System

Abstract

An object of a system according to an embodiment is to propose an optimal travel plan on the basis of desired conditions of a user and unify reservation procedures.SOLUTION: A system includes an information collection part, an analysis part, a proposal part, and a reservation part. The information collection unit collects desired conditions of a user. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes an optimal travel plan based on the information analyzed by the analysis unit. The reservation unit unifies reservation procedures based on the travel plan proposed by the proposal 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 had the problem that travel information gathering and reservation procedures are complicated and time-consuming for users.

[0005] The system according to the embodiment aims to propose an optimal travel plan based on the user's desired conditions and to unify the reservation procedure. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a reservation unit. The information collection unit collects desired conditions of a user. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes an optimal travel plan based on the information analyzed by the analysis unit. The reservation unit unifies the reservation procedure based on the travel plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal travel plan based on the user's desired conditions and centralize the reservation procedure. [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 comprehensive travel planning AI service according to an embodiment of the present invention is a system that uses AI to propose comprehensive travel plans for people who want to enjoy traveling but find the process of information gathering, itinerary planning, and reservation procedures cumbersome. This system uses AI to propose optimal itineraries based on the number of participants, budget, and schedule, and provides consistent support from itinerary creation to arrangements and even post-trip support. As a result, the comprehensive travel planning AI service reduces the burden on users and enables them to enjoy original and fulfilling trips.

[0029] The comprehensive travel planning AI service according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a reservation unit. The information collection unit collects the user's desired conditions. For example, it collects information such as travel destination, budget, schedule, and preferred activities. The analysis unit analyzes the information collected by the information collection unit. For example, it performs the analysis using data mining, statistical analysis, or machine learning algorithms. The proposal unit proposes an optimal travel plan based on the information analyzed by the analysis unit. For example, it customizes the plan based on the user's desired conditions and compares it with past data. The reservation unit centralizes the reservation procedure based on the travel plan proposed by the proposal unit. For example, it makes reservations for accommodation, transportation, and activities all at once. As a result, the comprehensive travel planning AI service according to the embodiment proposes an optimal travel plan based on the user's desired conditions and centralizes the reservation procedure, thereby reducing the burden on the user.

[0030] The information collection unit analyzes the user's past travel history or social media posts to understand individual preferences in more detail. For example, the generation AI analyzes the user's past travel history and collects data on places visited and activities participated in. For example, the information collection unit understands the user's preferences in detail based on ratings of tourist spots and accommodations visited in the past. The information collection unit also analyzes social media posts by the generation AI to understand the user's tastes and interests. For example, it analyzes posted photos and comments to identify the user's preferences. This allows the system to understand the user's preferences in detail and propose travel plans that will provide greater satisfaction.

[0031] The information collection unit can generate a health-conscious travel plan based on the user's health condition and allergy information. In the information collection unit, for example, the generation AI analyzes the user's health condition and proposes a health-conscious travel plan. For example, it selects safe meals and activities taking into account chronic illnesses and allergies. In addition, the information collection unit analyzes the user's allergy information and proposes meals and accommodations that accommodate allergies. For example, it selects restaurants that accommodate food allergies and allergy-free accommodations. Furthermore, in the information collection unit, the generation AI monitors the user's health condition and supports health management during travel. For example, it provides activity suggestions and health management advice based on the user's health condition. This makes it possible to propose a travel plan that takes into account the user's health condition and allergy information.

[0032] The suggestion unit learns the user's past travel plans and their ratings, and can propose itineraries that will provide more satisfaction. For example, the suggestion unit uses a generation AI to learn the user's past travel plans and their ratings, and propose itineraries that will provide more satisfaction. For example, it prioritizes suggestions of tourist spots and activities that have received high ratings in the past. The suggestion unit also uses the generation AI to analyze the user's past travel plans and customize them based on the user's preferences. For example, it proposes plans that suit the user's preferences based on data on places visited in the past and activities participated in. Furthermore, the suggestion unit uses the generation AI to analyze the user's rating data and identify areas for improvement in the travel plan. For example, it improves areas that caused dissatisfaction in past trips and proposes plans that will provide more satisfaction. In this way, the suggestion unit can learn the user's past travel plans and their ratings, and propose itineraries that will provide more satisfaction.

[0033] The suggestion unit can obtain local weather or event information in real time and suggest optimal times to visit tourist spots. For example, the suggestion unit uses the generation AI to obtain local weather information in real time and suggest optimal times to visit tourist spots. For example, it suggests outdoor activities on sunny days. The suggestion unit also uses the generation AI to obtain local event information in real time and suggest plans that match the events. For example, it suggests plans to participate in events based on information about local festivals and concerts. Furthermore, the suggestion unit adjusts travel plans based on the weather and event information. For example, it suggests indoor activities depending on changes in the weather. This allows the suggestion unit to obtain local weather and event information in real time and suggest optimal times to visit tourist spots.

