System

Abstract

An object of a system according to an embodiment is to predict a behavior of a tourist and optimize an operation of a tourist spot or a tourist facility on the basis of the predicted behavior.SOLUTION: A system according to an embodiment includes a tourist data collection unit, a data analysis unit, a behavior prediction unit, a proposal generation unit, a feedback collection unit, and an optimization unit. The tourist data collection part collects tourist data. The data analysis part analyzes the tourist data collected by the tourist data collection part. The behavior prediction unit predicts a behavior of the tourist on the basis of the data analyzed by the data analysis unit. The proposal generation unit generates a proposal based on the behavior predicted by the behavior prediction unit. The feedback collection unit provides the proposal generated by the proposal generation unit to a tourist spot or a tourist facility, and collects the result as feedback. The optimization unit analyzes the feedback data collected by the feedback collection unit and optimizes the operation of the sightseeing spot or the sightseeing facility.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 of not being able to adequately predict tourist behavior and optimize the operation of tourist destinations and facilities based on that information.

[0005] The system according to the embodiment aims to predict tourist behavior and optimize the operation of tourist destinations and facilities based on the prediction. [Means for solving the problem]

[0006] The system according to the embodiment includes a tourist data collection unit, a data analysis unit, a behavior prediction unit, a proposal generation unit, a feedback collection unit, and an optimization unit. The tourist data collection unit collects tourist data. The data analysis unit analyzes the tourist data collected by the tourist data collection unit. The behavior prediction unit predicts tourist behavior based on the data analyzed by the data analysis unit. The proposal generation unit generates proposals based on the behavior predicted by the behavior prediction unit. The feedback collection unit provides the proposals generated by the proposal generation unit to tourist destinations and tourist facilities and collects the results as feedback. The optimization unit analyzes the feedback data collected by the feedback collection unit and optimizes the operation of tourist destinations and tourist facilities. [Effects of the Invention]

[0007] The system according to the embodiment can predict tourist behavior and optimize the operation of tourist destinations and facilities based on the prediction. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) Tourist Predictor according to an embodiment of the present invention is a system that predicts the behavior and preferences of tourists and makes optimal suggestions to tourist destinations and facilities, thereby enabling Tourist Predictor to provide more personalized services to tourists.

[0029] A tourist predictor according to an embodiment includes a tourist data collection unit, a data analysis unit, a behavior prediction unit, a proposal generation unit, a feedback collection unit, and an optimization unit. The tourist data collection unit collects tourist data. For example, it collects tourist behavior data and preference data from tourists. The tourist data collection unit can also collect data from tourists' social media posts and review sites. For example, it collects the history of tourist destinations visited and services used by tourists. The data analysis unit analyzes the tourist data collected by the tourist data collection unit. For example, the generation AI analyzes tourist behavior patterns and preferences. The behavior prediction unit predicts tourist behavior based on the data analyzed by the data analysis unit. For example, the generation AI predicts tourist destinations that tourists are likely to visit next and services that tourists are likely to use. The proposal generation unit generates proposals based on the behavior predicted by the behavior prediction unit. For example, the generation AI suggests tourist destinations that tourists are likely to visit next and accommodations, restaurants, events, etc. that tourists are likely to use. The feedback collection unit provides the proposals generated by the proposal generation unit to tourist destinations and tourist facilities and collects the results as feedback. For example, it checks whether tourists actually visited the proposed tourist destinations and used the proposed services. The optimization unit analyzes the feedback data collected by the feedback collection unit and optimizes the operation of tourist destinations and tourist facilities. For example, the generation AI provides services tailored to tourist preferences and strengthens the promotion of popular tourist destinations. This allows the tourist predictor according to the embodiment to provide more personalized services to tourists. For example, by proposing sightseeing plans tailored to tourist preferences, tourist satisfaction can be improved. It can also improve the efficiency of the operation of tourist destinations and tourist facilities and increase revenue.

[0030] The tourist data collection unit can collect data from tourists' social media posts or review sites and analyze it with the generation AI. For example, the tourist data collection unit collects tourists' social media posts and analyzes them with the generation AI to understand tourists' interests. For example, it analyzes the content of posts on Instagram or Twitter to identify tourists' favorite tourist destinations and activities. The tourist data collection unit also collects data from review sites and analyzes it with the generation AI to understand tourists' ratings and feedback. For example, it analyzes reviews on TripAdvisor or Yelp to identify tourist destinations and services that tourists have given high ratings to. The tourist data collection unit also integrates data from social media and review sites and analyzes it with the generation AI to understand tourists' preferences and behavioral patterns in more detail. For example, it compares the content of social media posts and reviews to identify consistent preferences. This makes it possible to understand tourists' detailed preferences and behavioral patterns.

[0031] The tourist data collection unit can collect tourist movement data in real time and analyze it with the generation AI. For example, the tourist data collection unit collects GPS data from tourists' smartphones in real time and analyzes it with the generation AI to understand the tourists' current locations and movement patterns. For example, it identifies the tourist attractions and travel routes that tourists are visiting. The tourist data collection unit also collects public transportation usage data in real time and analyzes it with the generation AI to understand the tourists' means of transportation and travel time. For example, it identifies the bus and train routes used by tourists. The tourist data collection unit also collects tourist movement data in real time and analyzes it with the generation AI to instantly reflect the tourists' behavior patterns. For example, it predicts the tourist attractions that tourists are currently visiting and the tourist attractions that they plan to visit next. This makes it possible to instantly reflect the tourists' current behavior patterns.

[0032] The tourist data collection unit can collect tourist purchasing history data and analyze it with the generation AI. For example, the tourist data collection unit collects tourist credit card and electronic money usage history and analyzes it with the generation AI to understand the relationship between purchasing behavior and tourist behavior. For example, it analyzes shopping and dining history at tourist destinations. The tourist data collection unit also collects tourist online shopping history and analyzes it with the generation AI to understand the relationship between purchasing behavior and tourist behavior. For example, it analyzes the history of souvenirs and goods purchased at tourist destinations. The tourist data collection unit also collects tourist purchasing history data and analyzes it with the generation AI to clarify the relationship between purchasing behavior and tourist behavior. For example, it identifies products and services that tourists who visit a particular tourist destination tend to purchase. This makes it possible to clarify the relationship between purchasing behavior and tourist behavior.

[0033] The tourist data collection unit can collect tourist health data and analyze it with the generation AI. For example, the tourist data collection unit collects health data such as the number of steps and heart rate from the tourist's smartwatch or fitness tracker and analyzes it with the generation AI. For example, it proposes a sightseeing plan based on the tourist's physical strength and health condition. The tourist data collection unit also collects tourist health data from a health app and analyzes it with the generation AI. For example, it proposes a sightseeing plan based on the tourist's health condition based on the tourist's exercise habits and sleep patterns. The tourist data collection unit also collects tourist health data and analyzes it with the generation AI to propose a sightseeing plan based on the tourist's health condition. For example, it proposes reasonable sightseeing routes and activities based on the tourist's physical strength and health condition. This makes it possible to propose a sightseeing plan based on the tourist's health condition.

[0034] The data analysis unit can take into account the influence of seasons or weather when analyzing tourist data. For example, when analyzing tourist behavior data, the data analysis unit takes into account the popularity and visiting trends of tourist destinations by season. For example, the popularity of cherry blossom viewing spots in spring and beach resorts in summer can be reflected. The data analysis unit also collects weather data and analyzes it in combination with tourist behavior data. For example, the popularity of indoor facilities on rainy days can be taken into account and reflected in behavior predictions. The data analysis unit also develops a behavior prediction algorithm that takes into account the influence of seasons and weather, and analyzes tourist behavior data. For example, it can predict the visiting trends of tourist destinations depending on seasonal events and weather. This enables highly accurate behavior prediction that takes into account the influence of seasons and weather.

[0035] The data analysis unit can analyze tourists' past travel patterns and develop algorithms that predict the frequency and duration of travel. For example, the data analysis unit analyzes tourists' past travel history and develops algorithms that predict the frequency and duration of travel. For example, it predicts the next travel date for tourists who tend to travel at the same time every year. The data analysis unit also analyzes tourists' travel patterns and predicts the frequency and duration of travel. For example, it classifies tourists into those who prefer short trips and those who prefer long trips and predicts the travel patterns for each. The data analysis unit also develops algorithms that predict the frequency and duration of travel based on tourists' past travel data. For example, it analyzes past travel history and predicts the frequency and duration of the next trip. This makes it possible to predict the frequency and duration of tourists' travel and make more accurate suggestions.

[0036] When analyzing tourist data, the data analysis unit takes into account interactions with other tourists and can predict group behavior. The data analysis unit, for example, analyzes tourist behavior data and takes into account interactions with other tourists. For example, it identifies tourist spots and activities visited in groups and reflects this in behavior prediction. The data analysis unit also analyzes tourist social network data and understands interactions with other tourists. For example, it identifies tourist spots visited with friends or family and reflects this in behavior prediction. The data analysis unit also analyzes tourist behavior data and develops an algorithm for predicting group behavior. For example, it predicts the tendency for multiple tourists to visit the same tourist spot at the same time. This makes it possible to predict group behavior that takes into account interactions with other tourists.

[0037] When analyzing tourist data, the data analysis unit can compare the behavior patterns of tourists from different cultural regions and nationalities and make predictions that take cultural differences into account. The data analysis unit, for example, analyzes tourist behavior data and compares the behavior patterns of tourists from different cultural regions and nationalities. For example, it identifies differences in the behavior patterns of tourists from Asian regions and Western regions. The data analysis unit also analyzes the behavior data of tourists from different cultural regions and nationalities and makes behavior predictions that take cultural differences into account. For example, it predicts tourist destinations and activities preferred by tourists from a particular cultural region. The data analysis unit also analyzes tourist behavior data and develops behavior prediction algorithms that take cultural differences into account. For example, it makes predictions that reflect the preferences and behavior patterns of tourists from different cultural regions. This makes it possible to make behavior predictions that take cultural differences into account.

[0038] The proposal generation unit can generate customized sightseeing plans tailored to individual interests and concerns based on tourist preference data. The proposal generation unit, for example, analyzes tourist preference data and generates sightseeing plans tailored to individual interests and concerns. For example, it proposes historical tourist spots to tourists who love history, and natural parks to tourists who love nature. The proposal generation unit also generates customized sightseeing plans based on the tourist's past travel history. For example, it proposes new sightseeing plans based on tourist spots visited in the past and activities participated in. The proposal generation unit also generates sightseeing plans tailored to individual interests and concerns in real time based on the tourist preference data. For example, it proposes the optimal sightseeing plan based on the tourist's current interests and concerns. This makes it possible to provide sightseeing plans tailored to individual interests and concerns.

