System

Abstract

An object of a system according to an embodiment is to efficiently plan and reserve a trip.SOLUTION: A system includes a communication log analysis unit, a proposal unit, and a reservation unit. The communication log analysis unit analyzes a communication log of the mobile terminal. The proposal unit proposes a travel on the basis of the population flow rate analyzed by the communication log analysis unit. The reservation part performs collective reservation on the basis of the process of the travel proposed by the proposal part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that travel planning and booking are complicated and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently plan and book trips. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication log analysis unit, a proposal unit, and a reservation unit. The communication log analysis unit analyzes mobile phone communication logs. The proposal unit proposes a trip based on the population mobility rate analyzed by the communication log analysis unit. The reservation unit makes a bulk reservation based on the trip itinerary proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows efficient travel planning and booking. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interactive generative AI system according to an embodiment of the present invention analyzes population mobility rates using mobile phone communication logs and suggests recommended travel spots and inns. This allows the interactive generative AI system to propose a travel itinerary based on the user's preferences and conditions, and if the user agrees with the proposal, they can make reservations all at once through the system. This allows the user to consistently plan and book their trip, making it easier and more enjoyable for them.

[0029] The interactive generative AI system according to the embodiment includes a communication log analysis unit, a proposal unit, and a reservation unit. The communication log analysis unit analyzes a mobile phone communication log. For example, the communication log analysis unit analyzes communication logs such as call history, message history, and data traffic to determine population mobility. The communication log analysis unit can also analyze human movement data for specific time periods and regions. The proposal unit makes travel proposals based on the population mobility rate analyzed by the communication log analysis unit. For example, the proposal unit proposes recommended tourist spots and inns based on the user's desired travel conditions and budget. The proposal unit can also use the generative AI to propose a travel itinerary according to the user's preferences. The reservation unit makes a bulk reservation based on the travel itinerary proposed by the proposal unit. For example, the reservation unit can make all the necessary arrangements, such as admission tickets to tourist spots, hotel reservations, and transportation arrangements, all at once. If the user agrees with the proposal, the reservation unit can make all the reservations through the system. This allows the interactive generative AI system according to the embodiment to consistently use the mobile phone communication log to propose and book a trip. For example, users can avoid the hassle of making individual reservations, which helps them plan their trips smoothly. Furthermore, users can obtain necessary information in real time during their trip, allowing them to enjoy a comfortable trip.

[0030] The communication log analysis unit can predict congestion at specific tourist destinations and suggest optimal visit times to the user. For example, the communication log analysis unit analyzes communication log data and tally the number of past visitors to a specific tourist destination by time period. This identifies peak and off-peak times and suggests optimal visit times to the user. The communication log analysis unit also analyzes communication log data in real time to understand the current congestion situation. This makes it possible to predict congestion during the time period when the user is planning to visit and suggest optimal visit times. The communication log analysis unit also uses communication log data to analyze congestion patterns at specific tourist destinations by season. This makes it possible to suggest optimal visit times and time periods to the user. This makes it possible to suggest optimal visit times to the user, enabling them to travel while avoiding crowds.

[0031] The communication log analysis unit can analyze the seasonal popularity of a specific tourist destination and suggest the optimal time to visit to the user. For example, the communication log analysis unit analyzes the communication log data and tally the number of visitors to a specific tourist destination by season. This allows the popularity of each season to be understood and the optimal time to visit to be suggested to the user. The communication log analysis unit also uses the communication log data to analyze the seasonal congestion patterns of a specific tourist destination. This allows the user to suggest the optimal time to visit to avoid crowds. The communication log analysis unit can also collect information on seasonal events and activities at a specific tourist destination based on the communication log data and suggest the optimal time to visit to the user. This allows the user to travel while avoiding crowds by suggesting the optimal time to visit.

[0032] The communication log analysis unit can predict traffic congestion in urban areas and suggest the optimal travel route to the user. For example, the communication log analysis unit analyzes communication log data to understand traffic congestion patterns in urban areas by time period. This allows the unit to suggest the optimal travel route and time period to the user. The communication log analysis unit also analyzes communication log data in real time to understand the current traffic congestion situation. This allows the unit to predict traffic congestion for the time period when the user is planning to travel and suggest the optimal travel route. The communication log analysis unit also uses communication log data to analyze traffic congestion patterns in urban areas by season. This allows the unit to suggest the optimal travel route and time period to the user. This allows the unit to suggest the optimal travel route to the user, enabling travel that avoids traffic congestion.

[0033] The communication log analysis unit analyzes the congestion status of an event in real time and can suggest the optimal timing to participate to the user. For example, the communication log analysis unit analyzes communication log data in real time to understand the current congestion status of a specific event. This allows the unit to suggest the optimal timing to participate to the user. The communication log analysis unit also uses communication log data to analyze congestion patterns of past events and suggests the optimal timing to participate to the user. For example, it can make suggestions to avoid peak hours based on past data. The communication log analysis unit also analyzes the congestion status of a specific event by season based on communication log data. This allows the unit to suggest the optimal time and timing to participate to the user. This allows the user to participate in an event without being congested by suggesting the optimal timing to participate.

