system

Abstract

The system according to this embodiment aims to search for accommodations based on the user's desired conditions and automate the booking process. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a confirmation unit, and a reservation unit. The reception unit receives input from the user regarding their desired conditions. The search unit searches for accommodations based on the desired conditions received by the reception unit. The confirmation unit checks the reservation status of the search results obtained by the search unit in real time. The reservation unit automatically reserves the available accommodations confirmed by the confirmation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a user needs to spend a lot of time and effort to find a lodging facility that meets the desired conditions, and the reservation process is also time-consuming.

[0005] The system according to the embodiment aims to search for lodging facilities based on the desired conditions of the user and automate the reservation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a confirmation unit, and a reservation unit. The reception unit receives input from the user regarding their desired conditions. The search unit searches for accommodations based on the desired conditions received by the reception unit. The confirmation unit checks the reservation status of the search results obtained by the search unit in real time. The reservation unit automatically makes a reservation for the available accommodations confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can search for accommodations based on the user's desired conditions and automate the booking process. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI assistant for hotel reservations according to an embodiment of the present invention is a system that searches for hotels based on the user's desired conditions, checks the reservation status in real time, and automatically makes reservations. The AI ​​assistant for hotel reservations searches for accommodations based on the user's desired conditions, checks the reservation status in real time, and automatically reserves available accommodations. It also has a function to recommend the most suitable accommodations to the user based on past stay history and ratings. For example, the user enters their desired conditions. This can be done via voice input or chat format, and the system can also simplify input by automatically acquiring the user's current location information. For example, the user might enter a desired condition such as, "I'm looking for a business hotel in Tokyo for a 1-night, 2-day stay." This information is entered into the AI. Next, the AI ​​searches for accommodations based on the entered desired conditions. The AI ​​can estimate the user's past stay history and ratings, as well as the user's emotions, and search for accommodations that match those emotions. For example, it may recommend hotels that have received high ratings in the past, or, if the user wants to relax, hotels with a relaxing environment. Once search results are obtained, the AI ​​checks the reservation status in real time. If an available accommodation is found, the AI ​​automatically makes a reservation for that accommodation. This allows users to book their desired accommodations without any hassle. Furthermore, the AI ​​also has features to anonymize user data and implement security measures. This protects user privacy and allows them to use the service safely. With this system, users can easily search for and book hotels based on their desired conditions. In addition, a recommendation function based on past stay history and ratings can find the most suitable accommodations for the user. For example, if the user is traveling for business, it can recommend business hotels that have received high ratings in the past, and if the user wants to relax, it can recommend hotels with a relaxing environment. As a result, the hotel booking support AI assistant can search for hotels based on the user's desired conditions, check the availability in real time, and make reservations automatically.

[0029] The hotel reservation support AI assistant according to this embodiment comprises a reception unit, a search unit, a confirmation unit, and a reservation unit. The reception unit receives input from the user regarding their desired conditions. These conditions include, but are not limited to, location, price, and services. The reception unit can accept input via voice input or chat format, for example. The reception unit can also simplify input by automatically acquiring the user's current location information. For example, the reception unit can acquire the user's current location information using GPS or Wi-Fi location information. The search unit searches for accommodations based on the desired conditions received by the reception unit. The search unit can search for accommodations that match the user's preferences based on the user's past stay history and ratings, for example. For example, the search unit prioritizes searching for accommodations that have received high ratings in the past. The search unit can also estimate the user's emotions and search for accommodations that match the estimated emotions. For example, if the search unit finds that the user wants to relax, it will recommend accommodations with a relaxing environment. The confirmation unit checks the reservation status of the search results obtained by the search unit in real time. The confirmation unit can, for example, acquire and display the availability status of accommodations in real time. The confirmation unit can also update the availability status in real time when a reservation is confirmed or a cancellation occurs. The reservation unit automatically reserves the available accommodations confirmed by the confirmation unit. The reservation unit can, for example, automatically reserve the most suitable accommodation based on the user's desired conditions. As a result, the hotel reservation support AI assistant according to this embodiment can search for hotels based on the user's desired conditions, check the reservation status in real time, and make reservations automatically. Some or all of the above processes in the reception unit, search unit, confirmation unit, and reservation unit may be performed using AI, for example, or without AI. For example, the reception unit inputs the user's desired conditions into the AI, and the AI ​​searches for accommodations based on those conditions. The search unit has the AI ​​acquire the search results, and the confirmation unit has the AI ​​check the reservation status in real time. The reservation unit has the AI ​​automatically reserve the available accommodations.

