the generation AI makes suggestions based on that information. For example, it suggests local cooking classes and traditional craft workshops. The suggestion unit also develops an algorithm that suggests local activities and workshops based on travelers' interests. For example, it suggests experiential sightseeing plans. The suggestion unit also builds a system that suggests local activities and workshops, providing travelers with opportunities to learn through experience. For example, it suggests workshops where they can learn about local culture and techniques. This makes it possible to suggest local activities and workshops, providing travelers with opportunities to learn through experience.

[0067] The suggestion unit can use the emotion estimation function to suggest hidden tourist spots and local events that travelers might be interested in. For example, the suggestion unit uses the emotion estimation function to develop an algorithm that suggests hidden tourist spots and local events that travelers might be interested in. For example, the suggestion unit makes suggestions based on the emotion score. The suggestion unit also builds a system that suggests hidden tourist spots and local events according to the interests and concerns of travelers. For example, the suggestion unit suggests spots that only locals know about. The suggestion unit also uses the emotion estimation function to suggest hidden tourist spots and local events that travelers might be interested in in real time. For example, the suggestion unit customizes a sightseeing plan based on the emotion score. This makes it possible to suggest hidden tourist spots and local events that travelers might be interested in.

[0068] The suggestion unit can suggest the optimal means of transportation and route to minimize travel time for travelers. For example, the suggestion unit collects the traveler's departure and destination as input data, and the generation AI suggests the optimal means of transportation and route based on that information. For example, it suggests the route that will take the shortest time to travel. The suggestion unit also develops an algorithm that suggests the optimal means of transportation and route to minimize travel time. For example, it suggests the optimal route taking traffic conditions and operation schedules into consideration. The suggestion unit also builds a system that suggests the optimal means of transportation and route to minimize travel time for travelers. For example, it dynamically adjusts the route based on real-time traffic information. This makes it possible to suggest the optimal means of transportation and route to minimize travel time for travelers.

[0069] The suggestion unit can suggest the optimal means of transportation based on the traveler's budget, maximizing cost performance. For example, the suggestion unit collects the traveler's budget information as input data, and the generation AI suggests the optimal means of transportation based on that information. For example, it suggests the most cost-effective means of transportation within the budget. The suggestion unit also develops an algorithm that suggests the optimal means of transportation based on the traveler's budget. For example, it compares multiple means of transportation and suggests the most economical option. The suggestion unit also builds a system that suggests the optimal means of transportation based on the traveler's budget, maximizing cost performance. For example, it suggests the most efficient way to travel within the budget. This makes it possible to suggest the optimal means of transportation based on the traveler's budget, maximizing cost performance.

[0070] The suggestion unit can use the emotion estimation function to suggest relaxation methods and entertainment to reduce the stress felt by the traveler while traveling. For example, the suggestion unit uses the emotion estimation function to analyze the stress felt by the traveler while traveling and suggests relaxation methods and entertainment based on the results. For example, it suggests relaxing music or videos. The suggestion unit also develops an algorithm to suggest relaxation methods and entertainment to reduce the stress felt by the traveler while traveling. For example, it suggests the optimal relaxation method based on the emotion score. The suggestion unit also uses the emotion estimation function to suggest relaxation methods and entertainment in real time to reduce the stress felt by the traveler while traveling. For example, it customizes the entertainment based on the emotion score. This makes it possible to suggest relaxation methods and entertainment to reduce the stress felt by the traveler while traveling.

[0071] The suggestion unit can take into account the traveler's consideration for the environment and suggest eco-friendly means of transportation. For example, the suggestion unit collects the traveler's consideration for the environment as input data, and the generation AI suggests eco-friendly means of transportation based on that information. For example, it suggests public transportation such as trains and buses. The suggestion unit also develops an algorithm that takes into account the traveler's consideration for the environment and suggests eco-friendly means of transportation. For example, it suggests means of transportation that minimize CO2 emissions. The suggestion unit also builds a system that takes into account the traveler's consideration for the environment and suggests eco-friendly means of transportation. For example, it prioritizes suggesting eco-friendly modes of travel. This makes it possible to suggest eco-friendly means of transportation based on the traveler's consideration for the environment.

[0072] The suggestion unit can suggest lounges and rest spots to improve the comfort of the traveler during travel. The suggestion unit, for example, develops an algorithm to suggest lounges and rest spots to improve the comfort of the traveler during travel. For example, it suggests lounges and rest spots that can be used during travel. The suggestion unit also builds a system to suggest lounges and rest spots to improve the comfort of the traveler during travel. For example, it suggests rest spots along the travel route. The suggestion unit also suggests lounges and rest spots in real time to improve the comfort of the traveler during travel. For example, it provides information on lounges and rest spots that can be used during travel. This makes it possible to suggest lounges and rest spots to improve the comfort of the traveler during travel.