[0034] The suggestion unit can analyze past user reviews and suggest inns and tourist spots that are most suitable for specific conditions. For example, the suggestion unit performs text analysis on past user reviews to extract inns and tourist spots that are most suitable for specific conditions. For example, it can suggest inns that are ideal for families and tourist spots for couples. The suggestion unit also performs sentiment analysis on user reviews to identify inns and tourist spots with many positive reviews. This makes it possible to suggest the best options for the user. The suggestion unit also creates a ranking of inns and tourist spots that are most suitable for specific conditions based on past user reviews and suggests them to the user. For example, it can provide a ranking based on budget and facilities. This improves user satisfaction by suggesting the best inns and tourist spots based on past user reviews.

[0035] The suggestion unit can provide detailed information about spots and inns suggested by the generation AI to assist the user in making a selection. The suggestion unit, for example, builds a system that provides detailed information about spots and inns suggested by the generation AI. For example, it can display detailed information such as facilities, service content, and pricing plans. The suggestion unit also provides detailed information about the suggested spots and inns to the user to assist in their selection. For example, it can display photos, reviews, access information, etc. The suggestion unit also updates the detailed information about spots and inns suggested by the generation AI in real time to provide the user with the latest information. For example, it can display information about room availability and special plans. This provides detailed information to assist the user in their selection and improve their satisfaction.

[0036] The suggestion unit can add information about local culture and history to the suggestions for recommended spots and inns to pique the user's interest. For example, the suggestion unit can add information about local culture and history to the suggestions for recommended spots and inns. For example, it can provide information such as the history of tourist destinations, traditional events, and local specialty dishes. The suggestion unit also makes suggestions that pique the user's interest based on the information about local culture and history. For example, it can suggest plans to visit historical buildings and cultural heritage sites. The suggestion unit also updates the information about local culture and history in the suggestions for recommended spots and inns in real time to provide the user with the latest information. For example, it can display information about local events and festivals. In this way, adding information about local culture and history piques the user's interest and improves their satisfaction with the trip.

[0037] The suggestion unit can introduce hidden attractions and local restaurants around the proposed spot or inn. For example, the suggestion unit builds a system that introduces hidden attractions and local restaurants around the proposed spot or inn. For example, it can suggest attractions and popular restaurants that only locals know about. The suggestion unit also collects information about the area around the proposed spot or inn and provides it to the user. For example, it can display information about hidden attractions and local restaurants near tourist spots. The suggestion unit also updates information about hidden attractions and local restaurants around the proposed spot or inn in real time to provide the user with the latest information. For example, it can display information about newly opened restaurants and events. This enriches the user's travel experience by introducing hidden attractions and local restaurants.

[0038] The suggestion unit can include activities that take into account the user's health condition and physical strength in the itinerary suggested by the generation AI. For example, the suggestion unit can suggest appropriate activities by having the generation AI take into account the user's health condition and physical strength. For example, it can suggest a light walking course for a user who is not confident in their physical strength. The suggestion unit can also have the generation AI suggest optimal activities based on the user's health data. For example, it can analyze heart rate and step count data to suggest a reasonable itinerary. The suggestion unit can also have the generation AI monitor the user's health condition in real time and adjust activities based on that data. For example, it can suggest a break if the user appears tired. This makes it possible to travel without straining yourself by including activities that take into account the user's health condition and physical strength.

[0039] The suggestion unit can incorporate the opinions of local guides and experts when proposing travel itineraries. For example, the generation AI collects the opinions of local guides and experts and proposes travel itineraries based on them. For example, local recommended spots and hidden gems can be included. The suggestion unit also analyzes reviews from local guides and experts to propose the optimal travel itinerary for the user. For example, it can provide information that is not included in guidebooks. The suggestion unit also works with local guides and experts to collect the latest information in real time and proposes travel itineraries based on that information. For example, it can include information about local events. This makes it possible to propose more fulfilling travel itineraries by incorporating the opinions of local guides and experts.

[0040] The suggestion unit can add local events and workshops that the user can participate in to the proposed itinerary. For example, the generation AI collects information on local events and workshops and suggests travel itineraries based on that information. For example, local festivals and cultural experiences can be included. The suggestion unit also suggests local events and workshops based on the user's interests and concerns. For example, cooking classes and traditional craft experiences can be included. The suggestion unit also collects local event information in real time and suggests the optimal timing for the user to participate. For example, it can suggest itineraries that coincide with the date and time of the event. This enriches the travel experience by adding local events and workshops that the user can participate in.

[0041] The reservation unit can reflect the user's past reservation history and make more personalized suggestions. For example, the reservation system analyzes the user's past reservation history and makes personalized suggestions based on that data. For example, it can take into account accommodations and means of transportation used in the past. The reservation unit can also suggest similar accommodations and means of transportation based on the user's past reservation history. For example, it can prioritize suggestions based on hotel chains and airlines that the user has preferred in the past. The reservation system can also update the user's past reservation history in real time and make personalized suggestions based on the latest information. For example, it can reflect recent reservation history. In this way, by reflecting past reservation history, more personalized suggestions are possible.

[0042] The reservation unit can integrate the user's payment method and point system to improve convenience. The reservation unit, for example, integrates the user's payment method into the reservation system to improve convenience. For example, it can manage credit cards, electronic money, and point systems all at once. The reservation unit also integrates the user's point system into the reservation system to enable reservations using points. For example, points can be used to get discounts on accommodation fees. The reservation unit also allows the reservation system to update the user's payment method and point system in real time, improving convenience based on the latest information. For example, it can display the point balance and expiration date. In this way, integrating the payment method and point system improves user convenience.

