System

Abstract

To provide a system capable of centrally supporting a series of processes on accommodation bookings.SOLUTION: A system according to an embodiment comprises a search unit, a provision unit, a support unit, and a proposal unit. The search unit searches rooms in a plurality of accommodation facilities for available rooms. The provision unit provides price comparison information and privilege information of rooms. The support unit supports confirmation and changes of room reservation. The proposal unit proposes nearby tourist information and activities.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] With conventional technology, when booking accommodation, there is no centralized way to search for available rooms, compare prices, receive special offers, confirm or change reservations, or receive information on nearby tourist attractions and activities, leaving room for improvement.

[0005] The system according to the embodiment aims to provide integrated support for a series of processes related to booking accommodations. [Means for solving the problem]

[0006] The system according to the embodiment includes a search unit, a provision unit, a support unit, and a suggestion unit. The search unit searches for available rooms from among rooms at multiple accommodation facilities. The provision unit provides price comparison information and special offer information for rooms. The support unit supports confirmation and changes to room reservations. The suggestion unit suggests tourist information and activities in the surrounding area. [Effects of the Invention]

[0007] The system according to the embodiment can centrally support a series of processes related to booking accommodations. [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) An AI assistant system for hotel and inn reservations according to an embodiment of the present invention searches for available rooms at multiple accommodations, provides price comparisons and special offers, supports reservation confirmation and changes, and suggests nearby tourist information and activities. This system can enhance a user's travel experience by suggesting optimal accommodations and travel plans based on the user's preferences and history. For example, the search unit searches for available rooms for the user based on the user's preferences and accommodation history. For a user who has previously preferred luxury hotels, similar luxury hotel rooms are preferentially displayed as search results. The provision unit provides the user with price comparison information and special offers for the rooms searched by the search unit. For example, the system presents the user with the rooms with the lowest prices and special offers based on information collected from multiple reservation sites. The support unit supports reservations and changes for the rooms searched by the search unit. For example, if a user wants to change a reservation, the support unit responds in real time and handles the change procedure. The suggestion unit suggests tourist information and activities near the rooms searched by the search unit. For example, the system provides a virtual tour of tourist spots and activities near the accommodation and suggests travel plans to the user. Furthermore, the suggestion unit can suggest health-conscious travel plans based on the user's genetic information, or use AI to analyze the user's stress level and recommend accommodations and activities that are effective in reducing stress. In this way, the system of the present invention can improve the user's travel experience by considering the user's preferences and history and suggesting optimal accommodations and travel plans. This allows the hotel and inn reservation support AI assistant system to improve the user's travel experience by considering the user's preferences and history and suggesting optimal accommodations and travel plans.

[0029] An AI assistant system for supporting reservations at hotels and inns according to an embodiment includes a search unit, a provision unit, a support unit, and a suggestion unit. The search unit searches for available rooms from among rooms at multiple accommodation facilities. The search unit searches for available rooms for a user based on, for example, the user's preferences and accommodation history. For example, for a user who has previously preferred luxury hotels, similar luxury hotel rooms are preferentially displayed as search results. The provision unit provides price comparison information and special offer information for the rooms searched by the search unit. For example, the provision unit presents the user with the room with the lowest price and special offer based on information collected from multiple reservation sites. The support unit supports reservations and changes to the rooms searched by the search unit. For example, if the user wishes to change a reservation, the support unit responds in real time and handles the change procedure. The suggestion unit suggests tourist information and activities near the room searched by the search unit. For example, the suggestion unit provides a virtual tour of tourist spots and activities near the accommodation and suggests a travel plan to the user. As a result, the hotel and inn reservation support AI assistant system according to the embodiment can improve the user's travel experience by taking into account the user's preferences and history and proposing optimal accommodations and travel plans.

[0030] The search unit searches for available rooms from multiple accommodations. The search unit searches for available rooms for a user based on, for example, the user's preferences and accommodation history. Specifically, the search unit references a database of accommodations the user has used in the past and searches for the most suitable room, taking into account the user's preferences, budget, length of stay, and other conditions. For example, if a user has previously preferred luxury hotels, similar luxury hotel rooms will be displayed as search results. Furthermore, the search unit uses AI to analyze the user's search history and ratings to predict accommodations that the user is likely to prefer. For example, if a user has previously preferred a specific area or hotel chain, the search unit can suggest new options in that area or hotel chain. The search unit also checks accommodation availability in real time and updates search results based on the latest information. This allows the user to always select accommodations based on the most up-to-date information. Furthermore, the search unit also has the ability to filter search results based on the user's specific requests and conditions. For example, it can search for rooms tailored to the user's needs, such as non-smoking rooms, pet-friendly rooms, or rooms with specific amenities. This allows the search unit to meet the diverse needs of users and quickly and accurately provide the most suitable accommodations.

[0031] The provision unit provides price comparison information and special offer information for rooms searched by the search unit. For example, the provision unit presents the user with the lowest price and special offers based on information collected from multiple reservation sites. Specifically, the provision unit uses the APIs of each reservation site to collect and compare price information for the same accommodation facility and room type. This allows the user to compare prices from multiple reservation sites at a glance and make the most cost-effective choice. The provision unit also provides special offer information. For example, the provision unit collects special offer information such as free breakfast, spa vouchers, and point rewards that are only available on specific reservation sites and presents it to the user. This allows the user to make the optimal reservation by considering not only price but also special offers. Furthermore, the provision unit can evaluate the reliability and satisfaction of specific reservation sites and accommodations based on the user's past reservation history and ratings, and recommend them to the user. For example, preferentially displaying reservation sites and accommodations that have received high ratings in the past can improve user satisfaction. This allows the provision unit to provide the user with the most advantageous prices and special offer information, thereby improving the user's reservation experience.

[0032] The support unit supports reservations and changes to rooms searched by the search unit. For example, if a user wants to change a reservation, the support unit responds in real time and handles the change procedure. Specifically, the support unit provides an interface that simplifies the reservation process for users and ensures smooth progress. For example, the support unit is designed so that the reservation process can be completed simply by the user entering the desired accommodation, room type, and length of stay. The support unit also quickly handles reservation changes and cancellations. For example, if a user needs to change the date of their stay due to a sudden change in plans, the support unit checks room availability in real time and handles the change procedure. Furthermore, if a user has questions or issues regarding their reservation, the support unit quickly responds through a chatbot or customer support. For example, the support unit provides support tailored to the user's needs, such as reissuing a reservation confirmation or responding to specific requests. This allows the support unit to provide an environment where users can complete the reservation process with peace of mind and respond flexibly as needed.

[0033] The suggestion unit suggests tourist information and activities around the room searched by the search unit. For example, the suggestion unit provides a virtual tour of tourist spots and activities around the accommodation and proposes a travel plan to the user. Specifically, the suggestion unit collects information on tourist spots, restaurants, and activities around the accommodation and provides it to the user. For example, the suggestion unit suggests information tailored to the user's interests and needs, such as tourist spots within walking distance of the accommodation, restaurants serving local specialties, and activities popular with families. The suggestion unit also allows the user to experience the atmosphere of tourist spots and activities in advance through virtual tours. For example, the suggestion unit can provide virtual tours using 360-degree cameras or aerial footage using drone footage, allowing the user to get an idea of ​​the location before actually visiting. Furthermore, the suggestion unit can propose individually customized travel plans based on the user's past travel history and reviews. For example, if a user has previously preferred a particular activity, the suggestion unit can propose a travel plan including similar activities. This allows the suggestion unit to provide attractive travel plans to the user and enhance their travel experience.