[0073] The suggestion unit can use the emotion estimation function to provide support information to reduce anxiety felt by travelers while traveling. For example, the suggestion unit uses the emotion estimation function to analyze the anxiety felt by travelers while traveling and provides support information based on the results. For example, it provides information and advice to reduce anxiety. The suggestion unit also develops an algorithm to provide support information to reduce anxiety felt by travelers while traveling. For example, it provides optimal support information based on an emotion score. The suggestion unit also uses the emotion estimation function to provide support information in real time to reduce anxiety felt by travelers while traveling. For example, it customizes the support information based on the emotion score. This makes it possible to provide support information to reduce anxiety felt by travelers while traveling.

[0074] The suggestion unit can analyze a traveler's past accommodation history, learn their preferences and trends, and reflect this in their next accommodation selection. For example, the suggestion unit collects a traveler's past accommodation history, and the generation AI analyzes that information to reflect this in their next accommodation selection. For example, suggestions are made based on the characteristics of accommodations that have been highly rated in the past. The suggestion unit also develops an algorithm that learns preferences and trends based on a traveler's past accommodation history. For example, it suggests similar accommodations to a traveler who prefers a particular hotel chain or type of accommodation. The suggestion unit also analyzes a traveler's past accommodation history and builds a system that reflects this in their next accommodation selection. For example, it suggests the most suitable accommodation based on the traveler's past accommodation history. This makes it possible to optimize the traveler's next accommodation selection based on the traveler's past accommodation history.

[0075] The suggestion unit can analyze accommodation reviews and ratings and suggest the most suitable accommodation to travelers. For example, the suggestion unit collects accommodation review and rating data, and the generation AI analyzes that information to suggest the most suitable accommodation to travelers. For example, it prioritizes suggestions of highly rated accommodation. The suggestion unit also analyzes accommodation reviews and ratings and develops an algorithm that suggests the most suitable accommodation to travelers. For example, it analyzes the content of reviews to suggest accommodation that meets the needs of travelers. The suggestion unit also analyzes accommodation reviews and ratings and builds a system that suggests the most suitable accommodation to travelers. For example, it suggests accommodation based on the sentiment score of the reviews. This makes it possible to suggest the most suitable accommodation to travelers based on the accommodation reviews and ratings.

[0076] The suggestion unit can use the emotion estimation function to predict the emotions a traveler will feel at an accommodation and suggest accommodations that will provide a positive experience. The suggestion unit, for example, uses the emotion estimation function to develop an algorithm that predicts the emotions a traveler will feel at an accommodation. For example, it calculates an emotion score based on past data. The suggestion unit also builds a system that predicts the emotions a traveler will feel at an accommodation and suggests accommodations that will provide a positive experience. For example, it prioritizes suggesting accommodations with high emotion scores. The suggestion unit also uses the emotion estimation function to predict the emotions a traveler will feel at an accommodation in real time and suggests accommodations that will provide a positive experience. For example, it customizes an accommodation plan based on the emotion score. This makes it possible to predict the emotions a traveler will feel at an accommodation and suggest accommodations that will provide a positive experience.

[0077] The suggestion unit can suggest accommodations that meet the special requests of travelers. For example, the suggestion unit collects the special requests of travelers as input data, and the generation AI suggests accommodations that meet the requests based on that information. For example, it suggests accommodations that allow pets or are barrier-free. The suggestion unit also develops an algorithm that suggests accommodations that meet the special requests of travelers. For example, it suggests accommodations that offer special facilities or services. The suggestion unit also builds a system that suggests accommodations that meet the special requests of travelers. For example, it builds a database of accommodations that meet special requests and suggests the most suitable facility. This makes it possible to suggest accommodations that meet the special requests of travelers.

[0078] The suggestion unit can provide information about the area around the accommodation facility, thereby enriching the traveler's stay. For example, the suggestion unit collects information about the area around the accommodation facility, and the generation AI provides this information to the traveler. For example, it suggests restaurants and tourist spots around the accommodation facility. The suggestion unit also develops an algorithm that provides information about the area around the accommodation facility and enriches the traveler's stay. For example, it provides information about activities and events around the accommodation facility. The suggestion unit also builds a system that provides information about the area around the accommodation facility and enriches the traveler's stay. For example, it provides maps and guide information about the area around the accommodation facility. This allows the traveler to enjoy information about the area around the accommodation facility and enrich their stay.