[0030] The reception desk accepts input from users regarding their desired conditions. These conditions may include, but are not limited to, location, price, and services. The reception desk can accept input via voice or chat. Specifically, with voice input, the user speaks their desired conditions into a microphone, and speech recognition technology converts the content into text data. With chat, the user enters their desired conditions in text, and AI analyzes and understands the content. The reception desk can also simplify input by automatically acquiring the user's current location information. For example, the reception desk can acquire the user's current location using GPS or Wi-Fi location information. This eliminates the need for users to manually enter location information when searching for accommodations near their current location. Furthermore, the reception desk can learn the user's past input history and preferences, and incorporate personalization features to make future inputs smoother. For example, it can automatically suggest similar conditions to users who have previously preferred a particular price range or service. The reception desk can also provide real-time feedback on the user's input, prompting them to review and correct their input. This allows the reception department to accurately and quickly receive the user's desired conditions, enabling a smooth transfer of data to the search department.

[0031] The search unit searches for accommodations based on the user's preferences, which are received by the reception unit. For example, the search unit can search for accommodations that match the user's preferences based on their past stay history and ratings. Specifically, it uses AI to analyze the user's past stay history and prioritizes highly-rated accommodations and preferred conditions in the search results. The search unit can also estimate the user's emotions and search for accommodations that match those emotions. For example, if the user wants to relax, the search unit will recommend accommodations with a relaxing environment. Emotion estimation uses natural language processing technology and sentiment analysis algorithms, analyzing the user's input and past behavioral data. Furthermore, the search unit utilizes an extensive database to quickly find accommodations that meet the user's preferences. The database includes detailed information on accommodations, prices, availability, and user reviews, and this information is comprehensively evaluated to provide the best search results. The search results are filtered based on the user's preferences, and the most suitable accommodations are listed. The search unit displays the search results visually and clearly, allowing users to easily compare and consider their options. For example, the system can assist users in their selection by displaying the location of accommodations on a map and showing prices and ratings in graphs. This allows the search unit to quickly and accurately find and provide accommodations that best suit the user's desired conditions.

[0032] The confirmation unit checks the reservation status of search results obtained by the search unit in real time. For example, the confirmation unit can acquire and display the availability status of accommodations in real time. Specifically, it links with the accommodation's reservation system to acquire the latest availability information. This allows the user to instantly check whether the selected accommodation is actually available for reservation. The confirmation unit can also update the availability status in real time when a reservation is confirmed or a cancellation occurs. For example, if another user reserves or cancels the same accommodation, that information is immediately reflected. Furthermore, the confirmation unit can have a function to notify the user of changes in the reservation status. For example, if the desired accommodation is fully booked, it can send a notification when a vacancy becomes available, allowing the user to make a reservation quickly. The confirmation unit also provides a function to check and compare the availability status of multiple accommodations simultaneously. This allows the user to choose the best accommodation from multiple options. The confirmation unit provides a visually easy-to-understand interface, displaying availability status and available dates in a calendar format, allowing the user to intuitively grasp the information. This allows the confirmation unit to accurately grasp the reservation status of the accommodation selected by the user in real time, and to support the quick reservation process.

[0033] The reservation department automatically reserves available accommodations confirmed by the confirmation department. For example, the reservation department can automatically reserve the most suitable accommodation based on the user's preferences. Specifically, it automates the reservation process for the accommodation selected by the user, requiring them to input necessary information to complete the reservation. The reservation department integrates with the accommodation's reservation system to retrieve and provide reservation confirmations and detailed information to the user. Furthermore, the reservation department securely manages the user's payment information and can handle necessary payment procedures at the time of reservation. For example, it can encrypt and store credit card information and automatically process payments at the time of reservation. In addition, the reservation department can handle reservation changes and cancellations. If the user wishes to change or cancel a reservation, the reservation department responds quickly and reflects the changes in the accommodation's reservation system. The reservation department also provides a function to send reservation confirmation notifications to the user. For example, it can send notifications via email or SMS upon completion of the reservation, allowing the user to confirm their reservation details. This enables the reservation department to automatically reserve the most suitable accommodation based on the user's preferences and to perform the reservation process quickly and accurately. Furthermore, the reservation department manages the user's reservation history and provides a function to simplify the reservation process for future bookings. This allows the reservation department to provide users with a consistent reservation experience and improve convenience.