[0039] The proposal generation unit can suggest unvisited tourist destinations based on the tourist's past travel history, thereby providing a new experience. The proposal generation unit, for example, analyzes the tourist's past travel history and suggests unvisited tourist destinations. For example, it suggests tourist destinations similar to tourist destinations visited in the past, thereby providing a new experience. The proposal generation unit also develops an algorithm for suggesting unvisited tourist destinations based on the tourist's travel history. For example, it analyzes the characteristics of tourist destinations visited in the past by the tourist and suggests similar tourist destinations. The proposal generation unit also suggests unvisited tourist destinations in real time based on the tourist's past travel history. For example, it suggests unvisited tourist destinations related to tourist destinations the tourist is currently visiting. This makes it possible to provide a new experience to the tourist.

[0040] The proposal generation unit can generate proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences. The proposal generation unit, for example, analyzes the tourist's preference data and generates proposals based on the behavior of other tourists with the same preferences. For example, it proposes tourist spots and activities visited by tourists with the same hobbies and interests. The proposal generation unit also analyzes the behavior patterns of other tourists with the same preferences, based on the tourist's preference data. For example, it identifies tourist spots and activities preferred by tourists with the same preferences and reflects them in the proposals. The proposal generation unit also generates proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences in real time. For example, it proposes tourist spots and activities currently visited by tourists with the same preferences. This makes it possible to generate proposals based on the behavior of other tourists with the same preferences.

[0041] The proposal generation unit generates proposals according to different seasons and time periods based on tourist preference data, thereby drawing out the appeal of each season. The proposal generation unit, for example, analyzes tourist preference data and generates proposals according to different seasons and time periods. For example, it proposes cherry blossom viewing spots in spring and beach resorts in summer. The proposal generation unit also generates proposals that draw out the appeal of each season based on tourist preference data. For example, it proposes autumn foliage spots in autumn and ski resorts in winter. The proposal generation unit also develops proposal algorithms according to different seasons and time periods based on tourist preference data. For example, it reflects seasonal events and activities in the proposals. This makes it possible to make proposals that draw out the appeal of each season.

[0042] The feedback collection unit tracks the tourist's behavior in real time after providing the suggestion, and can immediately evaluate the effectiveness of the suggestion. For example, the feedback collection unit tracks the GPS data of the tourist's smartphone in real time after providing the suggestion, and evaluates the effectiveness of the suggestion. For example, it checks whether the tourist actually visited the suggested tourist spot. The feedback collection unit also analyzes the tourist's social media posts in real time after providing the suggestion, and evaluates the effectiveness of the suggestion. For example, it analyzes the content of posts about the suggested tourist spot and checks whether there are many positive responses. The feedback collection unit also tracks the tourist's behavior data in real time after providing the suggestion, and immediately evaluates the effectiveness of the suggestion. For example, it checks whether the tourist participated in the suggested activity. This allows the effectiveness of the suggestion to be evaluated in real time.

[0043] The feedback collection unit can analyze the feedback data and develop an algorithm that continuously improves the accuracy of the suggestions. For example, the feedback collection unit analyzes the feedback data collected after the suggestions are provided and develops an algorithm that improves the accuracy of the suggestions. For example, the content of the suggestions is improved based on the ratings and comments of tourists. The feedback collection unit also analyzes the feedback data and builds a system that continuously improves the accuracy of the suggestions. For example, the algorithm is adjusted based on the ratings of the suggested tourist destinations and activities. The feedback collection unit also develops an algorithm that improves the accuracy of the suggestions based on the feedback data collected after the suggestions are provided. For example, the behavioral data and emotional data of tourists are analyzed and the content of the suggestions is optimized. This enables the accuracy of the suggestions to be continuously improved.

[0044] After providing the suggestions, the feedback collection unit can analyze the tourists' social media posts and evaluate the effectiveness of the suggestions. For example, after providing the suggestions, the feedback collection unit analyzes the tourists' social media posts and evaluates the effectiveness of the suggestions. For example, it analyzes the content of posts about the proposed tourist destinations and checks whether there are many positive reactions. The feedback collection unit also collects the tourists' social media posts and evaluates the effectiveness of the suggestions. For example, it analyzes and evaluates posts about the proposed tourist destinations and activities. After providing the suggestions, the feedback collection unit also analyzes the tourists' social media posts in real time and immediately evaluates the effectiveness of the suggestions. For example, it analyzes and evaluates the content of posts about the proposed tourist destinations in real time. In this way, the effectiveness of the suggestions can be evaluated by analyzing the social media posts.

[0045] After providing the proposal, the feedback collection unit can analyze the tourist's purchase history and evaluate the economic effectiveness of the proposal. For example, after providing the proposal, the feedback collection unit analyzes the tourist's credit card or electronic money usage history and evaluates the economic effectiveness of the proposal. For example, it analyzes the shopping and dining history at the proposed tourist destination. The feedback collection unit also collects the tourist's purchase history and evaluates the economic effectiveness of the proposal. For example, it analyzes and evaluates spending at the proposed tourist destination or activity. Furthermore, after providing the proposal, the feedback collection unit analyzes the tourist's purchase history in real time and immediately evaluates the economic effectiveness of the proposal. For example, it analyzes and evaluates the purchase history at the proposed tourist destination in real time. In this way, the economic effectiveness of the proposal can be evaluated by analyzing the tourist's purchase history.

[0046] The optimization unit can propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities based on the feedback data. The optimization unit, for example, analyzes the feedback data and proposes specific improvement measures to optimize the operation of tourist destinations and tourist facilities. For example, it identifies areas for improvement at facilities based on tourists' ratings and comments. The optimization unit also proposes improvement measures to improve the efficiency of operations based on tourist feedback data. For example, it proposes specific measures to improve how to handle crowded times and the quality of service. The optimization unit also analyzes the feedback data and develops algorithms to optimize the operation of tourist destinations and tourist facilities. For example, it identifies areas for improvement in operations based on tourist behavioral data and emotional data. This makes it possible to propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities.

[0047] The optimization unit can optimize the promotion strategies of tourist destinations and tourist facilities based on tourist preference data. The optimization unit, for example, analyzes tourist preference data and optimizes the promotion strategies of tourist destinations and tourist facilities. For example, it conducts effective promotions for tourists with specific preferences. The optimization unit also develops an algorithm that optimizes the promotion strategies based on the tourist preference data. For example, it proposes advertisements and campaigns that match the tourist preferences. The optimization unit also analyzes the tourist preference data and optimizes the promotion strategies of tourist destinations and tourist facilities in real time. For example, it conducts promotions that match the tourists' current preferences and interests. This makes it possible to optimize the promotion strategies of tourist destinations and tourist facilities.

[0048] The optimization unit can develop new services and attractions for tourist destinations and facilities based on the feedback data. The optimization unit, for example, analyzes the feedback data and develops new services and attractions for tourist destinations and facilities. For example, it proposes new attractions based on tourists' ratings and comments. The optimization unit also develops an algorithm for developing new services and attractions based on tourist feedback data. For example, it proposes new services based on tourist preference and behavior data. The optimization unit also analyzes the feedback data and develops new services and attractions for tourist destinations and facilities in real time. For example, it proposes new attractions based on tourists' current preferences and interests. This enables the development of new services and attractions for tourist destinations and facilities.

[0049] The optimization unit can optimize the layout and design of tourist destinations and tourist facilities based on the feedback data. The optimization unit, for example, analyzes the feedback data and optimizes the layout and design of tourist destinations and tourist facilities. For example, it improves the layout of a facility based on tourists' ratings and comments. The optimization unit also develops an algorithm that optimizes the layout and design based on tourist feedback data. For example, it optimizes the layout of a facility based on tourist behavior data. The optimization unit also analyzes the feedback data and optimizes the layout and design of tourist destinations and tourist facilities in real time. For example, it proposes a layout based on tourists' current preferences and interests. This makes it possible to optimize the layout and design of tourist destinations and tourist facilities.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The tourist data collection unit can collect tourist health data and analyze it with the generation AI. For example, health data such as step counts and heart rate can be collected from tourist smartwatches or fitness trackers and analyzed with the generation AI. This makes it possible to propose sightseeing plans that suit the tourist's physical strength and health condition. The tourist data collection unit can also collect tourist health data from health apps and analyze it with the generation AI. For example, it can propose sightseeing plans that suit the tourist's health condition based on the tourist's exercise habits and sleep patterns. Furthermore, the tourist data collection unit can collect tourist health data and analyze it with the generation AI to propose sightseeing plans that suit the tourist's health condition. For example, it can propose reasonable sightseeing routes and activities based on the tourist's physical strength and health condition. This makes it possible to propose sightseeing plans that suit the tourist's health condition.

[0052] The tourist data collection unit can collect tourist purchasing history data and analyze it with the generation AI. For example, by collecting tourist credit card and electronic money usage history and analyzing it with the generation AI, it is possible to understand the relationship between purchasing behavior and tourist behavior. This makes it possible to analyze shopping and dining history at tourist destinations. The tourist data collection unit can also collect tourist online shopping history and analyze it with the generation AI. For example, it can analyze the history of souvenirs and goods purchased at tourist destinations. Furthermore, by collecting tourist purchasing history data and analyzing it with the generation AI, it is possible to clarify the relationship between purchasing behavior and tourist behavior. For example, it is possible to identify products and services that tourists who visit a particular tourist destination tend to purchase. This makes it possible to clarify the relationship between purchasing behavior and tourist behavior.

[0053] The tourist data collection unit can collect tourist movement data in real time and analyze it with the generation AI. For example, by collecting GPS data from tourists' smartphones in real time and analyzing it with the generation AI, it is possible to understand the tourist's current location and movement patterns. This makes it possible to identify the tourist attractions and travel routes that tourists are visiting. The tourist data collection unit can also collect public transportation usage data in real time and analyze it with the generation AI. For example, it can identify the bus and train routes that tourists are using. Furthermore, by collecting tourist movement data in real time and analyzing it with the generation AI, it is possible to instantly reflect tourist behavior patterns. For example, it can predict the tourist attractions that tourists are currently visiting and the tourist attractions that they plan to visit next. This makes it possible to instantly reflect the tourist's current behavior patterns.