[0034] The suggestion unit can propose travel plans for different seasons and time periods, providing the user with a variety of options. For example, the generation AI in the suggestion unit proposes travel plans for different seasons, providing the user with a variety of options. For example, the suggestion unit proposes seasonal activities such as cherry blossom viewing in the spring and ski trips in the winter. The suggestion unit also proposes travel plans for different time periods, such as morning walks and night tours, proposing activities for each time period. Furthermore, the suggestion unit allows the generation AI to customize plans according to the season and time period. For example, the suggestion unit proposes seasonal events and tourist spots for each time period. This allows the suggestion unit to propose travel plans for different seasons and time periods, providing the user with a variety of options.

[0035] The suggestion unit can suggest popular tourist spots and activities based on reviews or ratings from other users. For example, the suggestion unit uses a generation AI to analyze other users' reviews and ratings and suggest popular tourist spots and activities. For example, it prioritizes suggesting highly rated tourist spots and activities. The suggestion unit also uses a generation AI to analyze data from review sites and social media to identify popular spots. For example, it suggests popular tourist spots based on the number of visitors and review ratings. Furthermore, the suggestion unit uses the generation AI to improve travel plans based on feedback from other users. For example, it suggests activities that multiple users have given high ratings. This allows the suggestion unit to suggest popular tourist spots and activities based on other users' reviews and ratings.

[0036] The reservation unit can search multiple reservation sites across the network and automatically select the most advantageous plan. For example, the generation AI in the reservation unit can search multiple reservation sites across the network and automatically select the most advantageous plan. For example, it can compare prices for accommodation and activities and propose the most cost-effective plan. The generation AI in the reservation unit can also analyze data from reservation sites and select the optimal plan. For example, it can select a plan taking into consideration price, benefits, cancellation policy, etc. Furthermore, the generation AI in the reservation unit can propose the most advantageous plan based on the user's desired conditions. For example, it can select the optimal plan based on budget and dates. This allows the generation AI to search multiple reservation sites across the network and automatically select the most advantageous plan.

[0037] The reservation unit works in conjunction with the user's calendar and can suggest the best reservation timing for their schedule. For example, the generation AI in the reservation unit works in conjunction with the user's calendar and suggests the best reservation timing for their schedule. For example, it suggests the best reservation date based on the user's available dates. The reservation unit also analyzes calendar data and suggests reservation plans that fit the schedule. For example, it suggests reservation timing taking into account work and personal plans. Furthermore, the generation AI in the reservation unit works in conjunction with the calendar to optimize reservations. For example, it adjusts reservation dates so that they do not overlap with important appointments. This allows the reservation unit to work in conjunction with the user's calendar and suggest the best reservation timing for their schedule.

[0038] The reservation unit can suggest popular reservation plans by referring to the reservation history of other users. For example, the generation AI in the reservation unit analyzes the reservation history of other users and suggests popular reservation plans. For example, it refers to reservation history for the same destination or activity. The reservation unit also analyzes reservation history data and identifies popular plans. For example, it suggests popular plans based on the number of reservations and review ratings. Furthermore, the reservation unit improves reservation plans based on feedback from other users. For example, it suggests plans that have been highly rated by multiple users. This allows the reservation unit to suggest popular reservation plans by referring to the reservation history of other users.

[0039] The analysis unit acquires the user's location information in real time and can suggest the optimal route and means of transportation. For example, the generation AI acquires the user's location information in real time and suggests the optimal route and means of transportation. For example, it suggests the shortest route from the current location to the destination. The analysis unit also has the generation AI analyze the location information and select the means of transportation. For example, it suggests options such as public transportation, rental cars, and taxis. Furthermore, the analysis unit has the generation AI adjust the travel plan based on the location information. For example, it changes the route to take traffic congestion and delays into account. This allows the analysis unit to acquire the user's location information in real time and suggest the optimal route and means of transportation.

[0040] The analysis unit can provide information about the local language or culture, supporting users in smooth communication. For example, the analysis unit allows the generation AI to provide information about the local language and culture, supporting users in smooth communication. For example, it can provide basic greetings and phrases. The analysis unit can also allow the generation AI to provide information about the local culture, supporting users in understanding local customs and manners. For example, it can explain the local history and cultural background. Furthermore, the analysis unit can support communication based on the language and culture information provided by the generation AI. For example, it can suggest the use of a translation app or the provision of a local guide. This allows the generation AI to provide information about the local language and culture, supporting users in smooth communication.

[0041] The analysis unit can collect real-time feedback from other travelers and provide the latest local information. For example, the generation AI collects real-time feedback from other travelers in the analysis unit and provides the latest local information. For example, it provides information on the congestion status of tourist spots and event schedules. The analysis unit also allows the generation AI to analyze the feedback and understand the local situation. For example, it adjusts the travel plan based on traffic and weather information. Furthermore, the analysis unit allows the generation AI to improve the travel plan based on feedback from other travelers. For example, it improves problems pointed out by multiple travelers. This allows the generation AI to collect real-time feedback from other travelers and provide the latest local information.