[0034] The search unit can search for accommodations that match the user's preferences based on the user's past stay history and ratings. For example, the search unit can refer to the user's past stay history and prioritize searching for accommodations that have received high ratings in the past. The search unit can also filter preferred accommodations based on the user's ratings. For example, the search unit can prioritize displaying accommodations that the user has given high ratings to in the past. This allows the user to find more appropriate accommodations based on their past stay history and ratings. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit inputs the user's past stay history and ratings into the AI, and the AI ​​searches for accommodations based on that data.

[0035] The reception unit includes an interface unit that accepts input via voice or chat. The reception unit can, for example, accept the user's desired conditions using voice input. For example, the reception unit can use speech recognition technology to convert the user's voice into text data. The reception unit can also accept input in chat format. For example, the reception unit provides a text chat or bot-compatible interface. This allows the user to input their desired conditions via voice or chat. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit inputs voice input to the AI, and the AI ​​converts the voice into text data.

[0036] The reception department includes a security department that performs data anonymization and security measures. The reception department protects personal information by, for example, anonymizing user data. For example, the reception department anonymizes data by deleting personal information or masking data. The reception department also ensures data security by implementing security measures. For example, the reception department protects data using encryption technology and prevents unauthorized access by implementing access control. In this way, privacy can be protected by anonymizing user data and implementing security measures. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can have AI perform data anonymization and security measures.

[0037] The reception unit can simplify input by automatically acquiring the user's current location information. The reception unit can acquire the user's current location information using, for example, GPS technology. For example, the reception unit can acquire GPS data from the user's smartphone and determine the current location. The reception unit can also acquire the current location information using Wi-Fi location information. For example, the reception unit can determine the location based on information from surrounding Wi-Fi access points. This simplifies input by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit inputs the current location information into the AI, and the AI ​​simplifies input based on that information.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can refer to the user's past input history and prioritize suggesting frequently used input methods (voice, text, etc.). The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can automatically display desired conditions previously entered by the user as candidates. This allows the reception desk to suggest the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history into the AI, and the AI ​​can suggest the optimal input method based on that data.

[0039] The reception desk can automatically complete input based on the user's current activity status. For example, if the user is on the move, the reception desk can automatically acquire the user's current location and set it as the departure point. Furthermore, if the user is participating in a specific event, the reception desk can automatically suggest accommodations related to that event. For example, if the reception desk is active during a specific time period, it can automatically suggest accommodations suitable for that time period. This reduces the effort required for user input by automatically completing input based on the user's current activity status. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's current activity status into the AI, and the AI ​​automatically completes the input based on that information.

[0040] The reception desk can provide the optimal input method by considering the user's device information. For example, if the user is using a smartphone, the reception desk will prioritize voice input. It can also provide an input method optimized for a larger screen if the user is using a tablet. For example, if the user is using a desktop computer, the reception desk will prioritize keyboard input. This allows the system to provide the optimal input method by considering the user's device information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's device information into the AI, and the AI ​​provides the optimal input method based on that information.

[0041] The reception desk can analyze the user's social media activity and suggest relevant conditions. For example, the reception desk can suggest accommodations based on travel plans shared by the user on social media. It can also suggest accommodations based on places the user follows on social media. For example, the reception desk can suggest accommodations based on places the user checked into on social media. In this way, relevant conditions can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​suggests relevant conditions based on that information.

[0042] The search function can filter search results by considering the ratings and reviews of accommodations. For example, the search function may prioritize displaying highly-rated accommodations. It can also filter preferred accommodations based on the user's past ratings. For example, it may exclude accommodations with many negative reviews. This allows for more appropriate search results by considering accommodation ratings and reviews. Some or all of the above processing in the search function may be performed using AI, or not. For example, the search function may input accommodation ratings and reviews into an AI, which then filters the search results based on that information.