[0054] The tourist data collection unit can collect data from tourists' social media posts or review sites and analyze it with the generation AI. For example, by collecting tourists' social media posts and analyzing them with the generation AI, it is possible to understand tourists' interests. This makes it possible to analyze the content of Instagram and Twitter posts and identify tourists' favorite tourist destinations and activities. The tourist data collection unit can also collect data from review sites and analyze it with the generation AI. For example, it can analyze reviews on TripAdvisor and Yelp to identify tourist destinations and services that tourists have given high ratings to. Furthermore, the tourist data collection unit can integrate data from social media and review sites and analyze it with the generation AI to gain a more detailed understanding of tourists' preferences and behavioral patterns. For example, it can compare the content of social media posts and reviews to identify consistent preferences. This makes it possible to understand tourists' detailed preferences and behavioral patterns.

[0055] The data analysis unit can take into account the influence of seasons or weather when analyzing tourist data. For example, when analyzing tourist behavior data, the popularity and visiting trends of tourist destinations by season can be taken into account. This can reflect the fact that cherry blossom viewing spots are popular in spring and beach resorts in summer. The data analysis unit can also collect weather data and analyze it in combination with tourist behavior data. For example, the popularity of indoor facilities on rainy days can be taken into account and reflected in behavior predictions. Furthermore, the data analysis unit can develop behavior prediction algorithms that take into account the influence of seasons and weather and analyze tourist behavior data. For example, it can predict the visiting trends of tourist destinations depending on seasonal events and weather. This enables highly accurate behavior predictions that take into account the influence of seasons and weather.

[0056] The data analysis unit can analyze tourists' past travel patterns and develop algorithms that predict the frequency and duration of travel. For example, it can analyze tourists' past travel history and develop algorithms that predict the frequency and duration of travel. This makes it possible to predict the next travel date for tourists who tend to travel at the same time every year. The data analysis unit can also analyze tourists' travel patterns and predict the frequency and duration of travel. For example, it can classify tourists who prefer short trips and tourists who prefer long trips and predict the travel patterns for each. Furthermore, the data analysis unit can develop algorithms that predict the frequency and duration of travel based on tourists' past travel data. For example, it can analyze past travel history and predict the frequency and duration of the next trip. This makes it possible to predict the frequency and duration of tourists' travel and make more accurate suggestions.

[0057] When analyzing tourist data, the data analysis unit can take into account interactions with other tourists and predict group behavior. For example, it can analyze tourist behavior data and consider interactions with other tourists. This makes it possible to identify tourist destinations and activities visited in groups and reflect this in behavior prediction. The data analysis unit can also analyze tourist social network data to understand interactions with other tourists. For example, it can identify tourist destinations visited with friends or family and reflect this in behavior prediction. Furthermore, the data analysis unit can analyze tourist behavior data and develop algorithms for predicting group behavior. For example, it can predict the tendency for multiple tourists to visit the same tourist destination at the same time. This makes it possible to predict group behavior taking into account interactions with other tourists.

[0058] When analyzing tourist data, the data analysis unit can compare the behavior patterns of tourists from different cultural regions and nationalities and make predictions that take cultural differences into account. For example, by analyzing tourist behavior data, it is possible to compare the behavior patterns of tourists from different cultural regions and nationalities. This makes it possible to understand the differences in the behavior patterns of tourists from Asian regions and Western regions. The data analysis unit can also analyze the behavior data of tourists from different cultural regions and nationalities and make behavior predictions that take cultural differences into account. For example, it can predict the tourist destinations and activities preferred by tourists from a particular cultural region. Furthermore, the data analysis unit can analyze tourist behavior data and develop behavior prediction algorithms that take cultural differences into account. For example, it can make predictions that reflect the preferences and behavior patterns of tourists from different cultural regions. This makes it possible to make behavior predictions that take cultural differences into account.

[0059] The proposal generation unit can generate customized sightseeing plans tailored to individual interests and concerns based on tourist preference data. For example, it can analyze tourist preference data and generate sightseeing plans tailored to individual interests and concerns. This makes it possible to suggest historical tourist spots to tourists who love history, and natural parks to tourists who love nature. The proposal generation unit can also generate customized sightseeing plans based on the tourist's past travel history. For example, it can suggest new sightseeing plans based on tourist spots visited in the past and activities participated in. Furthermore, the proposal generation unit can generate sightseeing plans tailored to individual interests and concerns in real time based on the tourist preference data. For example, it can suggest the optimal sightseeing plan based on the tourist's current interests and concerns. This makes it possible to provide sightseeing plans tailored to the tourist's individual interests and concerns.

[0060] The proposal generation unit can suggest unvisited tourist destinations based on the tourist's past travel history, thereby providing a new experience. For example, it can analyze the tourist's past travel history and suggest unvisited tourist destinations. This makes it possible to suggest tourist destinations similar to tourist destinations that the tourist has visited in the past, thereby providing a new experience. The proposal generation unit can also develop an algorithm for suggesting unvisited tourist destinations based on the tourist's travel history. For example, it can analyze the characteristics of tourist destinations that the tourist has visited in the past and suggest similar tourist destinations. Furthermore, the proposal generation unit can suggest unvisited tourist destinations in real time based on the tourist's past travel history. For example, it can suggest unvisited tourist destinations related to the tourist destination that the tourist is currently visiting. This makes it possible to provide a new experience for the tourist.

[0061] The proposal generation unit can generate proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences. For example, the proposal generation unit can analyze the tourist's preference data and generate proposals based on the behavior of other tourists with the same preferences. This makes it possible to suggest tourist spots and activities visited by tourists with the same hobbies and interests. The proposal generation unit can also analyze the behavior patterns of other tourists with the same preferences, based on the tourist's preference data. For example, it can identify tourist spots and activities preferred by tourists with the same preferences and reflect them in the proposals. Furthermore, the proposal generation unit can generate proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences in real time. For example, it can suggest tourist spots and activities currently visited by tourists with the same preferences. This makes it possible to make proposals based on the behavior of other tourists with the same preferences.

[0062] The proposal generation unit can generate proposals according to different seasons and time periods based on tourist preference data, thereby drawing out the appeal of each season. For example, it can analyze tourist preference data and generate proposals according to different seasons and time periods. This makes it possible to propose cherry blossom viewing spots in spring and beach resorts in summer. The proposal generation unit can also generate proposals that draw out the appeal of each season based on tourist preference data. For example, it can propose autumn foliage spots in autumn and ski resorts in winter. Furthermore, the proposal generation unit can develop proposal algorithms according to different seasons and time periods based on tourist preference data. For example, seasonal events and activities can be reflected in the proposals. This makes it possible to make proposals that draw out the appeal of each season.

[0063] The feedback collection unit can track the tourist's behavior in real time after providing the suggestion and immediately evaluate the effectiveness of the suggestion. For example, after providing the suggestion, the GPS data of the tourist's smartphone can be tracked in real time to evaluate the effectiveness of the suggestion. This makes it possible to confirm whether the tourist actually visited the suggested tourist spot. The feedback collection unit can also analyze the tourist's social media posts in real time after providing the suggestion and evaluate the effectiveness of the suggestion. For example, it can analyze the content of posts about the suggested tourist spot and check whether there are many positive responses. Furthermore, the feedback collection unit can track the tourist's behavior data in real time after providing the suggestion and immediately evaluate the effectiveness of the suggestion. For example, it can check whether the tourist participated in the suggested activity. This makes it possible to evaluate the effectiveness of the suggestion in real time.

[0064] The feedback collection unit can analyze the feedback data and develop an algorithm that continuously improves the accuracy of the suggestions. For example, it can analyze the feedback data collected after the suggestions are provided and develop an algorithm that continuously improves the accuracy of the suggestions. This makes it possible to improve the content of the suggestions based on the ratings and comments of tourists. The feedback collection unit can also analyze the feedback data and build a system that continuously improves the accuracy of the suggestions. For example, it can adjust the algorithm based on the ratings of the suggested tourist spots and activities. Furthermore, the feedback collection unit can develop an algorithm that continuously improves the accuracy of the suggestions based on the feedback data collected after the suggestions are provided. For example, it can analyze the behavioral data and emotional data of tourists and optimize the content of the suggestions. This makes it possible to continuously improve the accuracy of the suggestions.

[0065] After providing the suggestions, the feedback collection unit can analyze the tourists' social media posts and evaluate the effectiveness of the suggestions. For example, after providing the suggestions, the feedback collection unit can analyze the tourists' social media posts and evaluate the effectiveness of the suggestions. This makes it possible to analyze the content of posts about the suggested tourist destinations and check whether there are many positive reactions. The feedback collection unit can also collect the tourists' social media posts and evaluate the effectiveness of the suggestions. For example, it can analyze posts about the suggested tourist destinations and activities and make an evaluation. Furthermore, after providing the suggestions, the feedback collection unit can analyze the tourists' social media posts in real time and immediately evaluate the effectiveness of the suggestions. For example, it can analyze the content of posts about the suggested tourist destinations in real time and make an evaluation. This makes it possible to analyze the social media posts and evaluate the effectiveness of the suggestions.

[0066] After providing the suggestions, the feedback collection unit can analyze the tourist's purchase history and evaluate the economic effectiveness of the suggestions. For example, after providing the suggestions, the tourist's credit card or electronic money usage history can be analyzed to evaluate the economic effectiveness of the suggestions. This makes it possible to analyze the shopping and dining history at the suggested tourist destinations. The feedback collection unit can also collect the tourist's purchase history and evaluate the economic effectiveness of the suggestions. For example, expenditures at the suggested tourist destinations or activities can be analyzed and evaluated. Furthermore, after providing the suggestions, the feedback collection unit can analyze the tourist's purchase history in real time and immediately evaluate the economic effectiveness of the suggestions. For example, the purchase history at the suggested tourist destinations can be analyzed in real time and evaluated. This makes it possible to analyze the tourist's purchase history and evaluate the economic effectiveness of the suggestions.

[0067] The optimization unit can propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities based on the feedback data. For example, the optimization unit can analyze the feedback data and propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities. This makes it possible to identify areas for improvement at the facilities based on tourists' ratings and comments. The optimization unit can also propose improvement measures to improve the efficiency of operations based on tourist feedback data. For example, it can propose specific measures to improve how to handle crowded times and the quality of service. Furthermore, the optimization unit can analyze the feedback data and develop algorithms to optimize the operation of tourist destinations and tourist facilities. For example, it can identify areas for improvement in operations based on tourist behavior data and emotion data. This makes it possible to propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities.