[0034] The search unit can search for rooms available to the user based on the user's preferences and accommodation history. The search unit can search for rooms available to the user based on, for example, the user's preferences and accommodation history. For example, for a user who has previously preferred luxury hotels, rooms at similar luxury hotels are preferentially displayed as search results. This allows the optimal room to be searched for based on the user's preferences and history. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can search for rooms using an AI model that inputs the user's preferences and accommodation history and outputs available rooms.

[0035] The providing unit can provide the user with price comparison information and special offer information for rooms searched by the search unit. For example, based on information collected from multiple reservation sites, the unit presents the user with rooms with the lowest prices and special offers. This allows the user to be provided with the optimal price and special offer information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives price comparison information and special offer information for rooms searched by the search unit as input and outputs information to be provided to the user.

[0036] The AI ​​assistant system for supporting reservations at hotels and inns includes a collection unit that collects information from multiple reservation sites, and a provision unit that can provide price comparison information across multiple reservation sites for rooms searched by a search unit. The collection unit, for example, collects information from multiple reservation sites. For example, the collection unit collects information from travel agency websites and official hotel websites. The provision unit, for example, provides price comparison information for rooms searched by the search unit based on the information collected by the collection unit. For example, the provision unit presents the user with the lowest price or rooms with the best deals based on a comparison of rates under the same conditions and whether or not discounts are applied. This allows optimal price comparison information to be provided based on information from multiple reservation sites. Some or all of the above-described processing by the collection unit and provision unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can receive information from multiple reservation sites, and the provision unit can provide the information using an AI model that outputs price comparison information.

[0037] The support unit supports reservations and changes to rooms searched by the search unit, and the suggestion unit can suggest tourist information and activities around the rooms searched by the search unit. The support unit, for example, supports reservations and changes to rooms searched by the search unit. For example, if a user wants to change a reservation, the support unit responds in real time and processes the change. The suggestion unit, for example, suggests tourist information and activities around the rooms searched by the search unit. For example, a virtual tour of tourist spots and activities around the accommodation is provided, and a travel plan is proposed to the user. This enables support for reservations and changes and the suggestion of tourist information. Some or all of the above-described processing in the support unit and suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input reservation and change information, and the suggestion unit can provide information using an AI model that outputs tourist information and activities.

[0038] The suggestion unit can provide a virtual tour of accommodations with rooms searched for by the search unit and tourist attractions around the accommodations. For example, visual information can be provided to the user using 360-degree camera footage or VR technology. This allows the virtual tour to provide visual information to the user. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without AI. For example, the suggestion unit can provide information using an AI model that inputs virtual tour information and outputs visual information.

[0039] The suggestion unit can provide security information about accommodation facilities that have rooms searched for by the search unit and the surrounding area of ​​the accommodation facilities. For example, it can provide information about crime rates and police station locations. By providing security information, the safety of the user can be ensured. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can provide information using an AI model that receives security information as input and outputs information to be provided to the user.

[0040] The support department can respond to user questions in real time via an interactive chatbot. For example, a chatbot available 24 hours a day can be provided using natural language processing technology. This allows for real-time responses to user questions. Some or all of the above-described processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can respond using an AI model that takes user questions as input and outputs answers in real time.

[0041] The suggestion unit can cooperate with the concierge service of the accommodation facility that has the room searched by the search unit to support special requests. For example, the suggestion unit can handle special requests and make reservations on behalf of the accommodation facility. This allows the special request to be accommodated. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the special request and cooperate with the concierge service to provide support using a corresponding AI model.

[0042] The suggestion unit can suggest a health-conscious travel plan based on the user's genetic information. For example, the suggestion unit can suggest an optimal travel plan for the user based on genetic test results and health checkup data. This allows a health-conscious travel plan to be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a health-conscious travel plan using an AI model that inputs genetic information and outputs it.

[0043] The suggestion unit can use AI to analyze the user's stress level and recommend accommodations and activities that are effective in reducing stress. For example, the suggestion unit can recommend optimal accommodations and activities for the user based on heart rate fluctuations and survey results. This makes it possible to recommend accommodations and activities that are effective in reducing stress. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can make recommendations using an AI model that inputs stress level data and outputs accommodations and activities that are effective in reducing stress.

[0044] The search unit can analyze the user's past accommodation history and search for the optimal room based on a specific season or event. For example, if the user has previously stayed at a seaside resort in the summer, the search will prioritize summer seaside resorts. If the user has previously stayed at a city hotel at Christmas, the search will prioritize city hotels during the Christmas season. If the user has previously stayed at a mountain hot spring inn in the fall, the search will prioritize autumn mountain hot spring inns. This makes it possible to search for the optimal room based on a specific season or event. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can search for a room using an AI model that inputs past accommodation history and outputs the optimal room based on a specific season or event.

[0045] During a search, the search unit can prioritize displaying appropriate accommodations based on the user's current health condition and physical condition. For example, if the user is tired, accommodations with extensive relaxation facilities can be prioritized. If the user is looking for healthy meals, accommodations that offer healthy meals can be prioritized. If the user wants to exercise, accommodations with extensive fitness facilities can be prioritized. This makes it possible to display optimal accommodations based on the user's health condition and physical condition. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can display accommodations using an AI model that inputs the user's health condition and physical condition data and outputs appropriate accommodations.

[0046] When searching, the search unit can prioritize displaying nearby accommodations, taking into account the user's geographical location information. For example, the search unit can prioritize displaying accommodations close to the user's current location. The search unit can prioritize displaying accommodations close to a specific tourist destination for the user. The search unit can prioritize displaying accommodations close to a specific event venue for the user. This makes it possible to display optimal accommodations based on the user's geographical location information. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can display accommodations using an AI model that inputs the user's geographical location information and outputs nearby accommodations.

[0047] During a search, the search unit can analyze the user's social media activity and suggest relevant accommodations. For example, it can suggest accommodations related to travel destinations the user has shared on social media. It can suggest accommodations near tourist spots the user has "liked" on social media. It can suggest accommodations recommended by travel bloggers the user follows on social media. This makes it possible to suggest optimal accommodations based on the user's social media activity. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can suggest accommodations using an AI model that takes the user's social media activity data as input and outputs relevant accommodations.

[0048] The provision unit can customize the benefit information based on the user's past reservation history when providing the benefit information. For example, similar benefit information can be provided based on benefits the user has used in the past. Benefit information for accommodations that the user has used in the past can be provided preferentially. Optimal benefit information can be provided based on the frequency with which the user has used benefits in the past. This makes it possible to provide optimal benefit information based on the user's past reservation history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide benefit information using an AI model that uses past reservation history as input and customizes benefit information.

[0049] At the time of providing, the providing unit can present optimal price information based on the user's current budget situation. For example, present the cheapest accommodation within the budget set by the user. Present accommodation with benefits according to the user's budget. Present accommodation with high cost performance according to the user's budget. This makes it possible to provide optimal price information according to the user's budget. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can present price information using an AI model that inputs the user's budget situation and outputs optimal price information.