[0043] The reservation unit can add a purchasing option for local specialties and souvenirs to the bulk reservation system. The reservation unit, for example, adds a purchasing option for local specialties and souvenirs to the bulk reservation system. For example, it can make it possible to purchase local specialties and handmade souvenirs when making a reservation. The reservation unit also provides information on specialties and souvenirs that users can purchase at their travel destinations and adds options that can be purchased together when making a reservation. For example, it can make it possible to reserve local specialties in advance. The reservation unit also updates information on local specialties and souvenirs in real time in the bulk reservation system to provide users with the latest information. For example, it can display newly released specialties and limited edition souvenirs. In this way, adding the purchasing option for local specialties and souvenirs enriches the user's travel experience.

[0044] The reservation unit can add a reservation option for local tours and activities that users can participate in to the reservation system. The reservation unit, for example, adds a reservation option for local tours and activities to a bulk reservation system. For example, it can enable reservations for sightseeing tours and activities to be made in bulk. The reservation unit also provides information on local tours and activities that users can participate in at their travel destinations and adds an option that can be booked together when making a reservation. For example, it can enable users to reserve a local guided tour in advance. The reservation unit also allows the bulk reservation system to update information on local tours and activities in real time to provide users with the latest information. For example, it can display newly added tours and activities. In this way, adding reservation options for local tours and activities enriches the user's travel experience.

[0045] The suggestion unit can reflect the user's past travel history and preferences in the information provided by the generation AI. For example, the suggestion unit analyzes the user's past travel history and provides information to improve the comfort of the trip based on that data. For example, it can suggest accommodations and restaurants that the user has previously preferred. The suggestion unit also analyzes the user's past travel history and reviews to provide optimal information to reflect the user's preferences. For example, it can include tourist spots and activities that the user likes. The suggestion unit also suggests similar travel plans based on the user's past travel history. For example, it can suggest tourist spots with a similar atmosphere to places visited in the past. This enables more personalized suggestions by reflecting the user's past travel history and preferences.

[0046] The suggestion unit can incorporate the opinions of local guides and experts to improve the comfort of the trip. For example, the generation AI collects the opinions of local guides and experts and provides information to improve the comfort of the trip based on that. For example, the suggestion unit can include recommended local spots and hidden gems. The suggestion unit also analyzes reviews from local guides and experts to provide the most suitable information to the user. For example, it can provide information that is not included in guidebooks. The suggestion unit also works with local guides and experts to collect the latest information in real time and provides information to improve the comfort of the trip based on that information. For example, it can include information about local events. This improves the comfort of the trip by incorporating the opinions of local guides and experts.

[0047] The suggestion unit can add local events and workshops that the user can participate in to improve the comfort of the trip. For example, the generation AI collects information on local events and workshops and provides information to improve the comfort of the trip based on that information. For example, local festivals and cultural experiences can be included. The suggestion unit also suggests local events and workshops based on the user's interests. For example, cooking classes and traditional craft experiences can be included. The suggestion unit also collects local event information in real time and suggests the optimal timing for the user to participate. For example, it can suggest a trip that matches the date and time of the event. This improves the comfort of the trip by adding local events and workshops that the user can participate in.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The suggestion unit can monitor the user's health condition and suggest an appropriate travel plan. For example, the suggestion unit can analyze the user's heart rate and step count data to suggest a reasonable sightseeing route. The suggestion unit can also suggest rest points and snacks based on the user's health condition. Furthermore, the suggestion unit can suggest activities that include an appropriate amount of exercise based on the user's health data. This allows the user to enjoy their trip while maintaining their health.

[0050] The suggestion unit can suggest similar travel plans based on the user's past travel history. For example, it can suggest spots with a similar atmosphere to tourist spots that the user has visited in the past. The suggestion unit can also create new travel plans based on activities and restaurants that the user has previously preferred. Furthermore, the suggestion unit can analyze the user's past travel history and suggest optimal accommodations and transportation options. This allows the user to enjoy a new trip that makes use of their past travel experience.

[0051] The suggestion unit can propose travel plans that incorporate the opinions of local guides and experts. For example, it can suggest hidden attractions and restaurants recommended by local guides. The suggestion unit can also suggest the most suitable tourist spots and activities for users based on reviews by experts. Furthermore, the suggestion unit can work with local guides and experts to collect the latest information in real time and propose travel plans based on that information. This allows users to enjoy a fulfilling trip that makes use of local knowledge.

[0052] The suggestion unit can suggest a travel plan that takes into account the user's health condition. For example, the suggestion unit can analyze the user's heart rate and step count data to suggest a reasonable sightseeing route. The suggestion unit can also suggest rest points and snacks based on the user's health condition. Furthermore, the suggestion unit can suggest activities that include an appropriate amount of exercise based on the user's health data. This allows the user to enjoy their trip while maintaining their health.

[0053] The suggestion unit can suggest similar travel plans based on the user's past travel history. For example, it can suggest spots with a similar atmosphere to tourist spots that the user has visited in the past. The suggestion unit can also create new travel plans based on activities and restaurants that the user has previously preferred. Furthermore, the suggestion unit can analyze the user's past travel history and suggest optimal accommodations and transportation options. This allows the user to enjoy a new trip that makes use of their past travel experience.