[0042] The analysis unit monitors the user's health condition and can provide information about medical institutions as needed. For example, the generation AI in the analysis unit monitors the user's health condition and provides information about medical institutions as needed. For example, if the user feels unwell, the analysis unit can guide the user to the nearest hospital or clinic. The generation AI in the analysis unit also analyzes the user's health condition and suggests appropriate medical institutions. For example, the analysis unit can guide the user to medical institutions that can treat chronic illnesses or allergies. Furthermore, the generation AI in the analysis unit supports health management during travel based on the user's health condition. For example, the analysis unit can suggest activities and provide health management advice according to the user's health condition. This allows the analysis unit to monitor the user's health condition and provide information about medical institutions as needed.

[0043] The suggestion unit can automatically organize photos and videos taken during a user's trip and create a memory album. For example, the generation AI in the suggestion unit automatically organizes photos and videos taken during a user's trip and creates a memory album. For example, it classifies photos and videos by date and location and creates an album. The suggestion unit also analyzes photo and video data and suggests the optimal layout. For example, it creates an album that highlights the highlights of the trip. Furthermore, the suggestion unit uses the generation AI to improve the album based on user feedback. For example, it prioritizes the placement of photos and videos that the user particularly likes. This allows the suggestion unit to automatically organize photos and videos taken during a user's trip and create a memory album.

[0044] The suggestion unit can collect post-travel feedback from other users and identify common issues or areas for improvement. In the suggestion unit, for example, the generation AI collects post-travel feedback from other users and identifies common issues or areas for improvement. For example, it improves problems pointed out by multiple users. In addition, the suggestion unit analyzes the feedback data and extracts common issues. For example, it identifies areas where multiple users felt dissatisfied and proposes improvements. Furthermore, the suggestion unit improves the travel plan based on the feedback from other users. For example, it proposes a plan to solve common issues. In this way, it is possible to collect post-travel feedback from other users and identify common issues or areas for improvement.

[0045] The analysis unit can monitor the user's health condition after traveling and suggest recovery methods. For example, the generation AI in the analysis unit can monitor the user's health condition after traveling and suggest recovery methods. For example, it can suggest relaxation methods to soothe the body tired during traveling. The generation AI in the analysis unit can also analyze the health condition and suggest appropriate recovery methods. For example, it can suggest rest, relaxation exercises, nutritional supplements, etc. Furthermore, the generation AI in the analysis unit can support recovery based on the user's health condition. For example, it can provide advice and support according to the health condition. This allows the generation AI to monitor the user's health condition after traveling and suggest recovery methods.

[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 analyzes the user's food preferences during their trip and can suggest recommended local restaurants. For example, it selects local restaurants based on data on dishes the user has previously enjoyed. The analysis unit also uses the generation AI to analyze the user's food preferences and customize the meal plan. For example, it provides vegetarian and gluten-free options. Furthermore, the analysis unit uses the generation AI to analyze reviews of local restaurants and suggest highly rated restaurants. For example, it prioritizes suggestions of restaurants that match the user's preferences. This makes it possible to suggest recommended local restaurants based on the user's food preferences.

[0048] The suggestion unit can analyze the user's activity preferences during travel and suggest recommended local activities. For example, it selects local activities based on data on activities the user has enjoyed in the past. The suggestion unit also uses the generation AI to analyze the user's activity preferences and customize the activity plan. For example, it provides options such as outdoor activities and cultural experiences. Furthermore, the suggestion unit uses the generation AI to analyze reviews of local activities and suggest highly rated activities. For example, it prioritizes suggestions of activities that match the user's preferences. This makes it possible to suggest recommended local activities based on the user's activity preferences.

[0049] The analysis unit analyzes the user's transportation preferences during the trip and can suggest the optimal transportation method. For example, it selects a local transportation method based on data on transportation methods the user has used in the past. The analysis unit also uses the generation AI to analyze the user's transportation preferences and customize the travel plan. For example, it provides options for public transportation and rental cars. The analysis unit also uses the generation AI to analyze local traffic information and suggest the optimal transportation method. For example, it selects transportation methods taking traffic congestion and delays into consideration. This makes it possible to suggest the optimal transportation method based on the user's transportation preferences.

[0050] The suggestion unit can analyze the user's shopping preferences during their trip and suggest recommended local shopping spots. For example, it selects local shopping spots based on data on products the user has previously purchased. The suggestion unit also uses the generation AI to analyze the user's shopping preferences and customize a shopping plan. For example, it provides fashion and souvenir options. Furthermore, the suggestion unit uses the generation AI to analyze reviews of local shopping spots and suggest highly rated spots. For example, it prioritizes suggesting shopping spots that match the user's preferences. This makes it possible to suggest recommended local shopping spots based on the user's shopping preferences.

[0051] The analysis unit analyzes the user's entertainment preferences during their trip and can suggest recommended local entertainment. For example, it selects local entertainment based on data on entertainment the user has enjoyed in the past. The analysis unit also uses the generation AI to analyze the user's entertainment preferences and customize the entertainment plan. For example, it provides options for movies and concerts. Furthermore, the analysis unit uses the generation AI to analyze reviews of local entertainment and suggest highly rated entertainment. For example, it prioritizes suggesting entertainment that matches the user's preferences. This makes it possible to suggest recommended local entertainment based on the user's entertainment preferences.