[0050] When providing the information, the providing unit can prioritize displaying region-specific special offer information, taking into account the user's geographical location information. For example, the providing unit can display region-specific special offer information close to the user's current location. The providing unit can display region-specific special offer information close to a specific tourist spot the user is visiting. The providing unit can display region-specific special offer information close to a specific event venue the user is visiting. This allows optimal special offer information to be provided based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide special offer information using an AI model that receives the user's geographical location information as input and outputs region-specific special offer information.

[0051] At the time of providing, the providing unit can analyze the user's social media activity and provide related reward information. For example, the providing unit can provide reward information related to travel destinations shared by the user on social media. The providing unit can provide reward information related to tourist spots that the user has "liked" on social media. The providing unit can provide reward information recommended by travel bloggers that the user follows on social media. This makes it possible to provide optimal reward information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide reward information using an AI model that inputs the user's social media activity data and outputs related reward information.

[0052] When providing support, the support unit can select the optimal support method by referring to the user's past reservation change history. For example, it can prioritize providing reservation change methods that the user has frequently used in the past. It can prioritize providing reservation change methods for accommodations that the user has used in the past. It can suggest the optimal support method based on the user's past reservation change history. This makes it possible to provide the optimal support method based on the user's past reservation change history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide a support method using an AI model that inputs past reservation change history and selects the optimal support method.

[0053] During support, the support unit can suggest the timing of rescheduling a reservation based on the user's current schedule. For example, the support unit can refer to the user's calendar information to suggest the optimal timing for rescheduling a reservation. The support unit can provide a reservation rescheduling reminder based on the user's schedule. The support unit can automatically set the optimal timing for rescheduling a reservation based on the user's schedule. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can suggest the timing using an AI model that inputs the user's schedule data and suggests the timing for rescheduling a reservation.

[0054] When providing support, the support unit can provide the optimal support method by taking into account the user's geographical location information. For example, the support unit can prioritize providing a support center that is close to the user's current location. The support unit can prioritize providing a support center that is close to a specific tourist attraction for the user. The support unit can prioritize providing a support center that is close to a specific event venue for the user. This allows optimal support to be provided based on the user's geographical location information. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can provide support using an AI model that inputs the user's geographical location information and outputs the optimal support method.

[0055] During support, the support unit can analyze the user's social media activity and provide relevant support information. For example, the support unit can provide support information related to travel destinations shared by the user on social media, support information related to tourist spots that the user has "liked" on social media, or support information recommended by travel bloggers the user follows on social media. This allows the most appropriate support information to be provided based on the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide support information using an AI model that inputs the user's social media activity data and outputs relevant support information.

[0056] When making a suggestion, the suggestion unit can provide optimal tourist information by referring to the user's past travel history. For example, it can provide tourist information related to tourist spots that the user has visited in the past. It can provide information related to sightseeing tours that the user has taken in the past. It can suggest optimal tourist spots based on the user's past travel history. This makes it possible to provide optimal tourist information based on the user's past travel history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that takes past travel history as input and outputs optimal tourist information.

[0057] When making a suggestion, the suggestion unit can customize tourist information based on the user's current interests and concerns. For example, the suggestion unit can provide tourist information related to a topic that the user is currently interested in. The suggestion unit can customize tourist information based on keywords recently searched by the user. The suggestion unit can provide tourist information related to accounts the user follows on social media. This makes it possible to provide optimal tourist information based on the user's current interests and concerns. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that uses the user's interest and concern data as input and customizes the tourist information.

[0058] When making a suggestion, the suggestion unit can prioritize displaying nearby tourist information, taking into account the user's geographical location information. For example, tourist spots close to the user's current location can be prioritized. Tourist information close to a specific tourist spot for the user can be prioritized. Tourist information close to a specific event venue for the user can be prioritized. This makes it possible to provide optimal tourist information based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that inputs the user's geographical location information and outputs nearby tourist information.

[0059] When making a suggestion, the suggestion unit can analyze the user's social media activities and provide relevant tourist information. For example, it can provide tourist information related to travel destinations shared by the user on social media. It can provide tourist information related to tourist destinations that the user has "liked" on social media. It can provide tourist information recommended by travel bloggers that the user follows on social media. This makes it possible to provide optimal tourist information based on the user's social media activities. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that inputs the user's social media activity data and outputs relevant tourist information.

[0060] When collecting data, the collection unit can select the optimal information collection method by referring to the user's past search history. For example, the collection unit collects information based on keywords that the user has frequently searched for in the past. The collection unit prioritizes collecting information sources that the user has used in the past. The collection unit suggests the optimal information collection method based on the user's past search history. This makes it possible to provide the optimal information collection method based on the user's past search history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that uses past search history as input and selects the optimal information collection method.

[0061] When collecting data, the collection unit can prioritize collecting highly relevant information, taking into account the user's current location information. For example, the collection unit prioritizes collecting information close to the user's current location. The user prioritizes collecting information close to a specific tourist spot. The user prioritizes collecting information close to a specific event venue. This allows optimal information to be collected based on the user's current location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that inputs the user's current location information and outputs highly relevant information.

[0062] When collecting data, the collection unit can prioritize collecting highly relevant information, taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information close to the user's current location. The user prioritizes collecting information close to a specific tourist spot. The user prioritizes collecting information close to a specific event venue. This allows optimal information to be collected based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that inputs the user's geographical location information and outputs highly relevant information.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The provider can also analyze the user's past activity history at travel destinations and suggest similar activities. For example, if the user has enjoyed skydiving in the past, activities such as bungee jumping and paragliding can be suggested. If the user has enjoyed visiting art museums in the past, local art museums and galleries can be suggested. If the user has enjoyed hiking in the past, local trekking courses can be suggested. This makes it possible to suggest optimal activities based on the user's past activity history.

[0065] The suggestion unit can also monitor the user's current health condition and suggest health-conscious activities. For example, if the user is tired, it can suggest a relaxing massage or a hot spring. If the user feels they are lacking exercise, it can suggest a yoga class or a fitness gym. If the user is looking for healthy food, it can suggest healthy restaurants or organic cafes. This allows the system to suggest optimal activities based on the user's health condition.

[0066] The suggestion unit can also analyze the user's dining history at past travel destinations and suggest similar meal plans. For example, if the user has enjoyed Italian food in the past, a local Italian restaurant can be suggested. If the user has enjoyed Japanese food in the past, a local Japanese restaurant can be suggested. If the user has enjoyed vegetarian food in the past, a local vegetarian restaurant can be suggested. This allows the system to suggest optimal meal plans based on the user's dining history.

[0067] The suggestion unit can also customize event information for travel destinations based on the user's current interests. For example, if the user is interested in music, local concerts and live events can be suggested. If the user is interested in art, local art festivals and gallery exhibitions can be suggested. If the user is interested in sports, local sporting events and matches can be suggested. This allows the system to provide optimal event information based on the user's current interests.