[0054] The suggestion unit can propose travel plans that incorporate the opinions of local guides and experts. For example, it can suggest hidden attractions and restaurants recommended by local guides. The suggestion unit can also suggest the most suitable tourist spots and activities for users based on reviews by experts. Furthermore, the suggestion unit can work with local guides and experts to collect the latest information in real time and propose travel plans based on that information. This allows users to enjoy a fulfilling trip that makes use of local knowledge.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The communication log analysis unit analyzes the mobile phone communication log. For example, it analyzes communication logs such as call history, message history, and data traffic volume to understand the population mobility rate. It can also analyze data on people's movements during specific time periods or in specific areas. Step 2: The proposal unit makes travel suggestions based on the population mobility rate analyzed by the communication log analysis unit. For example, it suggests recommended tourist spots and inns based on the user's desired travel conditions and budget. It can also use generation AI to suggest travel itineraries that meet the user's preferences. Step 3: The reservation unit makes a bulk reservation based on the travel itinerary proposed by the proposal unit. For example, it can make a bulk reservation for admission tickets to tourist spots, accommodation reservations at hotels, and transportation arrangements. If the user agrees with the proposal, they can make a bulk reservation through the system.

[0057] (Example 2) The interactive generative AI system according to an embodiment of the present invention analyzes population mobility rates using mobile phone communication logs and suggests recommended travel spots and inns. This allows the interactive generative AI system to propose a travel itinerary based on the user's preferences and conditions, and if the user agrees with the proposal, they can make reservations all at once through the system. This allows the user to consistently plan and book their trip, making it easier and more enjoyable for them.

[0058] The interactive generative AI system according to the embodiment includes a communication log analysis unit, a proposal unit, and a reservation unit. The communication log analysis unit analyzes a mobile phone communication log. For example, the communication log analysis unit analyzes communication logs such as call history, message history, and data traffic to determine population mobility. The communication log analysis unit can also analyze human movement data for specific time periods and regions. The proposal unit makes travel proposals based on the population mobility rate analyzed by the communication log analysis unit. For example, the proposal unit proposes recommended tourist spots and inns based on the user's desired travel conditions and budget. The proposal unit can also use the generative AI to propose a travel itinerary according to the user's preferences. The reservation unit makes a bulk reservation based on the travel itinerary proposed by the proposal unit. For example, the reservation unit can make all the necessary arrangements, such as admission tickets to tourist spots, hotel reservations, and transportation arrangements, all at once. If the user agrees with the proposal, the reservation unit can make all the reservations through the system. This allows the interactive generative AI system according to the embodiment to consistently use the mobile phone communication log to propose and book a trip. For example, users can avoid the hassle of making individual reservations, which helps them plan their trips smoothly. Furthermore, users can obtain necessary information in real time during their trip, allowing them to enjoy a comfortable trip.

[0059] The communication log analysis unit can predict congestion at specific tourist destinations and suggest optimal visit times to the user. For example, the communication log analysis unit analyzes communication log data and tally the number of past visitors to a specific tourist destination by time period. This identifies peak and off-peak times and suggests optimal visit times to the user. The communication log analysis unit also analyzes communication log data in real time to understand the current congestion situation. This makes it possible to predict congestion during the time period when the user is planning to visit and suggest optimal visit times. The communication log analysis unit also uses communication log data to analyze congestion patterns at specific tourist destinations by season. This makes it possible to suggest optimal visit times and time periods to the user. This makes it possible to suggest optimal visit times to the user, enabling them to travel while avoiding crowds.

[0060] The communication log analysis unit can analyze the seasonal popularity of a specific tourist destination and suggest the optimal time to visit to the user. For example, the communication log analysis unit analyzes the communication log data and tally the number of visitors to a specific tourist destination by season. This allows the popularity of each season to be understood and the optimal time to visit to be suggested to the user. The communication log analysis unit also uses the communication log data to analyze the seasonal congestion patterns of a specific tourist destination. This allows the user to suggest the optimal time to visit to avoid crowds. The communication log analysis unit can also collect information on seasonal events and activities at a specific tourist destination based on the communication log data and suggest the optimal time to visit to the user. This allows the user to travel while avoiding crowds by suggesting the optimal time to visit.

[0061] The communication log analysis unit uses the emotion estimation function to analyze the emotion data of past visitors and can suggest tourist destinations that will provide the user with the most positive experience. The communication log analysis unit, for example, combines the communication log data and the emotion estimation data to analyze the emotion scores of past visitors. This allows the unit to identify tourist destinations that will provide a positive experience and suggest them to the user. The communication log analysis unit also uses the emotion estimation function to analyze the emotion data of past visitors and identify factors that contribute to a positive experience at a specific tourist destination. Based on this, the communication log analysis unit can suggest the most suitable tourist destination for the user. The communication log analysis unit also analyzes the emotion scores of specific tourist destinations by season based on the communication log data and the emotion estimation data. This allows the unit to suggest the best time to visit that will provide the user with the most positive experience. This allows the unit to suggest tourist destinations that will provide the user with the most positive experience, thereby improving travel satisfaction.

[0062] The communication log analysis unit can predict traffic congestion in urban areas and suggest the optimal travel route to the user. For example, the communication log analysis unit analyzes communication log data to understand traffic congestion patterns in urban areas by time period. This allows the unit to suggest the optimal travel route and time period to the user. The communication log analysis unit also analyzes communication log data in real time to understand the current traffic congestion situation. This allows the unit to predict traffic congestion for the time period when the user is planning to travel and suggest the optimal travel route. The communication log analysis unit also uses communication log data to analyze traffic congestion patterns in urban areas by season. This allows the unit to suggest the optimal travel route and time period to the user. This allows the unit to suggest the optimal travel route to the user, enabling travel that avoids traffic congestion.