[0068] The optimization unit can optimize the promotion strategies of tourist destinations and tourist facilities based on tourist preference data. For example, it can analyze tourist preference data and optimize the promotion strategies of tourist destinations and tourist facilities. This makes it possible to carry out effective promotions for tourists with specific preferences. The optimization unit can also develop an algorithm that optimizes the promotion strategies based on the tourist preference data. For example, it can propose advertisements and campaigns that match the tourist preferences. Furthermore, the optimization unit can analyze the tourist preference data and optimize the promotion strategies of tourist destinations and tourist facilities in real time. For example, it can carry out promotions that match the tourists' current preferences and interests. This makes it possible to optimize the promotion strategies of tourist destinations and tourist facilities.

[0069] The optimization unit can develop new services and attractions for tourist destinations and facilities based on the feedback data. For example, the optimization unit can analyze the feedback data to develop new services and attractions for tourist destinations and facilities. This makes it possible to propose new attractions based on tourists' ratings and comments. The optimization unit can also develop algorithms for developing new services and attractions based on tourist feedback data. For example, new services can be proposed based on tourist preference and behavior data. Furthermore, the optimization unit can analyze the feedback data to develop new services and attractions for tourist destinations and facilities in real time. For example, new attractions can be proposed based on tourists' current preferences and interests. This makes it possible to develop new services and attractions for tourist destinations and facilities.

[0070] The optimization unit can optimize the layout and design of tourist destinations and tourist facilities based on the feedback data. For example, the optimization unit can analyze the feedback data and optimize the layout and design of tourist destinations and tourist facilities. This makes it possible to improve the layout of the facilities based on tourists' ratings and comments. The optimization unit can also develop an algorithm that optimizes the layout and design based on tourist feedback data. For example, the layout of the facilities can be optimized based on tourist behavior data. Furthermore, the optimization unit can analyze the feedback data and optimize the layout and design of tourist destinations and tourist facilities in real time. For example, it can propose a layout based on the current preferences and interests of tourists. This makes it possible to optimize the layout and design of tourist destinations and tourist facilities.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The tourist data collection unit collects tourist data. For example, it collects tourist past behavior data and preference data. The tourist data collection unit can also collect data from tourists' social media posts and review sites. For example, it collects the history of tourist spots visited by tourists and the services they have used. Step 2: The data analysis unit analyzes the tourist data collected by the tourist data collection unit. For example, the generation AI analyzes tourist behavior patterns and preferences. Step 3: The behavior prediction unit predicts tourist behavior based on the data analyzed by the data analysis unit. For example, the generation AI predicts the tourist's likely next visit destination and the services they are likely to use. Step 4: The proposal generation unit generates proposals based on the behavior predicted by the behavior prediction unit. For example, the generation AI suggests tourist spots that tourists are likely to visit next, as well as accommodations, restaurants, and events that they are likely to use. Step 5: The feedback collection unit provides the suggestions generated by the suggestion generation unit to tourist destinations and tourist facilities and collects the results as feedback, for example, by checking whether tourists actually visited the suggested tourist destinations and whether they used the suggested services. Step 6: The optimization unit analyzes the feedback data collected by the feedback collection unit and optimizes the operation of tourist destinations and facilities. For example, the generation AI can provide services tailored to tourist preferences and strengthen the promotion of popular tourist destinations.

[0073] (Example 2) Tourist Predictor according to an embodiment of the present invention is a system that predicts the behavior and preferences of tourists and makes optimal suggestions to tourist destinations and facilities, thereby enabling Tourist Predictor to provide more personalized services to tourists.

[0074] A tourist predictor according to an embodiment includes a tourist data collection unit, a data analysis unit, a behavior prediction unit, a proposal generation unit, a feedback collection unit, and an optimization unit. The tourist data collection unit collects tourist data. For example, it collects tourist behavior data and preference data from tourists. The tourist data collection unit can also collect data from tourists' social media posts and review sites. For example, it collects the history of tourist destinations visited and services used by tourists. The data analysis unit analyzes the tourist data collected by the tourist data collection unit. For example, the generation AI analyzes tourist behavior patterns and preferences. The behavior prediction unit predicts tourist behavior based on the data analyzed by the data analysis unit. For example, the generation AI predicts tourist destinations that tourists are likely to visit next and services that tourists are likely to use. The proposal generation unit generates proposals based on the behavior predicted by the behavior prediction unit. For example, the generation AI suggests tourist destinations that tourists are likely to visit next and accommodations, restaurants, events, etc. that tourists are likely to use. The feedback collection unit provides the proposals generated by the proposal generation unit to tourist destinations and tourist facilities and collects the results as feedback. For example, it checks whether tourists actually visited the proposed tourist destinations and used the proposed services. The optimization unit analyzes the feedback data collected by the feedback collection unit and optimizes the operation of tourist destinations and tourist facilities. For example, the generation AI provides services tailored to tourist preferences and strengthens the promotion of popular tourist destinations. This allows the tourist predictor according to the embodiment to provide more personalized services to tourists. For example, by proposing sightseeing plans tailored to tourist preferences, tourist satisfaction can be improved. It can also improve the efficiency of the operation of tourist destinations and tourist facilities and increase revenue.

[0075] The tourist data collection unit can collect data from tourists' social media posts or review sites and analyze it with the generation AI. For example, the tourist data collection unit collects tourists' social media posts and analyzes them with the generation AI to understand tourists' interests. For example, it analyzes the content of posts on Instagram or Twitter to identify tourists' favorite tourist destinations and activities. The tourist data collection unit also collects data from review sites and analyzes it with the generation AI to understand tourists' ratings and feedback. For example, it analyzes reviews on TripAdvisor or Yelp to identify tourist destinations and services that tourists have given high ratings to. The tourist data collection unit also integrates data from social media and review sites and analyzes it with the generation AI to understand tourists' preferences and behavioral patterns in more detail. For example, it compares the content of social media posts and reviews to identify consistent preferences. This makes it possible to understand tourists' detailed preferences and behavioral patterns.

[0076] The tourist data collection unit can collect tourist movement data in real time and analyze it with the generation AI. For example, the tourist data collection unit collects GPS data from tourists' smartphones in real time and analyzes it with the generation AI to understand the tourists' current locations and movement patterns. For example, it identifies the tourist attractions and travel routes that tourists are visiting. The tourist data collection unit also collects public transportation usage data in real time and analyzes it with the generation AI to understand the tourists' means of transportation and travel time. For example, it identifies the bus and train routes used by tourists. The tourist data collection unit also collects tourist movement data in real time and analyzes it with the generation AI to instantly reflect the tourists' behavior patterns. For example, it predicts the tourist attractions that tourists are currently visiting and the tourist attractions that they plan to visit next. This makes it possible to instantly reflect the tourists' current behavior patterns.

[0077] The tourist data collection unit can use the emotion estimation function to analyze emotions at places visited by tourists and identify tourist destinations that evoke positive emotions. The tourist data collection unit, for example, analyzes emotions in tourists' social media posts and reviews to identify tourist destinations that express many positive emotions. For example, it analyzes posts in which tourists express positive emotions such as "fun" and "beautiful." The tourist data collection unit also analyzes tourists' facial expressions to estimate emotions at places visited. For example, it analyzes facial expressions in photos taken at tourist destinations to identify tourist destinations with many smiling and happy expressions. The tourist data collection unit also analyzes tourists' voice data to estimate emotions at places visited. For example, it analyzes the tone and intonation of voices recorded at tourist destinations to identify tourist destinations that express positive emotions. This makes it possible to identify tourist destinations based on tourists' emotions.

[0078] The tourist data collection unit can collect tourist purchasing history data and analyze it with the generation AI. For example, the tourist data collection unit collects tourist credit card and electronic money usage history and analyzes it with the generation AI to understand the relationship between purchasing behavior and tourist behavior. For example, it analyzes shopping and dining history at tourist destinations. The tourist data collection unit also collects tourist online shopping history and analyzes it with the generation AI to understand the relationship between purchasing behavior and tourist behavior. For example, it analyzes the history of souvenirs and goods purchased at tourist destinations. The tourist data collection unit also collects tourist purchasing history data and analyzes it with the generation AI to clarify the relationship between purchasing behavior and tourist behavior. For example, it identifies products and services that tourists who visit a particular tourist destination tend to purchase. This makes it possible to clarify the relationship between purchasing behavior and tourist behavior.

[0079] The tourist data collection unit can collect tourist health data and analyze it with the generation AI. For example, the tourist data collection unit collects health data such as the number of steps and heart rate from the tourist's smartwatch or fitness tracker and analyzes it with the generation AI. For example, it proposes a sightseeing plan based on the tourist's physical strength and health condition. The tourist data collection unit also collects tourist health data from a health app and analyzes it with the generation AI. For example, it proposes a sightseeing plan based on the tourist's health condition based on the tourist's exercise habits and sleep patterns. The tourist data collection unit also collects tourist health data and analyzes it with the generation AI to propose a sightseeing plan based on the tourist's health condition. For example, it proposes reasonable sightseeing routes and activities based on the tourist's physical strength and health condition. This makes it possible to propose a sightseeing plan based on the tourist's health condition.

[0080] The tourist data collection unit uses the emotion estimation function to collect emotions of tourists at places visited in real time and evaluate tourist destinations based on their emotions. The tourist data collection unit, for example, analyzes tourists' facial expressions in real time to estimate their emotions at visited places. For example, it captures facial expressions at tourist destinations with a camera and analyzes smiles and happy expressions in real time. The tourist data collection unit also analyzes tourists' voice data in real time to estimate their emotions at visited places. For example, it collects conversations and voices at tourist destinations with a microphone and analyzes positive emotions in real time. The tourist data collection unit also analyzes tourists' social media posts in real time to estimate their emotions at visited places. For example, it analyzes the content of posts at tourist destinations in real time to identify posts expressing positive emotions. This makes it possible to evaluate tourist destinations based on tourists' emotions.

[0081] The data analysis unit can take into account the influence of seasons or weather when analyzing tourist data. For example, when analyzing tourist behavior data, the data analysis unit takes into account the popularity and visiting trends of tourist destinations by season. For example, the popularity of cherry blossom viewing spots in spring and beach resorts in summer can be reflected. The data analysis unit also collects weather data and analyzes it in combination with tourist behavior data. For example, the popularity of indoor facilities on rainy days can be taken into account and reflected in behavior predictions. The data analysis unit also develops a behavior prediction algorithm that takes into account the influence of seasons and weather, and analyzes tourist behavior data. For example, it can predict the visiting trends of tourist destinations depending on seasonal events and weather. This enables highly accurate behavior prediction that takes into account the influence of seasons and weather.