[0068] The support department can also analyze the user's past trouble history and respond quickly if a similar problem occurs. For example, if the user has experienced a flight delay in the past, the support department can quickly suggest an alternative flight if a similar situation occurs. If the user has experienced trouble booking a hotel in the past, the support department can quickly suggest an alternative accommodation if a similar situation occurs. If the user has experienced lost luggage in the past, the support department can quickly provide support if a similar situation occurs. This allows the support department to provide the most appropriate support based on the user's past trouble history.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The search unit searches for available rooms from among multiple accommodation facilities. The search unit searches for available rooms for the user based on the user's preferences and accommodation history. For example, for a user who has previously preferred luxury hotels, rooms from similar luxury hotels will be preferentially displayed as search results. Step 2: The provider provides price comparison information and special offer information for the rooms searched by the search unit. Based on information collected from multiple reservation sites, the provider presents the user with the rooms with the lowest prices and special offers. Step 3: The support section supports the reservation and modification of the room searched by the search section. If the user wants to change the reservation, the support section responds in real time and carries out the modification procedure. Step 4: The suggestion unit suggests tourist information and activities around the room searched by the search unit. The suggestion unit provides a virtual tour of tourist spots and activities around the accommodation and suggests travel plans to the user.

[0071] (Example 2) An AI assistant system for hotel and inn reservations according to an embodiment of the present invention searches for available rooms at multiple accommodations, provides price comparisons and special offers, supports reservation confirmation and changes, and suggests nearby tourist information and activities. This system can enhance a user's travel experience by suggesting optimal accommodations and travel plans based on the user's preferences and history. For example, the search unit searches for available rooms for the user based on the user's preferences and accommodation history. For a user who has previously preferred luxury hotels, similar luxury hotel rooms are preferentially displayed as search results. The provision unit provides the user with price comparison information and special offers for the rooms searched by the search unit. For example, the system presents the user with the rooms with the lowest prices and special offers based on information collected from multiple reservation sites. The support unit supports reservations and changes for the rooms searched by the search unit. For example, if a user wants to change a reservation, the support unit responds in real time and handles the change procedure. The suggestion unit suggests tourist information and activities near the rooms searched by the search unit. For example, the system provides a virtual tour of tourist spots and activities near the accommodation and suggests travel plans to the user. Furthermore, the suggestion unit can suggest health-conscious travel plans based on the user's genetic information, or use AI to analyze the user's stress level and recommend accommodations and activities that are effective in reducing stress. In this way, the system of the present invention can improve the user's travel experience by considering the user's preferences and history and suggesting optimal accommodations and travel plans. This allows the hotel and inn reservation support AI assistant system to improve the user's travel experience by considering the user's preferences and history and suggesting optimal accommodations and travel plans.

[0072] An AI assistant system for supporting reservations at hotels and inns according to an embodiment includes a search unit, a provision unit, a support unit, and a suggestion unit. The search unit searches for available rooms from among rooms at multiple accommodation facilities. The search unit searches for available rooms for a user based on, for example, the user's preferences and accommodation history. For example, for a user who has previously preferred luxury hotels, similar luxury hotel rooms are preferentially displayed as search results. The provision unit provides price comparison information and special offer information for the rooms searched by the search unit. For example, the provision unit presents the user with the room with the lowest price and special offer based on information collected from multiple reservation sites. The support unit supports reservations and changes to the rooms searched by the search unit. For example, if the user wishes to change a reservation, the support unit responds in real time and handles the change procedure. The suggestion unit suggests tourist information and activities near the room searched by the search unit. For example, the suggestion unit provides a virtual tour of tourist spots and activities near the accommodation and suggests a travel plan to the user. As a result, the hotel and inn reservation support AI assistant system according to the embodiment can improve the user's travel experience by taking into account the user's preferences and history and proposing optimal accommodations and travel plans.

[0073] The search unit searches for available rooms from multiple accommodations. The search unit searches for available rooms for a user based on, for example, the user's preferences and accommodation history. Specifically, the search unit references a database of accommodations the user has used in the past and searches for the most suitable room, taking into account the user's preferences, budget, length of stay, and other conditions. For example, if a user has previously preferred luxury hotels, similar luxury hotel rooms will be displayed as search results. Furthermore, the search unit uses AI to analyze the user's search history and ratings to predict accommodations that the user is likely to prefer. For example, if a user has previously preferred a specific area or hotel chain, the search unit can suggest new options in that area or hotel chain. The search unit also checks accommodation availability in real time and updates search results based on the latest information. This allows the user to always select accommodations based on the most up-to-date information. Furthermore, the search unit also has the ability to filter search results based on the user's specific requests and conditions. For example, it can search for rooms tailored to the user's needs, such as non-smoking rooms, pet-friendly rooms, or rooms with specific amenities. This allows the search unit to meet the diverse needs of users and quickly and accurately provide the most suitable accommodations.

[0074] The provision unit provides price comparison information and special offer information for rooms searched by the search unit. For example, the provision unit presents the user with the lowest price and special offers based on information collected from multiple reservation sites. Specifically, the provision unit uses the APIs of each reservation site to collect and compare price information for the same accommodation facility and room type. This allows the user to compare prices from multiple reservation sites at a glance and make the most cost-effective choice. The provision unit also provides special offer information. For example, the provision unit collects special offer information such as free breakfast, spa vouchers, and point rewards that are only available on specific reservation sites and presents it to the user. This allows the user to make the optimal reservation by considering not only price but also special offers. Furthermore, the provision unit can evaluate the reliability and satisfaction of specific reservation sites and accommodations based on the user's past reservation history and ratings, and recommend them to the user. For example, preferentially displaying reservation sites and accommodations that have received high ratings in the past can improve user satisfaction. This allows the provision unit to provide the user with the most advantageous prices and special offer information, thereby improving the user's reservation experience.

[0075] The support unit supports reservations and changes to rooms searched by the search unit. For example, if a user wants to change a reservation, the support unit responds in real time and handles the change procedure. Specifically, the support unit provides an interface that simplifies the reservation process for users and ensures smooth progress. For example, the support unit is designed so that the reservation process can be completed simply by the user entering the desired accommodation, room type, and length of stay. The support unit also quickly handles reservation changes and cancellations. For example, if a user needs to change the date of their stay due to a sudden change in plans, the support unit checks room availability in real time and handles the change procedure. Furthermore, if a user has questions or issues regarding their reservation, the support unit quickly responds through a chatbot or customer support. For example, the support unit provides support tailored to the user's needs, such as reissuing a reservation confirmation or responding to specific requests. This allows the support unit to provide an environment where users can complete the reservation process with peace of mind and respond flexibly as needed.

[0076] The suggestion unit suggests tourist information and activities around the room searched by the search unit. For example, the suggestion unit provides a virtual tour of tourist spots and activities around the accommodation and proposes a travel plan to the user. Specifically, the suggestion unit collects information on tourist spots, restaurants, and activities around the accommodation and provides it to the user. For example, the suggestion unit suggests information tailored to the user's interests and needs, such as tourist spots within walking distance of the accommodation, restaurants serving local specialties, and activities popular with families. The suggestion unit also allows the user to experience the atmosphere of tourist spots and activities in advance through virtual tours. For example, the suggestion unit can provide virtual tours using 360-degree cameras or aerial footage using drone footage, allowing the user to get an idea of ​​the location before actually visiting. Furthermore, the suggestion unit can propose individually customized travel plans based on the user's past travel history and reviews. For example, if a user has previously preferred a particular activity, the suggestion unit can propose a travel plan including similar activities. This allows the suggestion unit to provide attractive travel plans to the user and enhance their travel experience.