[0063] The communication log analysis unit analyzes the congestion status of an event in real time and can suggest the optimal timing to participate to the user. For example, the communication log analysis unit analyzes communication log data in real time to understand the current congestion status of a specific event. This allows the unit to suggest the optimal timing to participate to the user. The communication log analysis unit also uses communication log data to analyze congestion patterns of past events and suggests the optimal timing to participate to the user. For example, it can make suggestions to avoid peak hours based on past data. The communication log analysis unit also analyzes the congestion status of a specific event by season based on communication log data. This allows the unit to suggest the optimal time and timing to participate to the user. This allows the user to participate in an event without being congested by suggesting the optimal timing to participate.

[0064] The communication log analysis unit can use the emotion estimation function to suggest relaxing tourist spots and activities based on the user's current emotional state. The communication log analysis unit, for example, uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the unit to suggest relaxing tourist spots and activities. The communication log analysis unit also combines the communication log data with the emotion estimation data to suggest optimal tourist spots and activities based on the user's emotional state. For example, it can suggest places where you can relax when you are feeling high in stress. The communication log analysis unit also uses the emotion estimation function to analyze the user's emotional state and suggest optimal tourist spots and activities based on past data. For example, it can re-suggest places where you have found relaxation in the past. This improves travel satisfaction by suggesting relaxing tourist spots and activities based on the user's emotional state.

[0065] The suggestion unit can analyze past user reviews and suggest inns and tourist spots that are most suitable for specific conditions. For example, the suggestion unit performs text analysis on past user reviews to extract inns and tourist spots that are most suitable for specific conditions. For example, it can suggest inns that are ideal for families and tourist spots for couples. The suggestion unit also performs sentiment analysis on user reviews to identify inns and tourist spots with many positive reviews. This makes it possible to suggest the best options for the user. The suggestion unit also creates a ranking of inns and tourist spots that are most suitable for specific conditions based on past user reviews and suggests them to the user. For example, it can provide a ranking based on budget and facilities. This improves user satisfaction by suggesting the best inns and tourist spots based on past user reviews.

[0066] The suggestion unit can provide detailed information about spots and inns suggested by the generation AI to assist the user in making a selection. The suggestion unit, for example, builds a system that provides detailed information about spots and inns suggested by the generation AI. For example, it can display detailed information such as facilities, service content, and pricing plans. The suggestion unit also provides detailed information about the suggested spots and inns to the user to assist in their selection. For example, it can display photos, reviews, access information, etc. The suggestion unit also updates the detailed information about spots and inns suggested by the generation AI in real time to provide the user with the latest information. For example, it can display information about room availability and special plans. This provides detailed information to assist the user in their selection and improve their satisfaction.

[0067] The suggestion unit can use the emotion estimation function to suggest the most relaxing inn or activity based on the user's emotional state. For example, the suggestion unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the suggestion unit to suggest the most relaxing inn or activity. The suggestion unit also combines communication log data and emotion estimation data to suggest the most suitable inn or activity based on the user's emotional state. For example, it can suggest a place where you can relax when you are feeling high in stress. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest the most relaxing inn or activity based on past data. For example, it can re-suggest places where you found it relaxing in the past. This improves travel satisfaction by suggesting relaxing inns and activities based on the user's emotional state.

[0068] The suggestion unit can add information about local culture and history to the suggestions for recommended spots and inns to pique the user's interest. For example, the suggestion unit can add information about local culture and history to the suggestions for recommended spots and inns. For example, it can provide information such as the history of tourist destinations, traditional events, and local specialty dishes. The suggestion unit also makes suggestions that pique the user's interest based on the information about local culture and history. For example, it can suggest plans to visit historical buildings and cultural heritage sites. The suggestion unit also updates the information about local culture and history in the suggestions for recommended spots and inns in real time to provide the user with the latest information. For example, it can display information about local events and festivals. In this way, adding information about local culture and history piques the user's interest and improves their satisfaction with the trip.

[0069] The suggestion unit can introduce hidden attractions and local restaurants around the proposed spot or inn. For example, the suggestion unit builds a system that introduces hidden attractions and local restaurants around the proposed spot or inn. For example, it can suggest attractions and popular restaurants that only locals know about. The suggestion unit also collects information about the area around the proposed spot or inn and provides it to the user. For example, it can display information about hidden attractions and local restaurants near tourist spots. The suggestion unit also updates information about hidden attractions and local restaurants around the proposed spot or inn in real time to provide the user with the latest information. For example, it can display information about newly opened restaurants and events. This enriches the user's travel experience by introducing hidden attractions and local restaurants.

[0070] The suggestion unit can use the emotion estimation function to suggest spots and inns that fit a specific theme based on the user's emotions. For example, the suggestion unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the suggestion unit to suggest spots and inns that fit a specific theme. The suggestion unit also combines communication log data and emotion estimation data to suggest optimal spots and inns based on the user's emotional state. For example, the suggestion unit can suggest active spots to a user seeking adventure. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest spots and inns that fit a specific theme based on past data. For example, the suggestion unit can suggest a quiet inn to a user seeking relaxation. This improves travel satisfaction by suggesting spots and inns that fit a specific theme based on the user's emotions.