[0079] The suggestion unit can use the emotion estimation function to suggest amenities and services that maximize the relaxation and comfort that travelers feel at accommodations. The suggestion unit, for example, uses the emotion estimation function to develop an algorithm that predicts the relaxation and comfort that travelers will feel at accommodations. For example, it calculates an emotion score based on past data. The suggestion unit also builds a system that suggests amenities and services that maximize the relaxation and comfort that travelers will feel at accommodations. For example, it prioritizes suggesting amenities and services with a high emotion score. The suggestion unit also uses the emotion estimation function to predict the relaxation and comfort that travelers will feel at accommodations in real time and suggests optimal amenities and services. For example, it customizes an accommodation plan based on the emotion score. This makes it possible to suggest amenities and services that maximize the relaxation and comfort that travelers will feel at accommodations.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can suggest appropriate activities and tourist spots by taking into account the traveler's health condition and fitness level. For example, the traveler's health condition and fitness level are collected as input data, and the generation AI analyzes that information. For example, activities and tourist spots are suggested according to the traveler's health condition. The analysis unit also develops an algorithm that suggests appropriate activities and tourist spots by taking into account the traveler's fitness level. For example, it suggests hiking trails and walking tours according to physical strength. The analysis unit also builds a system that suggests appropriate activities and tourist spots based on the traveler's health condition and fitness level. For example, it suggests relaxation spots and spas according to the traveler's health condition. This makes it possible to suggest activities and tourist spots according to the traveler's health condition and fitness level.

[0082] The suggestion unit can suggest optimal tourist spots and events in real time according to the local weather and season. For example, local weather data is collected in real time, and the generation AI suggests optimal tourist spots and events based on that information. For example, indoor tourist spots are suggested on rainy days. The suggestion unit also collects seasonal tourist spot and event information, and the generation AI makes optimal suggestions based on that information. For example, cherry blossom viewing spots and cherry blossom festivals are suggested in spring. The suggestion unit also builds a system that suggests tourist spots and events in real time according to the local weather and season. For example, it dynamically adjusts sightseeing plans based on weather data. This makes it possible to suggest optimal tourist spots and events in real time according to the local weather and season.

[0083] The suggestion unit can analyze the congestion situation at tourist spots and suggest the optimal visit time to avoid crowds. For example, it can collect congestion data at tourist spots in real time, and the generation AI can suggest the optimal visit time based on that information. For example, it can suggest a time period when the congestion is least. The suggestion unit also develops an algorithm that analyzes congestion and suggests the optimal visit time to allow travelers to enjoy sightseeing comfortably. For example, it can predict congestion based on past data. The suggestion unit also builds a system that analyzes congestion at tourist spots and suggests the optimal visit time to avoid crowds. For example, it can dynamically adjust the visit time based on the congestion prediction data. This makes it possible to suggest the optimal visit time to avoid crowded tourist spots.

[0084] The suggestion unit can provide information about local culture and history and suggest guided tours to deepen travelers' understanding. For example, information about local culture and history is collected, and the generation AI suggests guided tours based on that information. For example, it suggests tours that include historical landmarks and cultural events. The suggestion unit also develops an algorithm that provides information about local culture and history according to travelers' interests. For example, it suggests guided tours based on specific themes. The suggestion unit also builds a system that provides information about local culture and history and suggests guided tours to deepen travelers' understanding. For example, it suggests tours that explain the cultural background. This makes it possible to provide information about local culture and history and suggest guided tours to deepen travelers' understanding.

[0085] The suggestion unit can suggest the optimal means of transportation and route to minimize travel time for travelers. For example, the traveler's departure and destination are collected as input data, and the generation AI suggests the optimal means of transportation and route based on that information. For example, it suggests the route that will take the shortest time to travel. The suggestion unit also develops an algorithm to suggest the optimal means of transportation and route to minimize travel time. For example, it suggests the optimal route taking into account traffic conditions and operation schedules. The suggestion unit also builds a system to suggest the optimal means of transportation and route to minimize travel time for travelers. For example, it dynamically adjusts the route based on real-time traffic information. This makes it possible to suggest the optimal means of transportation and route to minimize travel time for travelers.

[0086] The analysis unit uses the emotion estimation function to analyze the emotions felt by travelers when they enter information and can propose plans that elicit positive emotions. For example, when travelers enter information, the emotion estimation function is used to analyze their emotions in real time. For example, the emotion estimation function is used to analyze their facial expressions and voice when they enter information and propose plans that elicit positive emotions. The analysis unit also uses the emotion estimation function to analyze the emotions felt by travelers when they enter information and customizes plans based on the results. For example, it proposes tourist spots and activities that elicit positive emotions. The analysis unit also builds a system that analyzes the emotions felt by travelers when they enter information and proposes plans that elicit positive emotions. For example, it prioritizes the proposal of plans with a high emotion score. This makes it possible to analyze travelers' emotions and propose plans that elicit positive emotions.