[0082] The data analysis unit can analyze tourists' past travel patterns and develop algorithms that predict the frequency and duration of travel. For example, the data analysis unit analyzes tourists' past travel history and develops algorithms that predict the frequency and duration of travel. For example, it predicts the next travel date for tourists who tend to travel at the same time every year. The data analysis unit also analyzes tourists' travel patterns and predicts the frequency and duration of travel. For example, it classifies tourists into those who prefer short trips and those who prefer long trips and predicts the travel patterns for each. The data analysis unit also develops algorithms that predict the frequency and duration of travel based on tourists' past travel data. For example, it analyzes past travel history and predicts the frequency and duration of the next trip. This makes it possible to predict the frequency and duration of tourists' travel and make more accurate suggestions.

[0083] The data analysis unit uses the emotion estimation function to analyze changes in tourists' emotions and predict behavior based on those emotions. The data analysis unit, for example, analyzes emotions in tourists' social media posts and reviews to understand changes in emotions. For example, it identifies tourist destinations where positive emotions are increasing and reflects this in behavior prediction. The data analysis unit also analyzes tourists' facial expressions to understand changes in emotions. For example, it analyzes facial expressions at tourist destinations to identify tourist destinations where smiling and happy expressions are common and reflects this in behavior prediction. The data analysis unit also analyzes tourists' voice data to understand changes in emotions. For example, it analyzes conversations and voices at tourist destinations to identify tourist destinations where positive emotions are expressed and reflects this in behavior prediction. This makes it possible to predict behavior based on tourists' emotions.

[0084] When analyzing tourist data, the data analysis unit takes into account interactions with other tourists and can predict group behavior. The data analysis unit, for example, analyzes tourist behavior data and takes into account interactions with other tourists. For example, it identifies tourist spots and activities visited in groups and reflects this in behavior prediction. The data analysis unit also analyzes tourist social network data and understands interactions with other tourists. For example, it identifies tourist spots visited with friends or family and reflects this in behavior prediction. The data analysis unit also analyzes tourist behavior data and develops an algorithm for predicting group behavior. For example, it predicts the tendency for multiple tourists to visit the same tourist spot at the same time. This makes it possible to predict group behavior that takes into account interactions with other tourists.

[0085] When analyzing tourist data, the data analysis unit can compare the behavior patterns of tourists from different cultural regions and nationalities and make predictions that take cultural differences into account. The data analysis unit, for example, analyzes tourist behavior data and compares the behavior patterns of tourists from different cultural regions and nationalities. For example, it identifies differences in the behavior patterns of tourists from Asian regions and Western regions. The data analysis unit also analyzes the behavior data of tourists from different cultural regions and nationalities and makes behavior predictions that take cultural differences into account. For example, it predicts tourist destinations and activities preferred by tourists from a particular cultural region. The data analysis unit also analyzes tourist behavior data and develops behavior prediction algorithms that take cultural differences into account. For example, it makes predictions that reflect the preferences and behavior patterns of tourists from different cultural regions. This makes it possible to make behavior predictions that take cultural differences into account.

[0086] The data analysis unit uses the emotion estimation function to predict behavior based on the emotions of tourists and can propose sightseeing plans that will provide high emotional satisfaction. The data analysis unit, for example, uses the emotion estimation function to predict behavior based on the emotions of tourists. For example, it prioritizes suggesting tourist destinations that have strong positive emotions. The data analysis unit also analyzes tourist emotion data and proposes sightseeing plans that will provide high emotional satisfaction. For example, it makes suggestions based on tourist destinations and activities that tourists have felt positive about in the past. The data analysis unit also uses the emotion estimation function to develop a behavior prediction algorithm based on tourist emotions. For example, it analyzes tourist emotion data in real time and proposes sightseeing plans that will provide high emotional satisfaction. This makes it possible to predict behavior and propose sightseeing plans based on tourist emotions.

[0087] The proposal generation unit can generate customized sightseeing plans tailored to individual interests and concerns based on tourist preference data. The proposal generation unit, for example, analyzes tourist preference data and generates sightseeing plans tailored to individual interests and concerns. For example, it proposes historical tourist spots to tourists who love history, and natural parks to tourists who love nature. The proposal generation unit also generates customized sightseeing plans based on the tourist's past travel history. For example, it proposes new sightseeing plans based on tourist spots visited in the past and activities participated in. The proposal generation unit also generates sightseeing plans tailored to individual interests and concerns in real time based on the tourist preference data. For example, it proposes the optimal sightseeing plan based on the tourist's current interests and concerns. This makes it possible to provide sightseeing plans tailored to individual interests and concerns.

[0088] The proposal generation unit can suggest unvisited tourist destinations based on the tourist's past travel history, thereby providing a new experience. The proposal generation unit, for example, analyzes the tourist's past travel history and suggests unvisited tourist destinations. For example, it suggests tourist destinations similar to tourist destinations visited in the past, thereby providing a new experience. The proposal generation unit also develops an algorithm for suggesting unvisited tourist destinations based on the tourist's travel history. For example, it analyzes the characteristics of tourist destinations visited in the past by the tourist and suggests similar tourist destinations. The proposal generation unit also suggests unvisited tourist destinations in real time based on the tourist's past travel history. For example, it suggests unvisited tourist destinations related to tourist destinations the tourist is currently visiting. This makes it possible to provide a new experience to the tourist.

[0089] The proposal generation unit uses the emotion estimation function to generate proposals based on the emotions of tourists, and can provide sightseeing plans that are emotionally satisfying. The proposal generation unit, for example, uses the emotion estimation function to generate proposals based on the emotions of tourists. For example, it prioritizes suggesting tourist destinations and activities that evoke strong positive emotions. The proposal generation unit also analyzes tourist emotion data to provide sightseeing plans that are emotionally satisfying. For example, it makes suggestions based on tourist destinations and activities that tourists have felt positive about in the past. The proposal generation unit also uses the emotion estimation function to develop a proposal algorithm based on the emotions of tourists. For example, it analyzes tourist emotion data in real time to provide sightseeing plans that are emotionally satisfying. In this way, by making suggestions based on the emotions of tourists, it is possible to provide sightseeing plans that are emotionally satisfying.

[0090] The proposal generation unit can generate proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences. The proposal generation unit, for example, analyzes the tourist's preference data and generates proposals based on the behavior of other tourists with the same preferences. For example, it proposes tourist spots and activities visited by tourists with the same hobbies and interests. The proposal generation unit also analyzes the behavior patterns of other tourists with the same preferences, based on the tourist's preference data. For example, it identifies tourist spots and activities preferred by tourists with the same preferences and reflects them in the proposals. The proposal generation unit also generates proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences in real time. For example, it proposes tourist spots and activities currently visited by tourists with the same preferences. This makes it possible to generate proposals based on the behavior of other tourists with the same preferences.

[0091] The proposal generation unit generates proposals according to different seasons and time periods based on tourist preference data, thereby drawing out the appeal of each season. The proposal generation unit, for example, analyzes tourist preference data and generates proposals according to different seasons and time periods. For example, it proposes cherry blossom viewing spots in spring and beach resorts in summer. The proposal generation unit also generates proposals that draw out the appeal of each season based on tourist preference data. For example, it proposes autumn foliage spots in autumn and ski resorts in winter. The proposal generation unit also develops proposal algorithms according to different seasons and time periods based on tourist preference data. For example, it reflects seasonal events and activities in the proposals. This makes it possible to make proposals that draw out the appeal of each season.

[0092] The proposal generation unit uses the emotion estimation function to generate proposals based on the tourist's emotions in real time, thereby optimizing the local experience. The proposal generation unit, for example, uses the emotion estimation function to generate proposals based on the tourist's emotions in real time. For example, it proposes optimal tourist spots and activities based on the tourist's current emotional state. The proposal generation unit also analyzes the tourist's emotion data in real time and makes proposals based on the emotions. For example, it proposes a tour plan that will make the tourist feel positive emotions at the local site. The proposal generation unit also uses the emotion estimation function to develop a proposal algorithm based on the tourist's emotions. For example, it analyzes the tourist's emotion data in real time and makes proposals that optimize the local experience. In this way, the local experience can be optimized by making proposals based on the tourist's emotions in real time.

[0093] The feedback collection unit tracks the tourist's behavior in real time after providing the suggestion, and can immediately evaluate the effectiveness of the suggestion. For example, the feedback collection unit tracks the GPS data of the tourist's smartphone in real time after providing the suggestion, and evaluates the effectiveness of the suggestion. For example, it checks whether the tourist actually visited the suggested tourist spot. The feedback collection unit also analyzes the tourist's social media posts in real time after providing the suggestion, and evaluates the effectiveness of the suggestion. For example, it analyzes the content of posts about the suggested tourist spot and checks whether there are many positive responses. The feedback collection unit also tracks the tourist's behavior data in real time after providing the suggestion, and immediately evaluates the effectiveness of the suggestion. For example, it checks whether the tourist participated in the suggested activity. This allows the effectiveness of the suggestion to be evaluated in real time.

[0094] The feedback collection unit can analyze the feedback data and develop an algorithm that continuously improves the accuracy of the suggestions. For example, the feedback collection unit analyzes the feedback data collected after the suggestions are provided and develops an algorithm that improves the accuracy of the suggestions. For example, the content of the suggestions is improved based on the ratings and comments of tourists. The feedback collection unit also analyzes the feedback data and builds a system that continuously improves the accuracy of the suggestions. For example, the algorithm is adjusted based on the ratings of the suggested tourist destinations and activities. The feedback collection unit also develops an algorithm that improves the accuracy of the suggestions based on the feedback data collected after the suggestions are provided. For example, the behavioral data and emotional data of tourists are analyzed and the content of the suggestions is optimized. This enables the accuracy of the suggestions to be continuously improved.

[0095] The feedback collection unit can use the emotion estimation function to collect tourists' emotional responses and analyze feedback based on their emotions. The feedback collection unit, for example, uses the emotion estimation function to collect tourists' emotional responses. For example, it analyzes facial expressions and voices at tourist spots to check whether there are many positive emotions. The feedback collection unit also analyzes tourists' emotional data and provides feedback based on their emotions. For example, it identifies tourist spots and activities where tourists have positive emotions and improves the content of recommendations. The feedback collection unit also uses the emotion estimation function to collect tourists' emotional responses in real time and analyze the feedback based on their emotions. For example, it optimizes the content of recommendations based on the tourist's emotional data. This makes it possible to collect and analyze feedback based on tourists' emotions.