[0071] The suggestion unit can include activities that take into account the user's health condition and physical strength in the itinerary suggested by the generation AI. For example, the suggestion unit can suggest appropriate activities by having the generation AI take into account the user's health condition and physical strength. For example, it can suggest a light walking course for a user who is not confident in their physical strength. The suggestion unit can also have the generation AI suggest optimal activities based on the user's health data. For example, it can analyze heart rate and step count data to suggest a reasonable itinerary. The suggestion unit can also have the generation AI monitor the user's health condition in real time and adjust activities based on that data. For example, it can suggest a break if the user appears tired. This makes it possible to travel without straining yourself by including activities that take into account the user's health condition and physical strength.

[0072] The suggestion unit can use the emotion estimation function to suggest the most enjoyable travel itinerary based on the user's emotional state. For example, the suggestion unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the suggestion unit to suggest the most enjoyable travel itinerary. The suggestion unit also combines communication log data and emotion estimation data to suggest an optimal travel itinerary based on the user's emotional state. For example, if the user is highly stressed, it can suggest a relaxing itinerary. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest the most enjoyable travel itinerary based on past data. For example, it can re-suggest activities that the user enjoyed in the past. This allows the suggestion unit to suggest the most enjoyable travel itinerary based on the user's emotional state, thereby improving travel satisfaction.

[0073] The suggestion unit can incorporate the opinions of local guides and experts when proposing travel itineraries. For example, the generation AI collects the opinions of local guides and experts and proposes travel itineraries based on them. For example, local recommended spots and hidden gems can be included. The suggestion unit also analyzes reviews from local guides and experts to propose the optimal travel itinerary for the user. For example, it can provide information that is not included in guidebooks. The suggestion unit also works with local guides and experts to collect the latest information in real time and proposes travel itineraries based on that information. For example, it can include information about local events. This makes it possible to propose more fulfilling travel itineraries by incorporating the opinions of local guides and experts.

[0074] The suggestion unit can add local events and workshops that the user can participate in to the proposed itinerary. For example, the generation AI collects information on local events and workshops and suggests travel itineraries based on that information. For example, local festivals and cultural experiences can be included. The suggestion unit also suggests local events and workshops based on the user's interests and concerns. For example, cooking classes and traditional craft experiences can be included. The suggestion unit also collects local event information in real time and suggests the optimal timing for the user to participate. For example, it can suggest itineraries that coincide with the date and time of the event. This enriches the travel experience by adding local events and workshops that the user can participate in.

[0075] The suggestion unit can use the emotion estimation function to suggest a travel itinerary that matches a specific theme based on the user's emotions. For example, the suggestion unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the suggestion unit to suggest a travel itinerary that matches a specific theme. The suggestion unit also combines communication log data and emotion estimation data to suggest an optimal travel itinerary based on the user's emotional state. For example, the suggestion unit can suggest an active itinerary to a user seeking adventure. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest a travel itinerary that matches a specific theme based on past data. For example, the suggestion unit can suggest a quiet itinerary to a user seeking relaxation. This improves travel satisfaction by suggesting a travel itinerary that matches a specific theme based on the user's emotions.

[0076] The reservation unit can reflect the user's past reservation history and make more personalized suggestions. For example, the reservation system analyzes the user's past reservation history and makes personalized suggestions based on that data. For example, it can take into account accommodations and means of transportation used in the past. The reservation unit can also suggest similar accommodations and means of transportation based on the user's past reservation history. For example, it can prioritize suggestions based on hotel chains and airlines that the user has preferred in the past. The reservation system can also update the user's past reservation history in real time and make personalized suggestions based on the latest information. For example, it can reflect recent reservation history. In this way, by reflecting past reservation history, more personalized suggestions are possible.

[0077] The reservation unit can integrate the user's payment method and point system to improve convenience. The reservation unit, for example, integrates the user's payment method into the reservation system to improve convenience. For example, it can manage credit cards, electronic money, and point systems all at once. The reservation unit also integrates the user's point system into the reservation system to enable reservations using points. For example, points can be used to get discounts on accommodation fees. The reservation unit also allows the reservation system to update the user's payment method and point system in real time, improving convenience based on the latest information. For example, it can display the point balance and expiration date. In this way, integrating the payment method and point system improves user convenience.

[0078] The reservation unit can use the emotion estimation function to suggest the least stressful reservation procedure based on the user's emotional state. For example, the reservation unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the reservation unit to suggest the least stressful reservation procedure. The reservation unit also combines communication log data and emotion estimation data to suggest the optimal reservation procedure based on the user's emotional state. For example, if stress is high, a simple procedure can be suggested. The reservation unit also uses the emotion estimation function to analyze the user's emotional state and suggest the least stressful reservation procedure based on past data. For example, it can re-suggest a procedure that worked smoothly in the past. This improves the convenience of reservations by suggesting the least stressful reservation procedure based on the user's emotional state.