[0077] The search unit can search for rooms available to the user based on the user's preferences and accommodation history. The search unit can search for rooms available to the user based on, for example, the user's preferences and accommodation history. For example, for a user who has previously preferred luxury hotels, rooms at similar luxury hotels are preferentially displayed as search results. This allows the optimal room to be searched for based on the user's preferences and history. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can search for rooms using an AI model that inputs the user's preferences and accommodation history and outputs available rooms.

[0078] The providing unit can provide the user with price comparison information and special offer information for rooms searched by the search unit. For example, based on information collected from multiple reservation sites, the unit presents the user with rooms with the lowest prices and special offers. This allows the user to be provided with the optimal price and special offer information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives price comparison information and special offer information for rooms searched by the search unit as input and outputs information to be provided to the user.

[0079] The AI ​​assistant system for supporting reservations at hotels and inns includes a collection unit that collects information from multiple reservation sites, and a provision unit that can provide price comparison information across multiple reservation sites for rooms searched by a search unit. The collection unit, for example, collects information from multiple reservation sites. For example, the collection unit collects information from travel agency websites and official hotel websites. The provision unit, for example, provides price comparison information for rooms searched by the search unit based on the information collected by the collection unit. For example, the provision unit presents the user with the lowest price or rooms with the best deals based on a comparison of rates under the same conditions and whether or not discounts are applied. This allows optimal price comparison information to be provided based on information from multiple reservation sites. Some or all of the above-described processing by the collection unit and provision unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can receive information from multiple reservation sites, and the provision unit can provide the information using an AI model that outputs price comparison information.

[0080] The support unit supports reservations and changes to rooms searched by the search unit, and the suggestion unit can suggest tourist information and activities around the rooms searched by the search unit. The support unit, for example, supports reservations and changes to rooms searched by the search unit. For example, if a user wants to change a reservation, the support unit responds in real time and processes the change. The suggestion unit, for example, suggests tourist information and activities around the rooms searched by the search unit. For example, a virtual tour of tourist spots and activities around the accommodation is provided, and a travel plan is proposed to the user. This enables support for reservations and changes and the suggestion of tourist information. Some or all of the above-described processing in the support unit and suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input reservation and change information, and the suggestion unit can provide information using an AI model that outputs tourist information and activities.

[0081] The suggestion unit can provide a virtual tour of accommodations with rooms searched for by the search unit and tourist attractions around the accommodations. For example, visual information can be provided to the user using 360-degree camera footage or VR technology. This allows the virtual tour to provide visual information to the user. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without AI. For example, the suggestion unit can provide information using an AI model that inputs virtual tour information and outputs visual information.

[0082] The suggestion unit can provide security information about accommodation facilities that have rooms searched for by the search unit and the surrounding area of ​​the accommodation facilities. For example, it can provide information about crime rates and police station locations. By providing security information, the safety of the user can be ensured. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can provide information using an AI model that receives security information as input and outputs information to be provided to the user.

[0083] The support department can respond to user questions in real time via an interactive chatbot. For example, a chatbot available 24 hours a day can be provided using natural language processing technology. This allows for real-time responses to user questions. Some or all of the above-described processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can respond using an AI model that takes user questions as input and outputs answers in real time.

[0084] The suggestion unit can cooperate with the concierge service of the accommodation facility that has the room searched by the search unit to support special requests. For example, the suggestion unit can handle special requests and make reservations on behalf of the accommodation facility. This allows the special request to be accommodated. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the special request and cooperate with the concierge service to provide support using a corresponding AI model.

[0085] The suggestion unit can suggest a health-conscious travel plan based on the user's genetic information. For example, the suggestion unit can suggest an optimal travel plan for the user based on genetic test results and health checkup data. This allows a health-conscious travel plan to be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest a health-conscious travel plan using an AI model that inputs genetic information and outputs it.

[0086] The suggestion unit can use AI to analyze the user's stress level and recommend accommodations and activities that are effective in reducing stress. For example, the suggestion unit can recommend optimal accommodations and activities for the user based on heart rate fluctuations and survey results. This makes it possible to recommend accommodations and activities that are effective in reducing stress. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can make recommendations using an AI model that inputs stress level data and outputs accommodations and activities that are effective in reducing stress.

[0087] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. For example, if the user is feeling stressed, accommodations that offer relaxation are displayed preferentially. If the user is excited, accommodations with plenty of activities are displayed preferentially. If the user is tired, accommodations with quiet environments are displayed preferentially. This allows optimal search results to be displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can adjust the display order using an AI model that uses user emotion data as input and adjusts the display order of search results.

[0088] The search unit can analyze the user's past accommodation history and search for the optimal room based on a specific season or event. For example, if the user has previously stayed at a seaside resort in the summer, the search will prioritize summer seaside resorts. If the user has previously stayed at a city hotel at Christmas, the search will prioritize city hotels during the Christmas season. If the user has previously stayed at a mountain hot spring inn in the fall, the search will prioritize autumn mountain hot spring inns. This makes it possible to search for the optimal room based on a specific season or event. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can search for a room using an AI model that inputs past accommodation history and outputs the optimal room based on a specific season or event.

[0089] During a search, the search unit can prioritize displaying appropriate accommodations based on the user's current health condition and physical condition. For example, if the user is tired, accommodations with extensive relaxation facilities can be prioritized. If the user is looking for healthy meals, accommodations that offer healthy meals can be prioritized. If the user wants to exercise, accommodations with extensive fitness facilities can be prioritized. This makes it possible to display optimal accommodations based on the user's health condition and physical condition. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can display accommodations using an AI model that inputs the user's health condition and physical condition data and outputs appropriate accommodations.

[0090] The search unit can estimate the user's emotions and adjust the filtering conditions for search results based on the estimated user emotions. For example, if the user is feeling stressed, accommodations with plenty of relaxation facilities are filtered out. If the user is excited, accommodations with plenty of activities are filtered out. If the user is tired, accommodations with quiet environments are filtered out. This allows optimal filtering conditions to be set based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can adjust the filtering conditions using an AI model that inputs user emotion data and adjusts the filtering conditions for search results.

[0091] When searching, the search unit can prioritize displaying nearby accommodations, taking into account the user's geographical location information. For example, the search unit can prioritize displaying accommodations close to the user's current location. The search unit can prioritize displaying accommodations close to a specific tourist destination for the user. The search unit can prioritize displaying accommodations close to a specific event venue for the user. This makes it possible to display optimal accommodations based on the user's geographical location information. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can display accommodations using an AI model that inputs the user's geographical location information and outputs nearby accommodations.

[0092] During a search, the search unit can analyze the user's social media activity and suggest relevant accommodations. For example, it can suggest accommodations related to travel destinations the user has shared on social media. It can suggest accommodations near tourist spots the user has "liked" on social media. It can suggest accommodations recommended by travel bloggers the user follows on social media. This makes it possible to suggest optimal accommodations based on the user's social media activity. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can suggest accommodations using an AI model that takes the user's social media activity data as input and outputs relevant accommodations.

[0093] The providing unit can estimate the user's emotions and adjust the display method of the price comparison information based on the estimated user emotions. For example, if the user is stressed, simple and easy-to-read price comparison information is displayed. If the user is relaxed, detailed price comparison information is displayed. If the user is in a hurry, the cheapest price is highlighted. This allows optimal price comparison information to be displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can adjust the display method using an AI model that uses user emotion data as input and adjusts the display method of the price comparison information.