[0043] The search function can optimize search results by considering accommodation benefits and promotional information. For example, the search function may prioritize displaying accommodations with benefits. It can also prioritize displaying accommodations that are currently running promotions. For example, the search function may filter accommodations to offer benefits that match the user's preferences. This allows for more attractive search results by considering accommodation benefits and promotional information. Some or all of the above processing in the search function may be performed using AI, or not. For example, the search function may input accommodation benefits and promotional information into the AI, and the AI ​​may optimize the search results based on that information.

[0044] The search unit can prioritize displaying relevant accommodations by referring to the user's past search history. For example, the search unit can prioritize displaying accommodations that the user has searched for in the past. The search unit can also prioritize displaying preferred accommodations based on the user's past search history. For example, the search unit can prioritize displaying accommodations that the user has given high ratings to in the past. In this way, relevant accommodations can be prioritized by referring to the user's past search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input the user's past search history into the AI, and the AI ​​may prioritize displaying relevant accommodations based on that data.

[0045] The search unit can suggest the most suitable accommodation by taking into account the user's geographical location. For example, the search unit may prioritize displaying accommodations close to the user's current location. It can also prioritize displaying accommodations close to the user's destination. For example, the search unit may prioritize displaying accommodations along the user's travel route. In this way, the search unit can suggest the most suitable accommodation by taking into account the user's geographical location. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit may input the user's geographical location information into the AI, and the AI ​​may suggest the most suitable accommodation based on that information.

[0046] The confirmation unit can update the availability status of accommodations in real time. For example, the confirmation unit can acquire and display the availability status of accommodations in real time. The confirmation unit can also update the availability status in real time when a reservation is confirmed. For example, the confirmation unit updates the availability status in real time when a cancellation occurs. This allows the system to provide the latest information by updating the availability status of accommodations in real time. Some or all of the above processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit inputs the availability status of accommodations into the AI, and the AI ​​updates the information in real time based on that information.

[0047] The verification unit can adjust the displayed content considering the accommodation's cancellation policy. For example, the verification unit may prioritize displaying accommodations with strict cancellation policies. Alternatively, the verification unit may prioritize displaying accommodations with lenient cancellation policies. For example, the verification unit may filter accommodations with cancellation policies that match the user's preferences. This allows for more appropriate display content by considering the accommodation's cancellation policy. Some or all of the above processing in the verification unit may be performed using AI, or not. For example, the verification unit may input the accommodation's cancellation policy into the AI, and the AI ​​may adjust the displayed content based on that information.

[0048] The verification unit can display relevant information by referring to the user's past booking history. For example, the verification unit can display information about accommodations the user has booked in the past. The verification unit can also display preferred accommodations from the user's past booking history. For example, the verification unit can display information about accommodations the user has given high ratings to in the past. In this way, relevant information can be displayed by referring to the user's past booking history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit inputs the user's past booking history into the AI, and the AI ​​displays relevant information based on that data.

[0049] The verification unit can provide the optimal display method by considering the user's device information. For example, if the user is using a smartphone, the verification unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the verification unit can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the verification unit can provide a display method optimized for keyboard input. In this way, the system can provide the optimal display method by considering the user's device information. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit inputs the user's device information into the AI, and the AI ​​provides the optimal display method based on that information.

[0050] The reservation department can suggest the most suitable reservation method by referring to the user's past reservation history. For example, the reservation department may prioritize suggesting reservation methods the user has used in the past. Furthermore, the reservation department can also prioritize suggesting preferred accommodations based on the user's past reservation history. For example, the reservation department may prioritize suggesting accommodations that the user has given high ratings to in the past. This allows the system to suggest the most suitable reservation method by referring to the user's past reservation history. Some or all of the above processing in the reservation department may be performed using AI, or not. For example, the reservation department may input the user's past reservation history into the AI, which then suggests the most suitable reservation method based on that data.

[0051] The reservation department can optimize reservations by taking into account the accommodation's perks and promotions. For example, the reservation department can prioritize booking accommodations with perks. It can also prioritize booking accommodations that are currently running promotions. For example, the reservation department can prioritize booking accommodations that offer perks that match the user's preferences. This allows for the provision of more attractive reservations by considering the accommodation's perks and promotions. Some or all of the above processing in the reservation department may be performed using AI, or not. For example, the reservation department can input accommodation perks and promotions into the AI, and the AI ​​can optimize the reservation based on that information.