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

[0053] Step 1: The information gathering unit gathers the user's desired conditions, such as travel destination, budget, schedule, and preferred activities. Step 2: The analysis unit analyzes the information collected by the information collection unit, for example, using data mining, statistical analysis, or machine learning algorithms. Step 3: The proposal unit proposes an optimal travel plan based on the information analyzed by the analysis unit. For example, it customizes the plan based on the user's desired conditions and compares it with past data. Step 4: The reservation department centralizes the reservation process based on the travel plan proposed by the proposal department, for example, booking accommodation, transportation, and activities all at once.

[0054] (Example 2) The comprehensive travel planning AI service according to an embodiment of the present invention is a system that uses AI to propose comprehensive travel plans for people who want to enjoy traveling but find the process of information gathering, itinerary planning, and reservation procedures cumbersome. This system uses AI to propose optimal itineraries based on the number of participants, budget, and schedule, and provides consistent support from itinerary creation to arrangements and even post-trip support. As a result, the comprehensive travel planning AI service reduces the burden on users and enables them to enjoy original and fulfilling trips.

[0055] The comprehensive travel planning AI service according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a reservation unit. The information collection unit collects the user's desired conditions. For example, it collects information such as travel destination, budget, schedule, and preferred activities. The analysis unit analyzes the information collected by the information collection unit. For example, it performs the analysis using data mining, statistical analysis, or machine learning algorithms. The proposal unit proposes an optimal travel plan based on the information analyzed by the analysis unit. For example, it customizes the plan based on the user's desired conditions and compares it with past data. The reservation unit centralizes the reservation procedure based on the travel plan proposed by the proposal unit. For example, it makes reservations for accommodation, transportation, and activities all at once. As a result, the comprehensive travel planning AI service according to the embodiment proposes an optimal travel plan based on the user's desired conditions and centralizes the reservation procedure, thereby reducing the burden on the user.

[0056] The information collection unit analyzes the user's past travel history or social media posts to understand individual preferences in more detail. For example, the generation AI analyzes the user's past travel history and collects data on places visited and activities participated in. For example, the information collection unit understands the user's preferences in detail based on ratings of tourist spots and accommodations visited in the past. The information collection unit also analyzes social media posts by the generation AI to understand the user's tastes and interests. For example, it analyzes posted photos and comments to identify the user's preferences. This allows the system to understand the user's preferences in detail and propose travel plans that will provide greater satisfaction.

[0057] The information collection unit analyzes the user's real-time emotional state based on the emotion estimation function and can propose a travel plan that best suits their mood at that time. For example, the information collection unit uses a generation AI to analyze the user's real-time emotional state and propose a travel plan that best suits their mood at that time. For example, if the user is feeling stressed, the information collection unit proposes a plan that emphasizes relaxation. The information collection unit also uses a generation AI to analyze the user's facial expressions and voice to grasp their emotional state. For example, the information collection unit calculates an emotion score using facial recognition technology or voice analysis technology. Furthermore, the information collection unit uses a generation AI to analyze the user's biometric data and evaluate their emotional state. For example, the information collection unit calculates an emotion score based on heart rate and electrodermal activity. This allows the information collection unit to propose an optimal travel plan based on the user's real-time emotional state.

[0058] The information collection unit can generate a health-conscious travel plan based on the user's health condition and allergy information. In the information collection unit, for example, the generation AI analyzes the user's health condition and proposes a health-conscious travel plan. For example, it selects safe meals and activities taking into account chronic illnesses and allergies. In addition, the information collection unit analyzes the user's allergy information and proposes meals and accommodations that accommodate allergies. For example, it selects restaurants that accommodate food allergies and allergy-free accommodations. Furthermore, in the information collection unit, the generation AI monitors the user's health condition and supports health management during travel. For example, it provides activity suggestions and health management advice based on the user's health condition. This makes it possible to propose a travel plan that takes into account the user's health condition and allergy information.

[0059] The suggestion unit learns the user's past travel plans and their ratings, and can propose itineraries that will provide more satisfaction. For example, the suggestion unit uses a generation AI to learn the user's past travel plans and their ratings, and propose itineraries that will provide more satisfaction. For example, it prioritizes suggestions of tourist spots and activities that have received high ratings in the past. The suggestion unit also uses the generation AI to analyze the user's past travel plans and customize them based on the user's preferences. For example, it proposes plans that suit the user's preferences based on data on places visited in the past and activities participated in. Furthermore, the suggestion unit uses the generation AI to analyze the user's rating data and identify areas for improvement in the travel plan. For example, it improves areas that caused dissatisfaction in past trips and proposes plans that will provide more satisfaction. In this way, the suggestion unit can learn the user's past travel plans and their ratings, and propose itineraries that will provide more satisfaction.

[0060] The suggestion unit can obtain local weather or event information in real time and suggest optimal times to visit tourist spots. For example, the suggestion unit uses the generation AI to obtain local weather information in real time and suggest optimal times to visit tourist spots. For example, it suggests outdoor activities on sunny days. The suggestion unit also uses the generation AI to obtain local event information in real time and suggest plans that match the events. For example, it suggests plans to participate in events based on information about local festivals and concerts. Furthermore, the suggestion unit adjusts travel plans based on the weather and event information. For example, it suggests indoor activities depending on changes in the weather. This allows the suggestion unit to obtain local weather and event information in real time and suggest optimal times to visit tourist spots.