[0094] The provision unit can customize the benefit information based on the user's past reservation history when providing the benefit information. For example, similar benefit information can be provided based on benefits the user has used in the past. Benefit information for accommodations that the user has used in the past can be provided preferentially. Optimal benefit information can be provided based on the frequency with which the user has used benefits in the past. This makes it possible to provide optimal benefit information based on the user's past reservation history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide benefit information using an AI model that uses past reservation history as input and customizes benefit information.

[0095] At the time of providing, the providing unit can present optimal price information based on the user's current budget situation. For example, present the cheapest accommodation within the budget set by the user. Present accommodation with benefits according to the user's budget. Present accommodation with high cost performance according to the user's budget. This makes it possible to provide optimal price information according to the user's budget. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can present price information using an AI model that inputs the user's budget situation and outputs optimal price information.

[0096] The providing unit can estimate the user's emotions and determine the priority of the reward information based on the estimated user's emotions. For example, if the user is feeling stressed, relaxation rewards can be displayed preferentially. If the user is excited, activity rewards can be displayed preferentially. If the user is tired, spa rewards can be displayed preferentially. This allows optimal reward information to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide reward information using an AI model that inputs the user's emotion data and determines the priority of the reward information.

[0097] When providing the information, the providing unit can prioritize displaying region-specific special offer information, taking into account the user's geographical location information. For example, the providing unit can display region-specific special offer information close to the user's current location. The providing unit can display region-specific special offer information close to a specific tourist spot the user is visiting. The providing unit can display region-specific special offer information close to a specific event venue the user is visiting. This allows optimal special offer information to be provided based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide special offer information using an AI model that receives the user's geographical location information as input and outputs region-specific special offer information.

[0098] At the time of providing, the providing unit can analyze the user's social media activity and provide related reward information. For example, the providing unit can provide reward information related to travel destinations shared by the user on social media. The providing unit can provide reward information related to tourist spots that the user has "liked" on social media. The providing unit can provide reward information recommended by travel bloggers that the user follows on social media. This makes it possible to provide optimal reward information based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide reward information using an AI model that inputs the user's social media activity data and outputs related reward information.

[0099] The support unit can estimate the user's emotions and adjust the reservation change procedure based on the estimated user emotions. For example, if the user is stressed, a simple and quick reservation change procedure can be provided. If the user is relaxed, detailed reservation change options can be provided. If the user is in a hurry, voice input can be prioritized and the reservation change procedure can be quickly performed. This allows the optimal reservation change procedure to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Examples of generative AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can adjust the procedure using an AI model that uses user emotion data as input and adjusts the reservation change procedure.

[0100] When providing support, the support unit can select the optimal support method by referring to the user's past reservation change history. For example, it can prioritize providing reservation change methods that the user has frequently used in the past. It can prioritize providing reservation change methods for accommodations that the user has used in the past. It can suggest the optimal support method based on the user's past reservation change history. This makes it possible to provide the optimal support method based on the user's past reservation change history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide a support method using an AI model that inputs past reservation change history and selects the optimal support method.

[0101] During support, the support unit can suggest the timing of rescheduling a reservation based on the user's current schedule. For example, the support unit can refer to the user's calendar information to suggest the optimal timing for rescheduling a reservation. The support unit can provide a reservation rescheduling reminder based on the user's schedule. The support unit can automatically set the optimal timing for rescheduling a reservation based on the user's schedule. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can suggest the timing using an AI model that inputs the user's schedule data and suggests the timing for rescheduling a reservation.

[0102] The support unit can estimate the user's emotions and determine support priorities based on the estimated user emotions. For example, if the user is feeling stressed, quick support can be provided as a priority. If the user is relaxed, detailed support can be provided. If the user is in a hurry, concise and quick support can be provided. This allows optimal support to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide support using an AI model that inputs the user's emotion data and determines support priorities.

[0103] When providing support, the support unit can provide the optimal support method by taking into account the user's geographical location information. For example, the support unit can prioritize providing a support center that is close to the user's current location. The support unit can prioritize providing a support center that is close to a specific tourist attraction for the user. The support unit can prioritize providing a support center that is close to a specific event venue for the user. This allows optimal support to be provided based on the user's geographical location information. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can provide support using an AI model that inputs the user's geographical location information and outputs the optimal support method.

[0104] During support, the support unit can analyze the user's social media activity and provide relevant support information. For example, the support unit can provide support information related to travel destinations shared by the user on social media, support information related to tourist spots that the user has "liked" on social media, or support information recommended by travel bloggers the user follows on social media. This allows the most appropriate support information to be provided based on the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide support information using an AI model that inputs the user's social media activity data and outputs relevant support information.

[0105] The suggestion unit can estimate the user's emotions and adjust the display method of tourist information based on the estimated user emotions. For example, if the user is feeling stressed, tourist information that allows relaxation is displayed preferentially. If the user is excited, tourist information that offers a wide range of activities is displayed preferentially. If the user is tired, tourist information in a quiet environment is displayed preferentially. This allows optimal tourist information to be displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the display method using an AI model that uses user emotion data as input and adjusts the display method of tourist information.

[0106] When making a suggestion, the suggestion unit can provide optimal tourist information by referring to the user's past travel history. For example, it can provide tourist information related to tourist spots that the user has visited in the past. It can provide information related to sightseeing tours that the user has taken in the past. It can suggest optimal tourist spots based on the user's past travel history. This makes it possible to provide optimal tourist information based on the user's past travel history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that takes past travel history as input and outputs optimal tourist information.

[0107] When making a suggestion, the suggestion unit can customize tourist information based on the user's current interests and concerns. For example, the suggestion unit can provide tourist information related to a topic that the user is currently interested in. The suggestion unit can customize tourist information based on keywords recently searched by the user. The suggestion unit can provide tourist information related to accounts the user follows on social media. This makes it possible to provide optimal tourist information based on the user's current interests and concerns. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that uses the user's interest and concern data as input and customizes the tourist information.

[0108] The suggestion unit can estimate the user's emotions and prioritize tourist information based on the estimated user emotions. For example, if the user is feeling stressed, relaxation facilities can be displayed preferentially. If the user is excited, tourist destinations with plenty of activities can be displayed preferentially. If the user is tired, tourist destinations with quiet environments can be displayed preferentially. This allows optimal tourist information to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. Examples of generation AI include text generation AI (e.g., LLM) and multimodal generation AI, but are not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can provide tourist information using an AI model that inputs user emotion data and determines the priority of tourist information.

[0109] When making a suggestion, the suggestion unit can prioritize displaying nearby tourist information, taking into account the user's geographical location information. For example, tourist spots close to the user's current location can be prioritized. Tourist information close to a specific tourist spot for the user can be prioritized. Tourist information close to a specific event venue for the user can be prioritized. This makes it possible to provide optimal tourist information based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that inputs the user's geographical location information and outputs nearby tourist information.

[0110] When making a suggestion, the suggestion unit can analyze the user's social media activities and provide relevant tourist information. For example, it can provide tourist information related to travel destinations shared by the user on social media. It can provide tourist information related to tourist destinations that the user has "liked" on social media. It can provide tourist information recommended by travel bloggers that the user follows on social media. This makes it possible to provide optimal tourist information based on the user's social media activities. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide tourist information using an AI model that inputs the user's social media activity data and outputs relevant tourist information.