[0052] The reservation department can book the most suitable accommodation by taking into account the user's geographical location. For example, the reservation department can prioritize booking accommodations close to the user's current location. It can also prioritize booking accommodations close to the user's destination. For example, the reservation department can prioritize booking accommodations along the user's travel route. In this way, the most suitable accommodation can be booked by taking into account the user's geographical location. Some or all of the above processing in the reservation department may be performed using AI, or not. For example, the reservation department inputs the user's geographical location information into the AI, and the AI ​​uses that information to book the most suitable accommodation.

[0053] The booking department can analyze a user's social media activity and suggest relevant accommodations. For example, the booking department can suggest accommodations based on travel plans shared by the user on social media. It can also suggest accommodations based on places the user follows on social media. For example, the booking department can suggest accommodations based on places the user has checked into on social media. In this way, relevant accommodations can be suggested by analyzing the user's social media activity. Some or all of the above processing in the booking department may be performed using AI, for example, or not. For example, the booking department can input the user's social media activity into AI, and the AI ​​can suggest relevant accommodations based on that information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The AI ​​hotel reservation assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodations based on the user's travel purpose. For example, for business travel, it prioritizes hotels with ample meeting rooms and business centers. For sightseeing travel, it can suggest accommodations close to tourist attractions. For family trips, it can suggest hotels with family rooms and kids' clubs. This allows the system to suggest the most suitable accommodations for each user's travel purpose.

[0056] The AI ​​hotel reservation assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodation based on the user's health condition. For example, if the user has allergies, it will prioritize suggesting hotels that offer allergy-friendly rooms. If the user is undergoing rehabilitation, it can suggest hotels with excellent rehabilitation facilities. Additionally, if the user is health-conscious, it can suggest hotels that offer healthy meals. This allows the system to suggest the most suitable accommodation based on the user's health needs.

[0057] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodations based on the user's budget. For example, if the user is traveling on a low budget, it will prioritize suggesting cost-effective accommodations. If the user desires a luxury hotel, it can suggest luxury hotels. If the user has a mid-range budget, it can suggest accommodations that offer a good balance. This allows the system to suggest the most suitable accommodations according to the user's budget.

[0058] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodations based on the user's past travel patterns. For example, it can prioritize suggesting accommodations in cities the user has frequently visited in the past. It can also suggest accommodations that the user has previously given high ratings to. Additionally, it can consider benefits and promotions from accommodations the user has used in the past when making suggestions. This allows it to suggest the most suitable accommodations based on the user's past travel patterns.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives input from the user regarding their desired conditions. These conditions may include, but are not limited to, location, price, and services. The reception desk can accept input via voice or chat, for example. The reception desk can also simplify the input process by automatically obtaining the user's current location information. For example, the reception desk can obtain the user's current location information using GPS or Wi-Fi location information. Step 2: The search unit searches for accommodations based on the user's preferences received by the reception unit. The search unit can search for accommodations that match the user's preferences based on the user's past stay history and ratings, for example. For example, the search unit will prioritize searching for accommodations that have received high ratings in the past. The search unit can also estimate the user's emotions and search for accommodations that match those estimated emotions. For example, if the search unit thinks the user wants to relax, it will recommend accommodations with a relaxing environment. Step 3: The confirmation unit checks the reservation status of the search results obtained by the search unit in real time. For example, the confirmation unit can obtain and display the availability status of accommodations in real time. The confirmation unit can also update the availability status in real time when a reservation is confirmed or a cancellation occurs. Step 4: The reservation unit automatically reserves the available accommodations confirmed by the verification unit. The reservation unit can, for example, automatically reserve the most suitable accommodation based on the user's preferences.