[0061] The suggestion unit uses the emotion estimation function to suggest activities based on the user's emotions, thereby improving satisfaction during the trip. The suggestion unit, for example, uses the emotion estimation function to suggest activities based on the user's emotions. For example, if the user feels like relaxing, it suggests a spa or massage. The suggestion unit also uses the generation AI to analyze the user's emotional state and suggest activities according to the emotion. For example, if the user is excited, it suggests an adventure activity. Furthermore, the suggestion unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest appropriate activities. For example, if the user is tired, it suggests a relaxation activity. This makes it possible to suggest activities based on the user's emotions and improve satisfaction during the trip.

[0062] The suggestion unit can propose travel plans for different seasons and time periods, providing the user with a variety of options. For example, the generation AI in the suggestion unit proposes travel plans for different seasons, providing the user with a variety of options. For example, the suggestion unit proposes seasonal activities such as cherry blossom viewing in the spring and ski trips in the winter. The suggestion unit also proposes travel plans for different time periods, such as morning walks and night tours, proposing activities for each time period. Furthermore, the suggestion unit allows the generation AI to customize plans according to the season and time period. For example, the suggestion unit proposes seasonal events and tourist spots for each time period. This allows the suggestion unit to propose travel plans for different seasons and time periods, providing the user with a variety of options.

[0063] The suggestion unit can suggest popular tourist spots and activities based on reviews or ratings from other users. For example, the suggestion unit uses a generation AI to analyze other users' reviews and ratings and suggest popular tourist spots and activities. For example, it prioritizes suggesting highly rated tourist spots and activities. The suggestion unit also uses a generation AI to analyze data from review sites and social media to identify popular spots. For example, it suggests popular tourist spots based on the number of visitors and review ratings. Furthermore, the suggestion unit uses the generation AI to improve travel plans based on feedback from other users. For example, it suggests activities that multiple users have given high ratings. This allows the suggestion unit to suggest popular tourist spots and activities based on other users' reviews and ratings.

[0064] The suggestion unit uses the emotion estimation function to suggest the activities that the user will enjoy most in real time, thereby optimizing the travel experience. The suggestion unit, for example, uses the emotion estimation function to suggest the activities that the user will enjoy most in real time. For example, if the user seems to be having fun, the suggestion unit suggests a plan that emphasizes entertainment. The suggestion unit also uses the generation AI to analyze the user's emotional state and suggest the most appropriate activity. For example, if the user feels like relaxing, the suggestion unit suggests a relaxation activity. Furthermore, the suggestion unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest an appropriate activity. For example, if the user is excited, the suggestion unit suggests an adventure activity. This makes it possible to suggest the activities that the user will enjoy most in real time, thereby optimizing the travel experience.

[0065] The reservation unit can search multiple reservation sites across the network and automatically select the most advantageous plan. For example, the generation AI in the reservation unit can search multiple reservation sites across the network and automatically select the most advantageous plan. For example, it can compare prices for accommodation and activities and propose the most cost-effective plan. The generation AI in the reservation unit can also analyze data from reservation sites and select the optimal plan. For example, it can select a plan taking into consideration price, benefits, cancellation policy, etc. Furthermore, the generation AI in the reservation unit can propose the most advantageous plan based on the user's desired conditions. For example, it can select the optimal plan based on budget and dates. This allows the generation AI to search multiple reservation sites across the network and automatically select the most advantageous plan.

[0066] The reservation unit can use the emotion estimation function to provide an interface that reduces stress when the user goes through the reservation process. The reservation unit, for example, uses the emotion estimation function to provide an interface that reduces stress when the user goes through the reservation process. For example, it analyzes the user's facial expressions and voice and suggests simple operations if the user is feeling stressed. The reservation unit also uses the generation AI to analyze the user's emotional state and provide advice to reduce stress. For example, it suggests relaxing music or guides. Furthermore, the reservation unit uses the emotion estimation function to monitor the user's emotional state in real time and provide an appropriate interface. For example, it suggests a user-friendly design and intuitive operation methods. This makes it possible to provide an interface that reduces stress when the user goes through the reservation process.

[0067] The reservation unit works in conjunction with the user's calendar and can suggest the best reservation timing for their schedule. For example, the generation AI in the reservation unit works in conjunction with the user's calendar and suggests the best reservation timing for their schedule. For example, it suggests the best reservation date based on the user's available dates. The reservation unit also analyzes calendar data and suggests reservation plans that fit the schedule. For example, it suggests reservation timing taking into account work and personal plans. Furthermore, the generation AI in the reservation unit works in conjunction with the calendar to optimize reservations. For example, it adjusts reservation dates so that they do not overlap with important appointments. This allows the reservation unit to work in conjunction with the user's calendar and suggest the best reservation timing for their schedule.