[0111] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, information is collected during times when the user can relax. If the user is relaxed, detailed information is collected. If the user is in a hurry, necessary information is quickly collected. This allows information to be collected at the optimal timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the timing using an AI model that uses user emotion data as input and adjusts the timing of information collection.

[0112] When collecting data, the collection unit can select the optimal information collection method by referring to the user's past search history. For example, the collection unit collects information based on keywords that the user has frequently searched for in the past. The collection unit prioritizes collecting information sources that the user has used in the past. The collection unit suggests the optimal information collection method based on the user's past search history. This makes it possible to provide the optimal information collection method based on the user's past search history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that uses past search history as input and selects the optimal information collection method.

[0113] When collecting data, the collection unit can prioritize collecting highly relevant information, taking into account the user's current location information. For example, the collection unit prioritizes collecting information close to the user's current location. The user prioritizes collecting information close to a specific tourist spot. The user prioritizes collecting information close to a specific event venue. This allows optimal information to be collected based on the user's current location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that inputs the user's current location information and outputs highly relevant information.

[0114] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is stressed, information related to relaxation is preferentially collected. If the user is excited, information related to activity is preferentially collected. If the user is tired, information related to a quiet environment is preferentially collected. This allows optimal information to be preferentially collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that inputs user emotion data and determines the priority of information to be collected.

[0115] When collecting data, the collection unit can prioritize collecting highly relevant information, taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information close to the user's current location. The user prioritizes collecting information close to a specific tourist spot. The user prioritizes collecting information close to a specific event venue. This allows optimal information to be collected based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information using an AI model that inputs the user's geographical location information and outputs highly relevant information.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The suggestion unit can also estimate the user's emotions and suggest meal plans for the travel destination based on the estimated user emotions. For example, if the user is feeling stressed, a restaurant with a relaxing atmosphere can be suggested. If the user is excited, a lively restaurant or bar can be suggested. If the user is tired, a quiet and relaxing cafe can be suggested. This makes it possible to provide an optimal meal plan based on the user's emotions.

[0118] The provider can also analyze the user's past activity history at travel destinations and suggest similar activities. For example, if the user has enjoyed skydiving in the past, activities such as bungee jumping and paragliding can be suggested. If the user has enjoyed visiting art museums in the past, local art museums and galleries can be suggested. If the user has enjoyed hiking in the past, local trekking courses can be suggested. This makes it possible to suggest optimal activities based on the user's past activity history.

[0119] The support unit can also estimate the user's emotions and support emergency response during travel based on the estimated user emotions. For example, if the user feels anxious, it can quickly provide information on emergency contacts and medical institutions. If the user is in a state of panic, it can provide guidance to calm the user. If the user is confused, it can provide instructions on how to contact a local support center. This allows the optimal emergency response to be supported based on the user's emotions.

[0120] The suggestion unit can also monitor the user's current health condition and suggest health-conscious activities. For example, if the user is tired, it can suggest a relaxing massage or a hot spring. If the user feels they are lacking exercise, it can suggest a yoga class or a fitness gym. If the user is looking for healthy food, it can suggest healthy restaurants or organic cafes. This allows the system to suggest optimal activities based on the user's health condition.

[0121] The providing unit can also estimate the user's emotions and provide shopping information for the travel destination based on the estimated user emotions. For example, if the user is relaxed, local handmade crafts and art galleries are suggested. If the user is excited, shopping malls and outlets are suggested. If the user is tired, quiet and relaxing boutiques and bookstores are suggested. In this way, optimal shopping information can be provided based on the user's emotions.

[0122] The suggestion unit can also analyze the user's dining history at past travel destinations and suggest similar meal plans. For example, if the user has enjoyed Italian food in the past, a local Italian restaurant can be suggested. If the user has enjoyed Japanese food in the past, a local Japanese restaurant can be suggested. If the user has enjoyed vegetarian food in the past, a local vegetarian restaurant can be suggested. This allows the system to suggest optimal meal plans based on the user's dining history.

[0123] The support department can also estimate the user's emotions and provide troubleshooting support during travel based on the estimated user emotions. For example, if the user is feeling anxious, it can provide a step-by-step guide to resolving the problem. If the user is in a state of panic, it can provide emergency contact information and instructions on how to contact the local support center. If the user is confused, it can provide local language support and translation services. This allows it to provide optimal troubleshooting support based on the user's emotions.

[0124] The suggestion unit can also customize event information for travel destinations based on the user's current interests. For example, if the user is interested in music, local concerts and live events can be suggested. If the user is interested in art, local art festivals and gallery exhibitions can be suggested. If the user is interested in sports, local sporting events and matches can be suggested. This allows the system to provide optimal event information based on the user's current interests.

[0125] The providing unit can also estimate the user's emotions and provide entertainment information for the travel destination based on the estimated user emotions. For example, if the user is relaxed, information about movie theaters and theaters is provided. If the user is excited, information about nightclubs and live music venues is provided. If the user is tired, information about quiet cafes and reading spaces is provided. This makes it possible to provide optimal entertainment information based on the user's emotions.

[0126] The support department can also analyze the user's past trouble history and respond quickly if a similar problem occurs. For example, if the user has experienced a flight delay in the past, the support department can quickly suggest an alternative flight if a similar situation occurs. If the user has experienced trouble booking a hotel in the past, the support department can quickly suggest an alternative accommodation if a similar situation occurs. If the user has experienced lost luggage in the past, the support department can quickly provide support if a similar situation occurs. This allows the support department to provide the most appropriate support based on the user's past trouble history.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The search unit searches for available rooms from among multiple accommodation facilities. The search unit searches for available rooms for the user based on the user's preferences and accommodation history. For example, for a user who has previously preferred luxury hotels, rooms from similar luxury hotels will be preferentially displayed as search results. Step 2: The provider provides price comparison information and special offer information for the rooms searched by the search unit. Based on information collected from multiple reservation sites, the provider presents the user with the rooms with the lowest prices and special offers. Step 3: The support section supports the reservation and modification of the room searched by the search section. If the user wants to change the reservation, the support section responds in real time and carries out the modification procedure. Step 4: The suggestion unit suggests tourist information and activities around the room searched by the search unit. The suggestion unit provides a virtual tour of tourist spots and activities around the accommodation and suggests travel plans to the user.

[0129] 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.

[0130] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] 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.

[0132] For example, each of the multiple elements including the search unit, the provision unit, the support unit, and the suggestion unit is implemented by at least one of the smart device 14 and the data processing device 12. For example, the search unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and provides price comparison information and special offer information to the user. The support unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, for example, and supports reservation confirmation and changes. The suggestion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, for example, and suggests tourist information and activities in the area. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[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 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.

[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 (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).

[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] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] For example, each of the multiple elements including the search unit, the provision unit, the support unit, and the suggestion unit is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the search unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and provides price comparison information and special offer information to the user. The support unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, for example, and supports reservation confirmation and changes. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, for example, and suggests nearby tourist information and activities. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] For example, each of the multiple elements including the search unit, the provision unit, the support unit, and the suggestion unit is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the search unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, for example, and provides price comparison information and special offer information to the user. The support unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, for example, and supports reservation confirmation and changes. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, for example, and suggests tourist information and activities in the area. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0177] 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.