[0096] After providing the suggestions, the feedback collection unit can analyze the tourists' social media posts and evaluate the effectiveness of the suggestions. For example, after providing the suggestions, the feedback collection unit analyzes the tourists' social media posts and evaluates the effectiveness of the suggestions. For example, it analyzes the content of posts about the proposed tourist destinations and checks whether there are many positive reactions. The feedback collection unit also collects the tourists' social media posts and evaluates the effectiveness of the suggestions. For example, it analyzes and evaluates posts about the proposed tourist destinations and activities. After providing the suggestions, the feedback collection unit also analyzes the tourists' social media posts in real time and immediately evaluates the effectiveness of the suggestions. For example, it analyzes and evaluates the content of posts about the proposed tourist destinations in real time. In this way, the effectiveness of the suggestions can be evaluated by analyzing the social media posts.

[0097] After providing the proposal, the feedback collection unit can analyze the tourist's purchase history and evaluate the economic effectiveness of the proposal. For example, after providing the proposal, the feedback collection unit analyzes the tourist's credit card or electronic money usage history and evaluates the economic effectiveness of the proposal. For example, it analyzes the shopping and dining history at the proposed tourist destination. The feedback collection unit also collects the tourist's purchase history and evaluates the economic effectiveness of the proposal. For example, it analyzes and evaluates spending at the proposed tourist destination or activity. Furthermore, after providing the proposal, the feedback collection unit analyzes the tourist's purchase history in real time and immediately evaluates the economic effectiveness of the proposal. For example, it analyzes and evaluates the purchase history at the proposed tourist destination in real time. In this way, the economic effectiveness of the proposal can be evaluated by analyzing the tourist's purchase history.

[0098] The feedback collection unit uses the emotion estimation function to collect tourists' emotional responses in real time, allowing the effectiveness of the proposal to be evaluated immediately. The feedback collection unit, for example, uses the emotion estimation function to collect tourists' emotional responses in real time. For example, it analyzes facial expressions and voices at tourist spots to check whether there are many positive emotions. The feedback collection unit also analyzes tourists' emotional data in real time to evaluate the effectiveness of the proposal immediately. For example, it checks whether tourists felt positive emotions at the recommended tourist spots. The feedback collection unit also uses the emotion estimation function to collect tourists' emotional responses in real time to evaluate the effectiveness of the proposal. For example, it optimizes the content of the proposal based on the tourists' emotional data. This allows the emotional responses of tourists to be collected in real time, allowing the effectiveness of the proposal to be evaluated immediately.

[0099] The optimization unit can propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities based on the feedback data. The optimization unit, for example, analyzes the feedback data and proposes specific improvement measures to optimize the operation of tourist destinations and tourist facilities. For example, it identifies areas for improvement at facilities based on tourists' ratings and comments. The optimization unit also proposes improvement measures to improve the efficiency of operations based on tourist feedback data. For example, it proposes specific measures to improve how to handle crowded times and the quality of service. The optimization unit also analyzes the feedback data and develops algorithms to optimize the operation of tourist destinations and tourist facilities. For example, it identifies areas for improvement in operations based on tourist behavioral data and emotional data. This makes it possible to propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities.

[0100] The optimization unit can optimize the promotion strategies of tourist destinations and tourist facilities based on tourist preference data. The optimization unit, for example, analyzes tourist preference data and optimizes the promotion strategies of tourist destinations and tourist facilities. For example, it conducts effective promotions for tourists with specific preferences. The optimization unit also develops an algorithm that optimizes the promotion strategies based on the tourist preference data. For example, it proposes advertisements and campaigns that match the tourist preferences. The optimization unit also analyzes the tourist preference data and optimizes the promotion strategies of tourist destinations and tourist facilities in real time. For example, it conducts promotions that match the tourists' current preferences and interests. This makes it possible to optimize the promotion strategies of tourist destinations and tourist facilities.

[0101] The optimization unit can use the emotion estimation function to propose operational improvement measures based on tourist emotions. The optimization unit, for example, uses the emotion estimation function to propose operational improvement measures based on tourist emotions. For example, it identifies areas for improvement in services and facilities that will evoke positive emotions from tourists. The optimization unit also analyzes tourist emotion data and proposes operational improvement measures based on emotions. For example, it identifies areas for improvement in operations based on tourist destinations and activities that evoke positive emotions from tourists. The optimization unit also uses the emotion estimation function to develop an operational improvement algorithm based on tourist emotions. For example, it analyzes tourist emotion data in real time and identifies areas for improvement in operations. This makes it possible to propose operational improvement measures based on tourist emotions.

[0102] The optimization unit can develop new services and attractions for tourist destinations and facilities based on the feedback data. The optimization unit, for example, analyzes the feedback data and develops new services and attractions for tourist destinations and facilities. For example, it proposes new attractions based on tourists' ratings and comments. The optimization unit also develops an algorithm for developing new services and attractions based on tourist feedback data. For example, it proposes new services based on tourist preference and behavior data. The optimization unit also analyzes the feedback data and develops new services and attractions for tourist destinations and facilities in real time. For example, it proposes new attractions based on tourists' current preferences and interests. This enables the development of new services and attractions for tourist destinations and facilities.

[0103] The optimization unit can optimize the layout and design of tourist destinations and tourist facilities based on the feedback data. The optimization unit, for example, analyzes the feedback data and optimizes the layout and design of tourist destinations and tourist facilities. For example, it improves the layout of a facility based on tourists' ratings and comments. The optimization unit also develops an algorithm that optimizes the layout and design based on tourist feedback data. For example, it optimizes the layout of a facility based on tourist behavior data. The optimization unit also analyzes the feedback data and optimizes the layout and design of tourist destinations and tourist facilities in real time. For example, it proposes a layout based on tourists' current preferences and interests. This makes it possible to optimize the layout and design of tourist destinations and tourist facilities.

[0104] The optimization unit can use the emotion estimation function to propose operational improvement measures based on tourist emotions in real time. The optimization unit, for example, uses the emotion estimation function to propose operational improvement measures based on tourist emotions in real time. For example, it proposes optimal operational improvement measures based on the tourist's current emotional state. The optimization unit also analyzes tourist emotion data in real time and proposes operational improvement measures based on the emotions. For example, it identifies improvements to services and facilities that will evoke positive emotions in real time. The optimization unit also uses the emotion estimation function to develop an operational improvement algorithm based on tourist emotions. For example, it analyzes tourist emotion data in real time and identifies improvements to operations in real time. This makes it possible to propose operational improvement measures based on tourist emotions in real time.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The tourist data collection unit can collect tourist health data and analyze it with the generation AI. For example, health data such as step counts and heart rate can be collected from tourist smartwatches or fitness trackers and analyzed with the generation AI. This makes it possible to propose sightseeing plans that suit the tourist's physical strength and health condition. The tourist data collection unit can also collect tourist health data from health apps and analyze it with the generation AI. For example, it can propose sightseeing plans that suit the tourist's health condition based on the tourist's exercise habits and sleep patterns. Furthermore, the tourist data collection unit can collect tourist health data and analyze it with the generation AI to propose sightseeing plans that suit the tourist's health condition. For example, it can propose reasonable sightseeing routes and activities based on the tourist's physical strength and health condition. This makes it possible to propose sightseeing plans that suit the tourist's health condition.

[0107] The tourist data collection unit can collect tourist purchasing history data and analyze it with the generation AI. For example, by collecting tourist credit card and electronic money usage history and analyzing it with the generation AI, it is possible to understand the relationship between purchasing behavior and tourist behavior. This makes it possible to analyze shopping and dining history at tourist destinations. The tourist data collection unit can also collect tourist online shopping history and analyze it with the generation AI. For example, it can analyze the history of souvenirs and goods purchased at tourist destinations. Furthermore, by collecting tourist purchasing history data and analyzing it with the generation AI, it is possible to clarify the relationship between purchasing behavior and tourist behavior. For example, it is possible to identify products and services that tourists who visit a particular tourist destination tend to purchase. This makes it possible to clarify the relationship between purchasing behavior and tourist behavior.

[0108] The tourist data collection unit can collect tourist movement data in real time and analyze it with the generation AI. For example, by collecting GPS data from tourists' smartphones in real time and analyzing it with the generation AI, it is possible to understand the tourist's current location and movement patterns. This makes it possible to identify the tourist attractions and travel routes that tourists are visiting. The tourist data collection unit can also collect public transportation usage data in real time and analyze it with the generation AI. For example, it can identify the bus and train routes that tourists are using. Furthermore, by collecting tourist movement data in real time and analyzing it with the generation AI, it is possible to instantly reflect tourist behavior patterns. For example, it can predict the tourist attractions that tourists are currently visiting and the tourist attractions that they plan to visit next. This makes it possible to instantly reflect the tourist's current behavior patterns.

[0109] The tourist data collection unit can use the emotion estimation function to analyze the emotions of tourists at places they visit and identify tourist destinations that evoke positive emotions. For example, by analyzing the emotions in tourists' social media posts and reviews, it is possible to identify tourist destinations that express many positive emotions. This makes it possible to analyze posts in which tourists express positive emotions such as "fun" and "beautiful." The tourist data collection unit can also analyze tourists' facial expressions to estimate their emotions at places they visit. For example, by analyzing the facial expressions in photos taken at tourist destinations, it is possible to identify tourist destinations that show many smiling and happy expressions. Furthermore, the tourist data collection unit can analyze tourists' voice data to estimate their emotions at places they visit. For example, by analyzing the tone and intonation of voices recorded at tourist destinations, it is possible to identify tourist destinations that express positive emotions. This makes it possible to identify tourist destinations based on tourists' emotions.

[0110] The tourist data collection unit can collect data from tourists' social media posts or review sites and analyze it with the generation AI. For example, by collecting tourists' social media posts and analyzing them with the generation AI, it is possible to understand tourists' interests. This makes it possible to analyze the content of Instagram and Twitter posts and identify tourists' favorite tourist destinations and activities. The tourist data collection unit can also collect data from review sites and analyze it with the generation AI. For example, it can analyze reviews on TripAdvisor and Yelp to identify tourist destinations and services that tourists have given high ratings to. Furthermore, the tourist data collection unit can integrate data from social media and review sites and analyze it with the generation AI to gain a more detailed understanding of tourists' preferences and behavioral patterns. For example, it can compare the content of social media posts and reviews to identify consistent preferences. This makes it possible to understand tourists' detailed preferences and behavioral patterns.