[0079] The reservation unit can add a purchasing option for local specialties and souvenirs to the bulk reservation system. The reservation unit, for example, adds a purchasing option for local specialties and souvenirs to the bulk reservation system. For example, it can make it possible to purchase local specialties and handmade souvenirs when making a reservation. The reservation unit also provides information on specialties and souvenirs that users can purchase at their travel destinations and adds options that can be purchased together when making a reservation. For example, it can make it possible to reserve local specialties in advance. The reservation unit also updates information on local specialties and souvenirs in real time in the bulk reservation system to provide users with the latest information. For example, it can display newly released specialties and limited edition souvenirs. In this way, adding the purchasing option for local specialties and souvenirs enriches the user's travel experience.

[0080] The reservation unit can add a reservation option for local tours and activities that users can participate in to the reservation system. The reservation unit, for example, adds a reservation option for local tours and activities to a bulk reservation system. For example, it can enable reservations for sightseeing tours and activities to be made in bulk. The reservation unit also provides information on local tours and activities that users can participate in at their travel destinations and adds an option that can be booked together when making a reservation. For example, it can enable users to reserve a local guided tour in advance. The reservation unit also allows the bulk reservation system to update information on local tours and activities in real time to provide users with the latest information. For example, it can display newly added tours and activities. In this way, adding reservation options for local tours and activities enriches the user's travel experience.

[0081] The reservation unit can use the emotion estimation function to suggest reservation options based on a specific theme based on the user's emotions. For example, the reservation unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the reservation unit to suggest reservation options based on a specific theme. The reservation unit also combines communication log data and emotion estimation data to suggest optimal reservation options based on the user's emotional state. For example, an active tour can be suggested for a user seeking adventure. The reservation unit also uses the emotion estimation function to analyze the user's emotional state and suggest reservation options based on a specific theme based on past data. For example, quiet accommodations can be suggested for a user seeking relaxation. This improves travel satisfaction by suggesting reservation options based on a specific theme based on the user's emotions.

[0082] The suggestion unit can reflect the user's past travel history and preferences in the information provided by the generation AI. For example, the suggestion unit analyzes the user's past travel history and provides information to improve the comfort of the trip based on that data. For example, it can suggest accommodations and restaurants that the user has previously preferred. The suggestion unit also analyzes the user's past travel history and reviews to provide optimal information to reflect the user's preferences. For example, it can include tourist spots and activities that the user likes. The suggestion unit also suggests similar travel plans based on the user's past travel history. For example, it can suggest tourist spots with a similar atmosphere to places visited in the past. This enables more personalized suggestions by reflecting the user's past travel history and preferences.

[0083] The suggestion unit can use the emotion estimation function to suggest the most relaxing activities and restaurants based on the user's emotional state. For example, the suggestion unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the suggestion unit to suggest the most relaxing activities and restaurants. The suggestion unit also combines communication log data and emotion estimation data to suggest optimal activities and restaurants based on the user's emotional state. For example, it can suggest places where you can relax when you are under high stress. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest the most relaxing activities and restaurants based on past data. For example, it can re-suggest places where you found relaxation in the past. This improves travel satisfaction by suggesting relaxing activities and restaurants based on the user's emotional state.

[0084] The suggestion unit can incorporate the opinions of local guides and experts to improve the comfort of the trip. For example, the generation AI collects the opinions of local guides and experts and provides information to improve the comfort of the trip based on that. For example, the suggestion unit can include recommended local spots and hidden gems. The suggestion unit also analyzes reviews from local guides and experts to provide the most suitable information to the user. For example, it can provide information that is not included in guidebooks. The suggestion unit also works with local guides and experts to collect the latest information in real time and provides information to improve the comfort of the trip based on that information. For example, it can include information about local events. This improves the comfort of the trip by incorporating the opinions of local guides and experts.

[0085] The suggestion unit can add local events and workshops that the user can participate in to improve the comfort of the trip. For example, the generation AI collects information on local events and workshops and provides information to improve the comfort of the trip based on that information. For example, local festivals and cultural experiences can be included. The suggestion unit also suggests local events and workshops based on the user's interests. For example, cooking classes and traditional craft experiences can be included. The suggestion unit also collects local event information in real time and suggests the optimal timing for the user to participate. For example, it can suggest a trip that matches the date and time of the event. This improves the comfort of the trip by adding local events and workshops that the user can participate in.

[0086] The suggestion unit can use the emotion estimation function to suggest activities and restaurants that fit a specific theme based on the user's emotions. For example, the suggestion unit uses the emotion estimation function to analyze the user's current emotional state in real time. This allows the suggestion unit to suggest activities and restaurants that fit a specific theme. The suggestion unit also combines communication log data and emotion estimation data to suggest optimal activities and restaurants based on the user's emotional state. For example, the suggestion unit can suggest active activities to a user seeking adventure. The suggestion unit also uses the emotion estimation function to analyze the user's emotional state and suggest activities and restaurants that fit a specific theme based on past data. For example, the suggestion unit can suggest quiet restaurants to a user seeking relaxation. This improves travel satisfaction by suggesting activities and restaurants that fit a specific theme based on the user's emotions.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The suggestion unit can monitor the user's health condition and suggest an appropriate travel plan. For example, the suggestion unit can analyze the user's heart rate and step count data to suggest a reasonable sightseeing route. The suggestion unit can also suggest rest points and snacks based on the user's health condition. Furthermore, the suggestion unit can suggest activities that include an appropriate amount of exercise based on the user's health data. This allows the user to enjoy their trip while maintaining their health.