[0178] 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.

[0179] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0180] 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.

[0181] For example, each of the multiple elements including the search unit, the provision unit, the support unit, and the suggestion unit is realized by at least one of the robot 414 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The provision unit is realized by the specific processing unit 290 of the data processing device 12, for example, and provides price comparison information and special offer information to the user. The support unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, for example, and supports reservation confirmation and changes. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, for example, and suggests tourist information and activities in the area. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Appendix 1) a search unit that searches for available rooms from among rooms at multiple accommodation facilities; a provision unit that provides room price comparison information and special offer information; a support unit that supports confirmation and change of the room reservation; A suggestion unit that suggests tourist information and activities in the surrounding area. A system characterized by: (Appendix 2) The search unit Search for available rooms for a user based on the user's preferences and accommodation history 2. The system of claim 1. (Appendix 3) The providing unit To provide a user with price comparison information for the rooms searched by the search unit and information on benefits when using the rooms searched by the search unit. 2. The system of claim 1. (Appendix 4) The system described in Appendix 1 is characterized in that it includes a collection unit that collects information from multiple reservation sites, and the provision unit provides price comparison information between the multiple reservation sites for rooms searched by the search unit. (Appendix 5) The support portion is Supports reservations and changes to rooms searched by the search unit; The proposal unit Suggesting tourist information and activities around the room searched by the search unit 2. The system of claim 1. (Appendix 6) The proposal unit Providing a virtual tour of accommodation facilities having rooms searched by the search unit and tourist spots around the accommodation facilities 2. The system of claim 1. (Appendix 7) The proposal unit Providing security information for accommodation facilities that have rooms searched for by the search unit and the surrounding area of ​​the accommodation facilities 2. The system of claim 1. (Appendix 8) The support portion is Respond to user questions in real time via an interactive chatbot 2. The system of claim 1. (Appendix 9) The proposal unit Cooperating with the concierge service of the accommodation facility that has the room searched by the search unit to support special requests 2. The system of claim 1. (Appendix 10) The proposal unit Proposing health-conscious travel plans based on the user's genetic information 2. The system of claim 1. (Appendix 11) The proposal unit AI analyzes the user's stress level and recommends accommodations and activities that are effective in reducing stress. 2. The system of claim 1. (Appendix 12) The search unit Inferring user sentiment and adjusting the display order of search results based on the estimated user sentiment 2. The system of claim 1. (Appendix 13) The search unit Analyze users' past stay history and find the best room based on specific seasons or events 2. The system of claim 1. (Appendix 14) The search unit When searching, the system prioritizes suitable accommodations based on the user's current health and physical condition. 2. The system of claim 1. (Appendix 15) The search unit Estimate user sentiment and adjust search result filtering conditions based on the estimated user sentiment 2. The system of claim 1. (Appendix 16) The search unit When searching, the app takes into account the user's geographic location to prioritize nearby accommodations. 2. The system of claim 1. (Appendix 17) The search unit Analyzes users' social media activity and suggests relevant accommodations when they search 2. The system of claim 1. (Appendix 18) The providing unit Inferring user sentiment and adjusting how price comparison information is displayed based on the inferred user sentiment 2. The system of claim 1. (Appendix 19) The providing unit Customize reward information at the time of delivery based on the user's past booking history 2. The system of claim 1. (Appendix 20) The providing unit At the time of delivery, provide the best pricing information based on the user's current budget 2. The system of claim 1. (Appendix 21) The providing unit Estimate user emotions and prioritize reward information based on the estimated user emotions. 2. The system of claim 1. (Appendix 22) The providing unit When providing the service, the service takes into account the user's geographic location information and prioritizes displaying information about special offers that are only available in that area. 2. The system of claim 1. (Appendix 23) The providing unit When offered, analyze your social media activity to provide you with relevant offers 2. The system of claim 1. (Appendix 24) The support portion is Inferring user emotions and adjusting reservation change procedures based on the estimated user emotions 2. The system of claim 1. (Appendix 25) The support portion is When providing support, refer to the user's past reservation change history to determine the most appropriate support method. 2. The system of claim 1. (Appendix 26) The support portion is When assisting, suggest rescheduling based on the user's current schedule 2. The system of claim 1. (Appendix 27) The support portion is Estimate the user's emotions and determine the priority of support based on the estimated user emotions 2. The system of claim 1. (Appendix 28) The support portion is When providing support, consider the user's geographic location to provide the most appropriate support method. 2. The system of claim 1. (Appendix 29) The support portion is When providing support, analyze your social media activity and provide relevant support information 2. The system of claim 1. (Appendix 30) The proposal unit Estimate user emotions and adjust the way tourist information is displayed based on the estimated user emotions 2. The system of claim 1. (Appendix 31) The proposal unit When making suggestions, the system refers to the user's past travel history to provide optimal tourist information. 2. The system of claim 1. (Appendix 32) The proposal unit Customize tourist information based on the user's current interests at the time of suggestion 2. The system of claim 1. (Appendix 33) The proposal unit Estimate user emotions and prioritize tourist information based on the estimated user emotions 2. The system of claim 1. (Appendix 34) The proposal unit When making suggestions, the system takes into account the user's geographical location and prioritizes displaying nearby tourist information. 2. The system of claim 1. (Appendix 35) The proposal unit When making a suggestion, analyze the user's social media activity and provide relevant tourist information 2. The system of claim 1. (Appendix 36) The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions 2. The system of claim 1. (Appendix 37) The collecting unit When collecting information, the optimal method of collecting information is selected by referring to the user's past search history. 2. The system of claim 1. (Appendix 38) The collecting unit When collecting data, the system takes into account the user's current location information and prioritizes the collection of highly relevant information. 2. The system of claim 1. (Appendix 39) The collecting unit Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1. (Appendix 40) The collecting unit When collecting data, the geographic location of the user is taken into consideration to prioritize the collection of information that is most relevant. 2. The system of claim 1. [Explanation of symbols]

[0201] 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 search unit that searches for available rooms among rooms in a plurality of accommodation facilities; a provision section for providing price comparison information and special offer information for rooms; A support unit that supports confirmation and change of the room reservation; A suggestion unit that suggests tourist information and activities in the area. A system characterized by:

2. The search unit is Search for available rooms for a user based on the user's preferences and accommodation history 2. The system of claim 1 .

3. The providing unit is To provide a user with price comparison information for the rooms searched for by the search unit and information on benefits when using the rooms searched for by the search unit.

2. The system of claim 1 .

4. It has a collection unit that collects information from multiple reservation sites, The providing unit is Providing price comparison information among the multiple reservation sites for the rooms searched for by the search unit 2. The system of claim 1 .

5. The support portion is Supports reservations and changes to rooms searched by the search unit; The suggestion unit, Suggest tourist information and activities around the room searched by the search unit 2. The system of claim 1 .

6. The suggestion unit, Providing a virtual tour of accommodation facilities having rooms searched for by the search unit and tourist spots around the accommodation facilities 2. The system of claim 1 .

7. The suggestion unit, Providing security information for accommodation facilities having rooms searched for by the search unit and for the surrounding area of ​​the accommodation facilities 2. The system of claim 1 .

8. The support portion is Respond to user questions in real time via interactive chatbots 2. The system of claim 1 .

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