[0061] (Example of form 2) An AI assistant for hotel reservations according to an embodiment of the present invention is a system that searches for hotels based on the user's desired conditions, checks the reservation status in real time, and automatically makes reservations. The AI ​​assistant for hotel reservations searches for accommodations based on the user's desired conditions, checks the reservation status in real time, and automatically reserves available accommodations. It also has a function to recommend the most suitable accommodations to the user based on past stay history and ratings. For example, the user enters their desired conditions. This can be done via voice input or chat format, and the system can also simplify input by automatically acquiring the user's current location information. For example, the user might enter a desired condition such as, "I'm looking for a business hotel in Tokyo for a 1-night, 2-day stay." This information is entered into the AI. Next, the AI ​​searches for accommodations based on the entered desired conditions. The AI ​​can estimate the user's past stay history and ratings, as well as the user's emotions, and search for accommodations that match those emotions. For example, it may recommend hotels that have received high ratings in the past, or, if the user wants to relax, hotels with a relaxing environment. Once search results are obtained, the AI ​​checks the reservation status in real time. If an available accommodation is found, the AI ​​automatically makes a reservation for that accommodation. This allows users to book their desired accommodations without any hassle. Furthermore, the AI ​​also has features to anonymize user data and implement security measures. This protects user privacy and allows them to use the service safely. With this system, users can easily search for and book hotels based on their desired conditions. In addition, a recommendation function based on past stay history and ratings can find the most suitable accommodations for the user. For example, if the user is traveling for business, it can recommend business hotels that have received high ratings in the past, and if the user wants to relax, it can recommend hotels with a relaxing environment. As a result, the hotel booking support AI assistant can search for hotels based on the user's desired conditions, check the availability in real time, and make reservations automatically.

[0062] The hotel reservation support AI assistant according to this embodiment comprises a reception unit, a search unit, a confirmation unit, and a reservation unit. The reception unit receives input from the user regarding their desired conditions. These conditions include, but are not limited to, location, price, and services. The reception unit can accept input via voice input or chat format, for example. The reception unit can also simplify input by automatically acquiring the user's current location information. For example, the reception unit can acquire the user's current location information using GPS or Wi-Fi location information. The search unit searches for accommodations based on the desired conditions received by the reception unit. The search unit can search for accommodations that match the user's preferences based on the user's past stay history and ratings, for example. For example, the search unit prioritizes searching for accommodations that have received high ratings in the past. The search unit can also estimate the user's emotions and search for accommodations that match the estimated emotions. For example, if the search unit finds that the user wants to relax, it will recommend accommodations with a relaxing environment. The confirmation unit checks the reservation status of the search results obtained by the search unit in real time. The confirmation unit can, for example, acquire and display the availability status of accommodations in real time. The confirmation unit can also update the availability status in real time when a reservation is confirmed or a cancellation occurs. The reservation unit automatically reserves the available accommodations confirmed by the confirmation unit. The reservation unit can, for example, automatically reserve the most suitable accommodation based on the user's desired conditions. As a result, the hotel reservation support AI assistant according to this embodiment can search for hotels based on the user's desired conditions, check the reservation status in real time, and make reservations automatically. Some or all of the above processes in the reception unit, search unit, confirmation unit, and reservation unit may be performed using AI, for example, or without AI. For example, the reception unit inputs the user's desired conditions into the AI, and the AI ​​searches for accommodations based on those conditions. The search unit has the AI ​​acquire the search results, and the confirmation unit has the AI ​​check the reservation status in real time. The reservation unit has the AI ​​automatically reserve the available accommodations.

[0063] The reception desk accepts input from users regarding their desired conditions. These conditions may include, but are not limited to, location, price, and services. The reception desk can accept input via voice or chat. Specifically, with voice input, the user speaks their desired conditions into a microphone, and speech recognition technology converts the content into text data. With chat, the user enters their desired conditions in text, and AI analyzes and understands the content. The reception desk can also simplify input by automatically acquiring the user's current location information. For example, the reception desk can acquire the user's current location using GPS or Wi-Fi location information. This eliminates the need for users to manually enter location information when searching for accommodations near their current location. Furthermore, the reception desk can learn the user's past input history and preferences, and incorporate personalization features to make future inputs smoother. For example, it can automatically suggest similar conditions to users who have previously preferred a particular price range or service. The reception desk can also provide real-time feedback on the user's input, prompting them to review and correct their input. This allows the reception department to accurately and quickly receive the user's desired conditions, enabling a smooth transfer of data to the search department.