[0068] The reservation unit can suggest popular reservation plans by referring to the reservation history of other users. For example, the generation AI in the reservation unit analyzes the reservation history of other users and suggests popular reservation plans. For example, it refers to reservation history for the same destination or activity. The reservation unit also analyzes reservation history data and identifies popular plans. For example, it suggests popular plans based on the number of reservations and review ratings. Furthermore, the reservation unit improves reservation plans based on feedback from other users. For example, it suggests plans that have been highly rated by multiple users. This allows the reservation unit to suggest popular reservation plans by referring to the reservation history of other users.

[0069] The reservation unit uses the emotion estimation function to analyze the user's emotions in real time when making a reservation and make suggestions that will elicit positive emotions. For example, the reservation unit uses the emotion estimation function to analyze the user's emotions in real time when making a reservation and make suggestions that will elicit positive emotions. For example, if the user seems to be having fun, the reservation unit suggests a plan that emphasizes entertainment. The reservation unit also uses the generation AI to analyze the user's emotional state and provide advice to elicit positive emotions. For example, the reservation unit suggests music or a guide to help them relax. Furthermore, the reservation unit uses the emotion estimation function to monitor the user's emotional state in real time and make appropriate suggestions. For example, if the user is feeling stressed, the reservation unit suggests a relaxation activity. This makes it possible to analyze the user's emotions in real time when making a reservation and make suggestions that will elicit positive emotions.

[0070] The analysis unit acquires the user's location information in real time and can suggest the optimal route and means of transportation. For example, the generation AI acquires the user's location information in real time and suggests the optimal route and means of transportation. For example, it suggests the shortest route from the current location to the destination. The analysis unit also has the generation AI analyze the location information and select the means of transportation. For example, it suggests options such as public transportation, rental cars, and taxis. Furthermore, the analysis unit has the generation AI adjust the travel plan based on the location information. For example, it changes the route to take traffic congestion and delays into account. This allows the analysis unit to acquire the user's location information in real time and suggest the optimal route and means of transportation.

[0071] The analysis unit can provide information about the local language or culture, supporting users in smooth communication. For example, the analysis unit allows the generation AI to provide information about the local language and culture, supporting users in smooth communication. For example, it can provide basic greetings and phrases. The analysis unit can also allow the generation AI to provide information about the local culture, supporting users in understanding local customs and manners. For example, it can explain the local history and cultural background. Furthermore, the analysis unit can support communication based on the language and culture information provided by the generation AI. For example, it can suggest the use of a translation app or the provision of a local guide. This allows the generation AI to provide information about the local language and culture, supporting users in smooth communication.

[0072] The analysis unit can use the emotion estimation function to provide advice or support in real time according to the user's emotional state. For example, the analysis unit uses the emotion estimation function to provide advice or support in real time according to the user's emotional state. For example, if the user is feeling stressed, relaxation advice is provided. The analysis unit also has the generation AI analyze the user's emotional state and provide appropriate support. For example, it suggests emergency responses and on-site support services. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and provide appropriate advice. For example, if the user is feeling anxious, it provides advice that gives a sense of security. This makes it possible to provide advice and support in real time according to the user's emotional state.

[0073] The analysis unit can collect real-time feedback from other travelers and provide the latest local information. For example, the generation AI collects real-time feedback from other travelers in the analysis unit and provides the latest local information. For example, it provides information on the congestion status of tourist spots and event schedules. The analysis unit also allows the generation AI to analyze the feedback and understand the local situation. For example, it adjusts the travel plan based on traffic and weather information. Furthermore, the analysis unit allows the generation AI to improve the travel plan based on feedback from other travelers. For example, it improves problems pointed out by multiple travelers. This allows the generation AI to collect real-time feedback from other travelers and provide the latest local information.

[0074] The analysis unit monitors the user's health condition and can provide information about medical institutions as needed. For example, the generation AI in the analysis unit monitors the user's health condition and provides information about medical institutions as needed. For example, if the user feels unwell, the analysis unit can guide the user to the nearest hospital or clinic. The generation AI in the analysis unit also analyzes the user's health condition and suggests appropriate medical institutions. For example, the analysis unit can guide the user to medical institutions that can treat chronic illnesses or allergies. Furthermore, the generation AI in the analysis unit supports health management during travel based on the user's health condition. For example, the analysis unit can suggest activities and provide health management advice according to the user's health condition. This allows the analysis unit to monitor the user's health condition and provide information about medical institutions as needed.

[0075] The analysis unit can use the emotion estimation function to suggest relaxation methods to reduce the stress the user feels while traveling. For example, the analysis unit uses the emotion estimation function to suggest relaxation methods to reduce the stress the user feels while traveling. For example, if the user is feeling stressed, the analysis unit suggests relaxation activities. The analysis unit also uses the generation AI to analyze the user's emotional state and provide advice to reduce stress. For example, the analysis unit suggests music or guides to help users relax. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest appropriate relaxation methods. For example, if the user is tired, the analysis unit suggests relaxation exercises. This makes it possible to suggest relaxation methods to reduce the stress the user feels while traveling.