[0111] The tourist data collection unit can use the emotion estimation function to collect the emotions of tourists at places they visit in real time and evaluate tourist destinations based on their emotions. For example, it can analyze the facial expressions of tourists in real time to estimate their emotions at places they visit. This makes it possible to capture their expressions at tourist destinations with a camera and analyze smiles and happy expressions in real time. The tourist data collection unit can also analyze the audio data of tourists in real time to estimate their emotions at places they visit. For example, it can collect conversations and audio at tourist destinations with a microphone and analyze positive emotions in real time. Furthermore, the tourist data collection unit can analyze the social media posts of tourists in real time to estimate their emotions at places they visit. For example, it can analyze the content of posts at tourist destinations in real time and identify posts expressing positive emotions. This makes it possible to evaluate tourist destinations based on the emotions of tourists.

[0112] The data analysis unit can take into account the influence of seasons or weather when analyzing tourist data. For example, when analyzing tourist behavior data, the popularity and visiting trends of tourist destinations by season can be taken into account. This can reflect the fact that cherry blossom viewing spots are popular in spring and beach resorts in summer. The data analysis unit can also collect weather data and analyze it in combination with tourist behavior data. For example, the popularity of indoor facilities on rainy days can be taken into account and reflected in behavior predictions. Furthermore, the data analysis unit can develop behavior prediction algorithms that take into account the influence of seasons and weather and analyze tourist behavior data. For example, it can predict the visiting trends of tourist destinations depending on seasonal events and weather. This enables highly accurate behavior predictions that take into account the influence of seasons and weather.

[0113] The data analysis unit can analyze tourists' past travel patterns and develop algorithms that predict the frequency and duration of travel. For example, it can analyze tourists' past travel history and develop algorithms that predict the frequency and duration of travel. This makes it possible to predict the next travel date for tourists who tend to travel at the same time every year. The data analysis unit can also analyze tourists' travel patterns and predict the frequency and duration of travel. For example, it can classify tourists who prefer short trips and tourists who prefer long trips and predict the travel patterns for each. Furthermore, the data analysis unit can develop algorithms that predict the frequency and duration of travel based on tourists' past travel data. For example, it can analyze past travel history and predict the frequency and duration of the next trip. This makes it possible to predict the frequency and duration of tourists' travel and make more accurate suggestions.

[0114] The data analysis unit can use the emotion estimation function to analyze changes in tourists' emotions and predict their behavior based on their emotions. For example, it can analyze the emotions in tourists' social media posts and reviews to understand changes in emotions. This makes it possible to identify tourist destinations where positive emotions are increasing and reflect this in behavior predictions. The data analysis unit can also analyze tourists' facial expressions to understand changes in emotions. For example, it can analyze facial expressions at tourist destinations to identify tourist destinations where smiling and happy expressions are prevalent and reflect this in behavior predictions. Furthermore, the data analysis unit can analyze tourists' voice data to understand changes in emotions. For example, it can analyze conversations and voices at tourist destinations to identify tourist destinations where positive emotions are expressed and reflect this in behavior predictions. This makes it possible to predict tourist behavior based on tourists' emotions.

[0115] When analyzing tourist data, the data analysis unit can take into account interactions with other tourists and predict group behavior. For example, it can analyze tourist behavior data and consider interactions with other tourists. This makes it possible to identify tourist destinations and activities visited in groups and reflect this in behavior prediction. The data analysis unit can also analyze tourist social network data to understand interactions with other tourists. For example, it can identify tourist destinations visited with friends or family and reflect this in behavior prediction. Furthermore, the data analysis unit can analyze tourist behavior data and develop algorithms for predicting group behavior. For example, it can predict the tendency for multiple tourists to visit the same tourist destination at the same time. This makes it possible to predict group behavior taking into account interactions with other tourists.

[0116] When analyzing tourist data, the data analysis unit can compare the behavior patterns of tourists from different cultural regions and nationalities and make predictions that take cultural differences into account. For example, by analyzing tourist behavior data, it is possible to compare the behavior patterns of tourists from different cultural regions and nationalities. This makes it possible to understand the differences in the behavior patterns of tourists from Asian regions and Western regions. The data analysis unit can also analyze the behavior data of tourists from different cultural regions and nationalities and make behavior predictions that take cultural differences into account. For example, it can predict the tourist destinations and activities preferred by tourists from a particular cultural region. Furthermore, the data analysis unit can analyze tourist behavior data and develop behavior prediction algorithms that take cultural differences into account. For example, it can make predictions that reflect the preferences and behavior patterns of tourists from different cultural regions. This makes it possible to make behavior predictions that take cultural differences into account.

[0117] The data analysis unit can use the emotion estimation function to predict behavior based on tourist emotions and propose sightseeing plans that are emotionally satisfying. For example, the emotion estimation function can be used to predict behavior based on tourist emotions. This makes it possible to preferentially propose tourist destinations where tourists have strong positive emotions. The data analysis unit can also analyze tourist emotion data and propose sightseeing plans that are emotionally satisfying. For example, suggestions can be made based on tourist destinations and activities where tourists have had positive emotions in the past. Furthermore, the data analysis unit can use the emotion estimation function to develop a behavior prediction algorithm based on tourist emotions. For example, it can analyze tourist emotion data in real time and propose sightseeing plans that are emotionally satisfying. This makes it possible to predict behavior and propose sightseeing plans based on tourist emotions.

[0118] The proposal generation unit can generate customized sightseeing plans tailored to individual interests and concerns based on tourist preference data. For example, it can analyze tourist preference data and generate sightseeing plans tailored to individual interests and concerns. This makes it possible to suggest historical tourist spots to tourists who love history, and natural parks to tourists who love nature. The proposal generation unit can also generate customized sightseeing plans based on the tourist's past travel history. For example, it can suggest new sightseeing plans based on tourist spots visited in the past and activities participated in. Furthermore, the proposal generation unit can generate sightseeing plans tailored to individual interests and concerns in real time based on the tourist preference data. For example, it can suggest the optimal sightseeing plan based on the tourist's current interests and concerns. This makes it possible to provide sightseeing plans tailored to the tourist's individual interests and concerns.

[0119] The proposal generation unit can suggest unvisited tourist destinations based on the tourist's past travel history, thereby providing a new experience. For example, it can analyze the tourist's past travel history and suggest unvisited tourist destinations. This makes it possible to suggest tourist destinations similar to tourist destinations that the tourist has visited in the past, thereby providing a new experience. The proposal generation unit can also develop an algorithm for suggesting unvisited tourist destinations based on the tourist's travel history. For example, it can analyze the characteristics of tourist destinations that the tourist has visited in the past and suggest similar tourist destinations. Furthermore, the proposal generation unit can suggest unvisited tourist destinations in real time based on the tourist's past travel history. For example, it can suggest unvisited tourist destinations related to the tourist destination that the tourist is currently visiting. This makes it possible to provide a new experience for the tourist.

[0120] The proposal generation unit can use the emotion estimation function to generate proposals based on the emotions of tourists and provide sightseeing plans that are emotionally satisfying. For example, the emotion estimation function can be used to generate proposals based on the emotions of tourists. This makes it possible to preferentially suggest tourist destinations and activities that evoke strong positive emotions. The proposal generation unit can also analyze tourist emotion data and provide sightseeing plans that are emotionally satisfying. For example, suggestions can be made based on tourist destinations and activities that tourists have felt positive about in the past. Furthermore, the proposal generation unit can use the emotion estimation function to develop a proposal algorithm based on tourist emotions. For example, the emotion data of tourists can be analyzed in real time to provide sightseeing plans that are emotionally satisfying. This makes it possible to provide sightseeing plans that are emotionally satisfying by making proposals based on tourist emotions.

[0121] The proposal generation unit can generate proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences. For example, the proposal generation unit can analyze the tourist's preference data and generate proposals based on the behavior of other tourists with the same preferences. This makes it possible to suggest tourist spots and activities visited by tourists with the same hobbies and interests. The proposal generation unit can also analyze the behavior patterns of other tourists with the same preferences, based on the tourist's preference data. For example, it can identify tourist spots and activities preferred by tourists with the same preferences and reflect them in the proposals. Furthermore, the proposal generation unit can generate proposals based on the tourist's preference data, taking into account the behavior of other tourists with the same preferences in real time. For example, it can suggest tourist spots and activities currently visited by tourists with the same preferences. This makes it possible to make proposals based on the behavior of other tourists with the same preferences.

[0122] The proposal generation unit can generate proposals according to different seasons and time periods based on tourist preference data, thereby drawing out the appeal of each season. For example, it can analyze tourist preference data and generate proposals according to different seasons and time periods. This makes it possible to propose cherry blossom viewing spots in spring and beach resorts in summer. The proposal generation unit can also generate proposals that draw out the appeal of each season based on tourist preference data. For example, it can propose autumn foliage spots in autumn and ski resorts in winter. Furthermore, the proposal generation unit can develop proposal algorithms according to different seasons and time periods based on tourist preference data. For example, seasonal events and activities can be reflected in the proposals. This makes it possible to make proposals that draw out the appeal of each season.

[0123] The proposal generation unit can use the emotion estimation function to generate proposals based on the tourist's emotions in real time, thereby optimizing the local experience. For example, the emotion estimation function can be used to generate proposals based on the tourist's emotions in real time. This makes it possible to suggest optimal tourist spots and activities according to the tourist's current emotional state. The proposal generation unit can also analyze the tourist's emotion data in real time and make proposals based on the emotions. For example, it can propose a tour plan that will make the tourist feel positive emotions at the local site. Furthermore, the proposal generation unit can use the emotion estimation function to develop a proposal algorithm based on the tourist's emotions. For example, it can analyze the tourist's emotion data in real time and make proposals that optimize the local experience. This makes it possible to optimize the local experience by making proposals based on the tourist's emotions in real time.

[0124] The feedback collection unit can track the tourist's behavior in real time after providing the suggestion and immediately evaluate the effectiveness of the suggestion. For example, after providing the suggestion, the GPS data of the tourist's smartphone can be tracked in real time to evaluate the effectiveness of the suggestion. This makes it possible to confirm whether the tourist actually visited the suggested tourist spot. The feedback collection unit can also analyze the tourist's social media posts in real time after providing the suggestion and evaluate the effectiveness of the suggestion. For example, it can analyze the content of posts about the suggested tourist spot and check whether there are many positive responses. Furthermore, the feedback collection unit can track the tourist's behavior data in real time after providing the suggestion and immediately evaluate the effectiveness of the suggestion. For example, it can check whether the tourist participated in the suggested activity. This makes it possible to evaluate the effectiveness of the suggestion in real time.