[0064] The search unit searches for accommodations based on the user's preferences, which are received by the reception unit. For example, the search unit can search for accommodations that match the user's preferences based on their past stay history and ratings. Specifically, it uses AI to analyze the user's past stay history and prioritizes highly-rated accommodations and preferred conditions in the search results. The search unit can also estimate the user's emotions and search for accommodations that match those emotions. For example, if the user wants to relax, the search unit will recommend accommodations with a relaxing environment. Emotion estimation uses natural language processing technology and sentiment analysis algorithms, analyzing the user's input and past behavioral data. Furthermore, the search unit utilizes an extensive database to quickly find accommodations that meet the user's preferences. The database includes detailed information on accommodations, prices, availability, and user reviews, and this information is comprehensively evaluated to provide the best search results. The search results are filtered based on the user's preferences, and the most suitable accommodations are listed. The search unit displays the search results visually and clearly, allowing users to easily compare and consider their options. For example, the system can assist users in their selection by displaying the location of accommodations on a map and showing prices and ratings in graphs. This allows the search unit to quickly and accurately find and provide accommodations that best suit the user's desired conditions.

[0065] The confirmation unit checks the reservation status of search results obtained by the search unit in real time. For example, the confirmation unit can acquire and display the availability status of accommodations in real time. Specifically, it links with the accommodation's reservation system to acquire the latest availability information. This allows the user to instantly check whether the selected accommodation is actually available for reservation. The confirmation unit can also update the availability status in real time when a reservation is confirmed or a cancellation occurs. For example, if another user reserves or cancels the same accommodation, that information is immediately reflected. Furthermore, the confirmation unit can have a function to notify the user of changes in the reservation status. For example, if the desired accommodation is fully booked, it can send a notification when a vacancy becomes available, allowing the user to make a reservation quickly. The confirmation unit also provides a function to check and compare the availability status of multiple accommodations simultaneously. This allows the user to choose the best accommodation from multiple options. The confirmation unit provides a visually easy-to-understand interface, displaying availability status and available dates in a calendar format, allowing the user to intuitively grasp the information. This allows the confirmation unit to accurately grasp the reservation status of the accommodation selected by the user in real time, and to support the quick reservation process.

[0066] The reservation department automatically reserves available accommodations confirmed by the confirmation department. For example, the reservation department can automatically reserve the most suitable accommodation based on the user's preferences. Specifically, it automates the reservation process for the accommodation selected by the user, requiring them to input necessary information to complete the reservation. The reservation department integrates with the accommodation's reservation system to retrieve and provide reservation confirmations and detailed information to the user. Furthermore, the reservation department securely manages the user's payment information and can handle necessary payment procedures at the time of reservation. For example, it can encrypt and store credit card information and automatically process payments at the time of reservation. In addition, the reservation department can handle reservation changes and cancellations. If the user wishes to change or cancel a reservation, the reservation department responds quickly and reflects the changes in the accommodation's reservation system. The reservation department also provides a function to send reservation confirmation notifications to the user. For example, it can send notifications via email or SMS upon completion of the reservation, allowing the user to confirm their reservation details. This enables the reservation department to automatically reserve the most suitable accommodation based on the user's preferences and to perform the reservation process quickly and accurately. Furthermore, the reservation department manages the user's reservation history and provides a function to simplify the reservation process for future bookings. This allows the reservation department to provide users with a consistent reservation experience and improve convenience.

[0067] The search unit can search for accommodations that match the user's preferences based on the user's past stay history and ratings. For example, the search unit can refer to the user's past stay history and prioritize searching for accommodations that have received high ratings in the past. The search unit can also filter preferred accommodations based on the user's ratings. For example, the search unit can prioritize displaying accommodations that the user has given high ratings to in the past. This allows the user to find more appropriate accommodations based on their past stay history and ratings. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit inputs the user's past stay history and ratings into the AI, and the AI ​​searches for accommodations based on that data.

[0068] The reception unit includes an interface unit that accepts input via voice or chat. The reception unit can, for example, accept the user's desired conditions using voice input. For example, the reception unit can use speech recognition technology to convert the user's voice into text data. The reception unit can also accept input in chat format. For example, the reception unit provides a text chat or bot-compatible interface. This allows the user to input their desired conditions via voice or chat. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit inputs voice input to the AI, and the AI ​​converts the voice into text data.