[0076] The suggestion unit can automatically organize photos and videos taken during a user's trip and create a memory album. For example, the generation AI in the suggestion unit automatically organizes photos and videos taken during a user's trip and creates a memory album. For example, it classifies photos and videos by date and location and creates an album. The suggestion unit also analyzes photo and video data and suggests the optimal layout. For example, it creates an album that highlights the highlights of the trip. Furthermore, the suggestion unit uses the generation AI to improve the album based on user feedback. For example, it prioritizes the placement of photos and videos that the user particularly likes. This allows the suggestion unit to automatically organize photos and videos taken during a user's trip and create a memory album.

[0077] The suggestion unit can use the emotion estimation function to analyze the user's post-trip emotions and make suggestions for the next trip. For example, the suggestion unit can use the emotion estimation function to analyze the user's post-trip emotions and make suggestions for the next trip. For example, the suggestion unit can incorporate activities that the user particularly enjoyed into the next plan. The suggestion unit also uses the generation AI to analyze the user's emotional state and customize the next travel plan. For example, the suggestion unit can suggest relaxing plans or active plans based on the user's post-trip emotional state. Furthermore, the suggestion unit can use the emotion estimation function to monitor the user's emotional state in real time and optimize the next travel plan. For example, the suggestion unit can reflect the activities that the user found satisfying in the next plan. This allows the suggestion unit to analyze the user's post-trip emotions and make suggestions for the next trip.

[0078] The suggestion unit can collect post-travel feedback from other users and identify common issues or areas for improvement. In the suggestion unit, for example, the generation AI collects post-travel feedback from other users and identifies common issues or areas for improvement. For example, it improves problems pointed out by multiple users. In addition, the suggestion unit analyzes the feedback data and extracts common issues. For example, it identifies areas where multiple users felt dissatisfied and proposes improvements. Furthermore, the suggestion unit improves the travel plan based on the feedback from other users. For example, it proposes a plan to solve common issues. In this way, it is possible to collect post-travel feedback from other users and identify common issues or areas for improvement.

[0079] The analysis unit can monitor the user's health condition after traveling and suggest recovery methods. For example, the generation AI in the analysis unit can monitor the user's health condition after traveling and suggest recovery methods. For example, it can suggest relaxation methods to soothe the body tired during traveling. The generation AI in the analysis unit can also analyze the health condition and suggest appropriate recovery methods. For example, it can suggest rest, relaxation exercises, nutritional supplements, etc. Furthermore, the generation AI in the analysis unit can support recovery based on the user's health condition. For example, it can provide advice and support according to the health condition. This allows the generation AI to monitor the user's health condition after traveling and suggest recovery methods.

[0080] The analysis unit can use the emotion estimation function to suggest activities that will help the user maintain the positive emotions they feel after traveling. For example, the analysis unit uses the emotion estimation function to suggest activities that will help the user maintain the positive emotions they feel after traveling. For example, it may suggest incorporating activities that the user enjoyed during the trip into their daily life. The analysis unit also uses the generation AI to analyze the user's emotional state and provide advice to maintain positive emotions. For example, it may suggest music or a guide to help them relax. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest appropriate activities. For example, it may suggest incorporating activities that the user found satisfying into their daily life. This makes it possible to suggest activities that will help the user maintain the positive emotions they feel after traveling.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The analysis unit analyzes the user's food preferences during their trip and can suggest recommended local restaurants. For example, it selects local restaurants based on data on dishes the user has previously enjoyed. The analysis unit also uses the generation AI to analyze the user's food preferences and customize the meal plan. For example, it provides vegetarian and gluten-free options. Furthermore, the analysis unit uses the generation AI to analyze reviews of local restaurants and suggest highly rated restaurants. For example, it prioritizes suggestions of restaurants that match the user's preferences. This makes it possible to suggest recommended local restaurants based on the user's food preferences.

[0083] The suggestion unit can analyze the user's activity preferences during travel and suggest recommended local activities. For example, it selects local activities based on data on activities the user has enjoyed in the past. The suggestion unit also uses the generation AI to analyze the user's activity preferences and customize the activity plan. For example, it provides options such as outdoor activities and cultural experiences. Furthermore, the suggestion unit uses the generation AI to analyze reviews of local activities and suggest highly rated activities. For example, it prioritizes suggestions of activities that match the user's preferences. This makes it possible to suggest recommended local activities based on the user's activity preferences.

[0084] The analysis unit analyzes the user's transportation preferences during the trip and can suggest the optimal transportation method. For example, it selects a local transportation method based on data on transportation methods the user has used in the past. The analysis unit also uses the generation AI to analyze the user's transportation preferences and customize the travel plan. For example, it provides options for public transportation and rental cars. The analysis unit also uses the generation AI to analyze local traffic information and suggest the optimal transportation method. For example, it selects transportation methods taking traffic congestion and delays into consideration. This makes it possible to suggest the optimal transportation method based on the user's transportation preferences.

[0085] The suggestion unit can analyze the user's shopping preferences during their trip and suggest recommended local shopping spots. For example, it selects local shopping spots based on data on products the user has previously purchased. The suggestion unit also uses the generation AI to analyze the user's shopping preferences and customize a shopping plan. For example, it provides fashion and souvenir options. Furthermore, the suggestion unit uses the generation AI to analyze reviews of local shopping spots and suggest highly rated spots. For example, it prioritizes suggesting shopping spots that match the user's preferences. This makes it possible to suggest recommended local shopping spots based on the user's shopping preferences.