[0089] The suggestion unit can suggest similar travel plans based on the user's past travel history. For example, it can suggest spots with a similar atmosphere to tourist spots that the user has visited in the past. The suggestion unit can also create new travel plans based on activities and restaurants that the user has previously preferred. Furthermore, the suggestion unit can analyze the user's past travel history and suggest optimal accommodations and transportation options. This allows the user to enjoy a new trip that makes use of their past travel experience.

[0090] The suggestion unit can propose travel plans that incorporate the opinions of local guides and experts. For example, it can suggest hidden attractions and restaurants recommended by local guides. The suggestion unit can also suggest the most suitable tourist spots and activities for users based on reviews by experts. Furthermore, the suggestion unit can work with local guides and experts to collect the latest information in real time and propose travel plans based on that information. This allows users to enjoy a fulfilling trip that makes use of local knowledge.

[0091] The suggestion unit can suggest the most relaxing inn or activity based on the user's emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state in real time and suggest a place where the user can relax. The suggestion unit can also combine communication log data and emotion estimation data to suggest the most suitable inn or activity based on the user's emotional state. Furthermore, the suggestion unit can re-suggest places where the user was able to relax based on past data. This allows the user to enjoy the optimal trip according to their emotional state.

[0092] The suggestion unit can suggest travel plans based on specific themes based on the user's emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state in real time and suggest active spots for users seeking adventure. The suggestion unit can also combine communication log data and emotion estimation data to suggest optimal spots and inns based on the user's emotional state. Furthermore, the suggestion unit can suggest travel plans based on themes that the user enjoys based on past data. This allows the user to enjoy a trip based on a theme that suits their emotional state.

[0093] The suggestion unit can suggest a travel plan that takes into account the user's health condition. For example, the suggestion unit can analyze the user's heart rate and step count data to suggest a reasonable sightseeing route. The suggestion unit can also suggest rest points and snacks based on the user's health condition. Furthermore, the suggestion unit can suggest activities that include an appropriate amount of exercise based on the user's health data. This allows the user to enjoy their trip while maintaining their health.

[0094] The suggestion unit can suggest similar travel plans based on the user's past travel history. For example, it can suggest spots with a similar atmosphere to tourist spots that the user has visited in the past. The suggestion unit can also create new travel plans based on activities and restaurants that the user has previously preferred. Furthermore, the suggestion unit can analyze the user's past travel history and suggest optimal accommodations and transportation options. This allows the user to enjoy a new trip that makes use of their past travel experience.

[0095] The suggestion unit can propose travel plans that incorporate the opinions of local guides and experts. For example, it can suggest hidden attractions and restaurants recommended by local guides. The suggestion unit can also suggest the most suitable tourist spots and activities for users based on reviews by experts. Furthermore, the suggestion unit can work with local guides and experts to collect the latest information in real time and propose travel plans based on that information. This allows users to enjoy a fulfilling trip that makes use of local knowledge.

[0096] The suggestion unit can suggest the most relaxing inn or activity based on the user's emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state in real time and suggest a place where the user can relax. The suggestion unit can also combine communication log data and emotion estimation data to suggest the most suitable inn or activity based on the user's emotional state. Furthermore, the suggestion unit can re-suggest places where the user was able to relax based on past data. This allows the user to enjoy the optimal trip according to their emotional state.

[0097] The suggestion unit can suggest travel plans based on specific themes based on the user's emotional state. For example, the emotion estimation function can be used to analyze the user's current emotional state in real time and suggest active spots for users seeking adventure. The suggestion unit can also combine communication log data and emotion estimation data to suggest optimal spots and inns based on the user's emotional state. Furthermore, the suggestion unit can suggest travel plans based on themes that the user enjoys based on past data. This allows the user to enjoy a trip based on a theme that suits their emotional state.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The communication log analysis unit analyzes the mobile phone communication log. For example, it analyzes communication logs such as call history, message history, and data traffic volume to understand the population mobility rate. It can also analyze data on people's movements during specific time periods or in specific areas. Step 2: The proposal unit makes travel suggestions based on the population mobility rate analyzed by the communication log analysis unit. For example, it suggests recommended tourist spots and inns based on the user's desired travel conditions and budget. It can also use generation AI to suggest travel itineraries that meet the user's preferences. Step 3: The reservation unit makes a bulk reservation based on the travel itinerary proposed by the proposal unit. For example, it can make a bulk reservation for admission tickets to tourist spots, accommodation reservations at hotels, and transportation arrangements. If the user agrees with the proposal, they can make a bulk reservation through the system.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0106] The 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.

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

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

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

[0110] Fig. 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.

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

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

[0113] In the 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.

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

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0117] The data processing system 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0121] The 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.

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

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 AI 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 AI 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 communication log analysis unit that analyzes a communication log of a mobile phone; a suggestion unit that proposes travel plans based on the population mobility rate analyzed by the communication log analysis unit; a reservation unit that makes a lump-sum reservation based on the itinerary of the travel proposed by the proposal unit. A system characterized by:

2. The communication log analysis unit Predicting crowds at specific tourist spots and suggesting optimal visit times to users 2. The system of claim 1.

3. The communication log analysis unit Analyzing the seasonal popularity of specific tourist destinations and suggesting the best time to visit 2. The system of claim 1.

4. The communication log analysis unit Analyzes past visitor sentiment data to suggest tourist destinations that will provide users with the most positive experience 2. The system of claim 1.

5. The communication log analysis unit Predicting urban traffic congestion and suggesting optimal travel routes to users 2. The system of claim 1.

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