[0087] The suggestion unit can use the emotion estimation function to predict emotions felt by travelers at spots they visit and suggest spots that will provide a positive experience. For example, the emotion estimation function is used to develop an algorithm that predicts emotions felt by travelers at spots they visit. For example, an emotion score is calculated based on past data. The suggestion unit also builds a system that predicts emotions felt by travelers at spots they visit and suggests spots that will provide a positive experience. For example, it prioritizes suggesting spots with high emotion scores. The suggestion unit also uses the emotion estimation function to predict emotions felt by travelers at spots they visit in real time and suggests spots that will provide a positive experience. For example, it customizes a sightseeing plan based on the emotion score. This makes it possible to predict emotions felt by travelers at spots they visit and suggest spots that will provide a positive experience.

[0088] The suggestion unit can use the emotion estimation function to suggest relaxation methods and entertainment to reduce the stress felt by travelers while traveling. For example, the emotion estimation function is used to analyze the stress felt by travelers while traveling, and based on the results, suggests relaxation methods and entertainment. For example, relaxing music and videos are suggested. The suggestion unit also develops an algorithm to suggest relaxation methods and entertainment to reduce the stress felt by travelers while traveling. For example, the suggestion unit suggests optimal relaxation methods based on emotion scores. The suggestion unit also uses the emotion estimation function to suggest relaxation methods and entertainment in real time to reduce the stress felt by travelers while traveling. For example, entertainment is customized based on emotion scores. This makes it possible to suggest relaxation methods and entertainment to reduce the stress felt by travelers while traveling.

[0089] The suggestion unit can use the emotion estimation function to provide support information to reduce anxiety felt by travelers while traveling. For example, the emotion estimation function is used to analyze the anxiety felt by travelers while traveling, and support information is provided based on the results. For example, information and advice to reduce anxiety is provided. The suggestion unit also develops an algorithm to provide support information to reduce anxiety felt by travelers while traveling. For example, optimal support information is provided based on the emotion score. The suggestion unit also uses the emotion estimation function to provide support information in real time to reduce anxiety felt by travelers while traveling. For example, support information is customized based on the emotion score. This makes it possible to provide support information to reduce anxiety felt by travelers while traveling.

[0090] The suggestion unit can use the emotion estimation function to predict the emotions a traveler will feel at an accommodation and suggest accommodations that will provide a positive experience. For example, the emotion estimation function can be used to develop an algorithm that predicts the emotions a traveler will feel at an accommodation. For example, an emotion score can be calculated based on past data. The suggestion unit can also build a system that predicts the emotions a traveler will feel at an accommodation and suggest accommodations that will provide a positive experience. For example, it can preferentially suggest accommodations with high emotion scores. The suggestion unit can also use the emotion estimation function to predict the emotions a traveler will feel at an accommodation in real time and suggest accommodations that will provide a positive experience. For example, it can customize an accommodation plan based on the emotion score. This makes it possible to predict the emotions a traveler will feel at an accommodation and suggest accommodations that will provide a positive experience.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The information input unit inputs information about the traveler's preferences, budget, and desired area. For example, a traveler can input that they like nature, their budget is within 100,000 yen, and that the desired area is Hokkaido. The information input unit can also save the information the traveler inputs and reuse it later. Step 2: The analysis unit analyzes the information entered by the information input unit. For example, the generation AI selects the most suitable tourist spots and activities based on the traveler's preferences, budget, and information about the areas they want to visit. The analysis unit can also analyze the traveler's past travel history and social media posts to understand their interests. Step 3: The proposal unit proposes an optimal itinerary based on the results of the analysis by the analysis unit. For example, the generation AI proposes an itinerary that includes tourist spots and activities that allow travelers to enjoy Hokkaido's natural beauty, based on the traveler's preferences and budget. The proposal unit can also customize the itinerary to suit the individual needs of travelers and make real-time changes.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. An information input section where travelers can input their preferences, budget, and information about the areas they want to visit; an analysis unit that analyzes the information input by the information input unit; a proposal unit that proposes an optimal itinerary based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Analyzing the traveler's social media posts or photos to understand the traveler's interests in more detail and reflect this in the plan 2. The system of claim 1.

3. The analysis unit Considering the traveler's health and fitness level, suggesting appropriate activities and attractions 2. The system of claim 1.

4. The proposal unit Real-time recommendations for the best attractions and events based on local weather and seasons 2. The system of claim 1.

5. The proposal unit Suggesting optimal transportation modes and routes to minimize travel time for said travelers 2. The system of claim 1.

6. The proposal unit Analyzing the traveler's past accommodation history, learning their preferences and tendencies, and reflecting this in their next accommodation selection 2. The system of claim 1.

7. The analysis unit Analyze the emotions felt by the traveler when they input their information and propose a plan that will elicit those positive emotions.

2. The system of claim 1.

8. The proposal unit Predicting the emotions felt by the traveler at the spots they visit and suggesting spots that will provide a positive experience 2. The system of claim 1.

Images