[0086] The analysis unit analyzes the user's entertainment preferences during their trip and can suggest recommended local entertainment. For example, it selects local entertainment based on data on entertainment the user has enjoyed in the past. The analysis unit also uses the generation AI to analyze the user's entertainment preferences and customize the entertainment plan. For example, it provides options for movies and concerts. Furthermore, the analysis unit uses the generation AI to analyze reviews of local entertainment and suggest highly rated entertainment. For example, it prioritizes suggesting entertainment that matches the user's preferences. This makes it possible to suggest recommended local entertainment based on the user's entertainment preferences.

[0087] The suggestion unit can use the emotion estimation function to suggest a meal plan based on the user's emotions. For example, if the user feels like relaxing, it will suggest a meal that has a relaxing effect. The suggestion unit also uses the generation AI to analyze the user's emotional state and suggest a meal plan according to the emotion. For example, if the user is excited, it will suggest a meal that will replenish energy. Furthermore, the suggestion unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest an appropriate meal plan. For example, if the user is tired, it will suggest a meal that has a relaxing effect. This makes it possible to suggest a meal plan based on the user's emotions.

[0088] The analysis unit can use the emotion estimation function to suggest a shopping plan based on the user's emotions. For example, if the user feels like relaxing, it will suggest products with a relaxation effect. The analysis unit also uses the generation AI to analyze the user's emotional state and suggest a shopping plan according to the emotion. For example, if the user is excited, it will suggest products that will replenish energy. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest an appropriate shopping plan. For example, if the user is tired, it will suggest products with a relaxation effect. This makes it possible to suggest a shopping plan based on the user's emotions.

[0089] The suggestion unit can use the emotion estimation function to suggest an entertainment plan based on the user's emotions. For example, if the user feels like relaxing, it will suggest entertainment that has a relaxing effect. The suggestion unit also uses the generation AI to analyze the user's emotional state and suggest an entertainment plan according to the emotion. For example, if the user is excited, it will suggest entertainment that will replenish energy. Furthermore, the suggestion unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest an appropriate entertainment plan. For example, if the user is tired, it will suggest entertainment that has a relaxing effect. This makes it possible to suggest an entertainment plan based on the user's emotions.

[0090] The analysis unit can use the emotion estimation function to suggest a travel plan based on the user's emotions. For example, if the user feels like relaxing, it will suggest a means of transportation that has a relaxing effect. The analysis unit also uses the generation AI to analyze the user's emotional state and suggest a travel plan that corresponds to the emotion. For example, if the user is excited, it will suggest a means of transportation that will replenish energy. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest an appropriate travel plan. For example, if the user is tired, it will suggest a means of transportation that has a relaxing effect. This makes it possible to suggest a travel plan based on the user's emotions.

[0091] The suggestion unit can use the emotion estimation function to suggest tourist spots based on the user's emotions. For example, if the user feels like relaxing, it will suggest tourist spots that have a relaxing effect. The suggestion unit also uses the generation AI to analyze the user's emotional state and suggest tourist spots that correspond to the emotion. For example, if the user is excited, it will suggest tourist spots that will replenish energy. Furthermore, the suggestion unit uses the emotion estimation function to monitor the user's emotional state in real time and suggest appropriate tourist spots. For example, if the user is tired, it will suggest tourist spots that have a relaxing effect. This makes it possible to suggest tourist spots based on the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The information gathering unit gathers the user's desired conditions, such as travel destination, budget, schedule, and preferred activities. Step 2: The analysis unit analyzes the information collected by the information collection unit, for example, using data mining, statistical analysis, or machine learning algorithms. Step 3: The proposal unit proposes an optimal travel plan based on the information analyzed by the analysis unit. For example, it customizes the plan based on the user's desired conditions and compares it with past data. Step 4: The reservation department centralizes the reservation process based on the travel plan proposed by the proposal department, for example, booking accommodation, transportation, and activities all at once.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 collection unit that collects desired conditions of users; an analysis unit that analyzes the information collected by the information collection unit; a suggestion unit that proposes an optimal travel plan based on the information analyzed by the analysis unit; a reservation unit that unifies reservation procedures based on the travel plan proposed by the proposal unit. A system characterized by:

2. The information collecting unit Analyzing a user's past travel history or social media posts to better understand individual preferences 2. The system of claim 1.

3. The proposal unit Learns the user's past travel plans and their ratings, and suggests more satisfying itineraries 2. The system of claim 1.

4. The reservation unit Learns the user's past reservation history and suggests the best time and method for making a reservation 2. The system of claim 1.

5. The analysis unit Obtaining the user's location information in real time and suggesting the best route and transportation method 2. The system of claim 1.

6. The information collecting unit Analyzes the user's real-time emotional state based on emotion estimation and suggests travel plans that best suit their mood at that time.

2. The system of claim 1.

7. The proposal unit Providing activity suggestions based on user emotions to improve satisfaction during travel 2. The system of claim 1.

8. The analysis unit Providing real-time advice or support based on the user's emotional state 2. The system of claim 1.

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