[0125] The feedback collection unit can analyze the feedback data and develop an algorithm that continuously improves the accuracy of the suggestions. For example, it can analyze the feedback data collected after the suggestions are provided and develop an algorithm that continuously improves the accuracy of the suggestions. This makes it possible to improve the content of the suggestions based on the ratings and comments of tourists. The feedback collection unit can also analyze the feedback data and build a system that continuously improves the accuracy of the suggestions. For example, it can adjust the algorithm based on the ratings of the suggested tourist spots and activities. Furthermore, the feedback collection unit can develop an algorithm that continuously improves the accuracy of the suggestions based on the feedback data collected after the suggestions are provided. For example, it can analyze the behavioral data and emotional data of tourists and optimize the content of the suggestions. This makes it possible to continuously improve the accuracy of the suggestions.

[0126] The feedback collection unit can use the emotion estimation function to collect tourists' emotional responses and analyze emotion-based feedback. For example, the emotion estimation function can be used to collect tourists' emotional responses. This makes it possible to analyze facial expressions and voices at tourist spots and check whether there are many positive emotions. The feedback collection unit can also analyze tourists' emotional data and provide emotion-based feedback. For example, it can identify tourist spots and activities that tourists have positive emotions about and improve the recommendations. Furthermore, the feedback collection unit can use the emotion estimation function to collect tourists' emotional responses in real time and analyze emotion-based feedback. For example, it can optimize the recommendations based on the tourist emotion data. This makes it possible to collect and analyze feedback based on tourists' emotions.

[0127] After providing the suggestions, the feedback collection unit can analyze the tourists' social media posts and evaluate the effectiveness of the suggestions. For example, after providing the suggestions, the feedback collection unit can analyze the tourists' social media posts and evaluate the effectiveness of the suggestions. This makes it possible to analyze the content of posts about the suggested tourist destinations and check whether there are many positive reactions. The feedback collection unit can also collect the tourists' social media posts and evaluate the effectiveness of the suggestions. For example, it can analyze posts about the suggested tourist destinations and activities and make an evaluation. Furthermore, after providing the suggestions, the feedback collection unit can analyze the tourists' social media posts in real time and immediately evaluate the effectiveness of the suggestions. For example, it can analyze the content of posts about the suggested tourist destinations in real time and make an evaluation. This makes it possible to analyze the social media posts and evaluate the effectiveness of the suggestions.

[0128] After providing the suggestions, the feedback collection unit can analyze the tourist's purchase history and evaluate the economic effectiveness of the suggestions. For example, after providing the suggestions, the tourist's credit card or electronic money usage history can be analyzed to evaluate the economic effectiveness of the suggestions. This makes it possible to analyze the shopping and dining history at the suggested tourist destinations. The feedback collection unit can also collect the tourist's purchase history and evaluate the economic effectiveness of the suggestions. For example, expenditures at the suggested tourist destinations or activities can be analyzed and evaluated. Furthermore, after providing the suggestions, the feedback collection unit can analyze the tourist's purchase history in real time and immediately evaluate the economic effectiveness of the suggestions. For example, the purchase history at the suggested tourist destinations can be analyzed in real time and evaluated. This makes it possible to analyze the tourist's purchase history and evaluate the economic effectiveness of the suggestions.

[0129] The feedback collection unit can use the emotion estimation function to collect tourists' emotional responses in real time and instantly evaluate the effectiveness of the proposal. For example, the emotion estimation function can be used to collect tourists' emotional responses in real time. This makes it possible to analyze facial expressions and voices at tourist spots and check whether there are many positive emotions. The feedback collection unit can also analyze tourists' emotional data in real time and instantly evaluate the effectiveness of the proposal. For example, it can be checked whether tourists felt positive emotions at the recommended tourist spot. Furthermore, the feedback collection unit can use the emotion estimation function to collect tourists' emotional responses in real time and evaluate the effectiveness of the proposal. For example, the content of the proposal can be optimized based on the tourist emotion data. This makes it possible to collect tourists' emotional responses in real time and instantly evaluate the effectiveness of the proposal.

[0130] The optimization unit can propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities based on the feedback data. For example, the optimization unit can analyze the feedback data and propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities. This makes it possible to identify areas for improvement at the facilities based on tourists' ratings and comments. The optimization unit can also propose improvement measures to improve the efficiency of operations based on tourist feedback data. For example, it can propose specific measures to improve how to handle crowded times and the quality of service. Furthermore, the optimization unit can analyze the feedback data and develop algorithms to optimize the operation of tourist destinations and tourist facilities. For example, it can identify areas for improvement in operations based on tourist behavior data and emotion data. This makes it possible to propose specific improvement measures to optimize the operation of tourist destinations and tourist facilities.

[0131] The optimization unit can optimize the promotion strategies of tourist destinations and tourist facilities based on tourist preference data. For example, it can analyze tourist preference data and optimize the promotion strategies of tourist destinations and tourist facilities. This makes it possible to carry out effective promotions for tourists with specific preferences. The optimization unit can also develop an algorithm that optimizes the promotion strategies based on the tourist preference data. For example, it can propose advertisements and campaigns that match the tourist preferences. Furthermore, the optimization unit can analyze the tourist preference data and optimize the promotion strategies of tourist destinations and tourist facilities in real time. For example, it can carry out promotions that match the tourists' current preferences and interests. This makes it possible to optimize the promotion strategies of tourist destinations and tourist facilities.

[0132] The optimization unit can use the emotion estimation function to propose operational improvement measures based on tourist emotions. For example, the emotion estimation function can be used to propose operational improvement measures based on tourist emotions. This makes it possible to identify areas for improvement in services and facilities that will evoke positive emotions from tourists. The optimization unit can also analyze tourist emotion data and propose operational improvement measures based on emotions. For example, it can identify areas for improvement in operations based on tourist destinations and activities that evoke positive emotions from tourists. Furthermore, the optimization unit can use the emotion estimation function to develop an operational improvement algorithm based on tourist emotions. For example, it can analyze tourist emotion data in real time to identify areas for improvement in operations. This makes it possible to propose operational improvement measures based on tourist emotions.

[0133] The optimization unit can develop new services and attractions for tourist destinations and facilities based on the feedback data. For example, the optimization unit can analyze the feedback data to develop new services and attractions for tourist destinations and facilities. This makes it possible to propose new attractions based on tourists' ratings and comments. The optimization unit can also develop algorithms for developing new services and attractions based on tourist feedback data. For example, new services can be proposed based on tourist preference and behavior data. Furthermore, the optimization unit can analyze the feedback data to develop new services and attractions for tourist destinations and facilities in real time. For example, new attractions can be proposed based on tourists' current preferences and interests. This makes it possible to develop new services and attractions for tourist destinations and facilities.

[0134] The optimization unit can optimize the layout and design of tourist destinations and tourist facilities based on the feedback data. For example, the optimization unit can analyze the feedback data and optimize the layout and design of tourist destinations and tourist facilities. This makes it possible to improve the layout of the facilities based on tourists' ratings and comments. The optimization unit can also develop an algorithm that optimizes the layout and design based on tourist feedback data. For example, the layout of the facilities can be optimized based on tourist behavior data. Furthermore, the optimization unit can analyze the feedback data and optimize the layout and design of tourist destinations and tourist facilities in real time. For example, it can propose a layout based on the current preferences and interests of tourists. This makes it possible to optimize the layout and design of tourist destinations and tourist facilities.

[0135] The optimization unit can use the emotion estimation function to propose operational improvement measures based on tourist emotions in real time. For example, the emotion estimation function can be used to propose operational improvement measures based on tourist emotions in real time. This makes it possible to propose optimal operational improvement measures based on the tourist's current emotional state. The optimization unit can also analyze tourist emotion data in real time and propose operational improvement measures based on the emotions. For example, it can identify improvements to services and facilities that will evoke positive emotions in tourists in real time. Furthermore, the optimization unit can use the emotion estimation function to develop an operation improvement algorithm based on tourist emotions. For example, it can analyze tourist emotion data in real time and identify improvements to operations in real time. This makes it possible to propose operational improvement measures based on tourist emotions in real time.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The tourist data collection unit collects tourist data. For example, it collects tourist past behavior data and preference data. The tourist data collection unit can also collect data from tourists' social media posts and review sites. For example, it collects the history of tourist spots visited by tourists and the services they have used. Step 2: The data analysis unit analyzes the tourist data collected by the tourist data collection unit. For example, the generation AI analyzes tourist behavior patterns and preferences. Step 3: The behavior prediction unit predicts tourist behavior based on the data analyzed by the data analysis unit. For example, the generation AI predicts the tourist's likely next visit destination and the services they are likely to use. Step 4: The proposal generation unit generates proposals based on the behavior predicted by the behavior prediction unit. For example, the generation AI suggests tourist spots that tourists are likely to visit next, as well as accommodations, restaurants, and events that they are likely to use. Step 5: The feedback collection unit provides the suggestions generated by the suggestion generation unit to tourist destinations and tourist facilities and collects the results as feedback, for example, by checking whether tourists actually visited the suggested tourist destinations and whether they used the suggested services. Step 6: The optimization unit analyzes the feedback data collected by the feedback collection unit and optimizes the operation of tourist destinations and facilities. For example, the generation AI can provide services tailored to tourist preferences and strengthen the promotion of popular tourist destinations.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a tourist data collection unit that collects tourist data; a data analysis unit that analyzes the tourist data collected by the tourist data collection unit; a behavior prediction unit that predicts tourist behavior based on the data analyzed by the data analysis unit; a proposal generation unit that generates a proposal based on the behavior predicted by the behavior prediction unit; a feedback collection unit that provides the proposals generated by the proposal generation unit to tourist destinations and tourist facilities and collects the results as feedback; an optimization unit that analyzes the feedback data collected by the feedback collection unit and optimizes the operation of tourist destinations and tourist facilities. A system characterized by:

2. The tourist data collection unit Collect data from tourists' social media posts or review sites and analyze it with the generative AI.

2. The system of claim 1.

3. The tourist data collection unit Tourist movement data is collected in real time and analyzed using the generative AI.

2. The system of claim 1.

4. The tourist data collection unit Analyzing tourists' emotions at the places they visit and identifying tourist spots that evoke positive emotions 2. The system of claim 1.

5. The tourist data collection unit Collect tourist purchase history data and analyze it using the generation AI.

2. The system of claim 1.

6. The tourist data collection unit Collecting tourist health data and analyzing it with the generative AI 2. The system of claim 1.

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