[0069] The reception department includes a security department that performs data anonymization and security measures. The reception department protects personal information by, for example, anonymizing user data. For example, the reception department anonymizes data by deleting personal information or masking data. The reception department also ensures data security by implementing security measures. For example, the reception department protects data using encryption technology and prevents unauthorized access by implementing access control. In this way, privacy can be protected by anonymizing user data and implementing security measures. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can have AI perform data anonymization and security measures.

[0070] The search unit can estimate the user's emotions and search for accommodations that match the estimated emotions of the user. The search unit can estimate the user's emotions using, for example, facial recognition technology. For example, the search unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The search unit can also estimate the user's emotions using voice analysis technology. For example, the search unit can record the user's voice and estimate the emotions using voice analysis technology. This allows the search unit to find more appropriate accommodations 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and searches for accommodations based on those emotions.

[0071] The reception unit can simplify input by automatically acquiring the user's current location information. The reception unit can acquire the user's current location information using, for example, GPS technology. For example, the reception unit can acquire GPS data from the user's smartphone and determine the current location. The reception unit can also acquire the current location information using Wi-Fi location information. For example, the reception unit can determine the location based on information from surrounding Wi-Fi access points. This simplifies input by automatically acquiring the user's current location information. Some or all of the above processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit inputs the current location information into the AI, and the AI ​​simplifies input based on that information.

[0072] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, the reception unit can estimate the user's emotions using facial recognition technology. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can record the user's voice and estimate the emotions using voice analysis technology. This allows for a more comfortable input experience by adjusting the design of the input interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the design of the input interface based on those emotions.

[0073] The reception unit can estimate the user's emotions and adjust the timing of inputting desired conditions based on the estimated emotions. The reception unit can estimate the user's emotions using, for example, facial recognition technology. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit can record the user's voice and estimate the emotions using voice analysis technology. This allows the user to input desired conditions at a more appropriate time by adjusting the input timing based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the input timing based on those emotions.

[0074] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can refer to the user's past input history and prioritize suggesting frequently used input methods (voice, text, etc.). The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can automatically display desired conditions previously entered by the user as candidates. This allows the reception desk to suggest the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input history into the AI, and the AI ​​can suggest the optimal input method based on that data.

[0075] The reception desk can automatically complete input based on the user's current activity status. For example, if the user is on the move, the reception desk can automatically acquire the user's current location and set it as the departure point. Furthermore, if the user is participating in a specific event, the reception desk can automatically suggest accommodations related to that event. For example, if the reception desk is active during a specific time period, it can automatically suggest accommodations suitable for that time period. This reduces the effort required for user input by automatically completing input based on the user's current activity status. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's current activity status into the AI, and the AI ​​automatically completes the input based on that information.

[0076] The reception desk can estimate the user's emotions and determine the priority of the user's desired conditions based on the estimated emotions. The reception desk can estimate the user's emotions using, for example, facial recognition technology. For example, the reception desk can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can record the user's voice and estimate the emotions using voice analysis technology. This allows the reception desk to suggest more appropriate accommodations by determining the priority of desired conditions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and determines the priority of desired conditions based on those emotions.

[0077] The reception desk can provide the optimal input method by considering the user's device information. For example, if the user is using a smartphone, the reception desk will prioritize voice input. It can also provide an input method optimized for a larger screen if the user is using a tablet. For example, if the user is using a desktop computer, the reception desk will prioritize keyboard input. This allows the system to provide the optimal input method by considering the user's device information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk inputs the user's device information into the AI, and the AI ​​provides the optimal input method based on that information.

[0078] The reception desk can analyze the user's social media activity and suggest relevant conditions. For example, the reception desk can suggest accommodations based on travel plans shared by the user on social media. It can also suggest accommodations based on places the user follows on social media. For example, the reception desk can suggest accommodations based on places the user checked into on social media. In this way, relevant conditions can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's social media activity into the AI, and the AI ​​suggests relevant conditions based on that information.

[0079] The search unit can estimate the user's emotions and adjust the display method of search results based on the estimated emotions. For example, the search unit can estimate the user's emotions using facial recognition technology. For example, the search unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The search unit can also estimate the user's emotions using voice analysis technology. For example, the search unit can record the user's voice and estimate the emotions using voice analysis technology. This allows for a more appropriate display method by adjusting the display method of search results 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the display method of search results based on those emotions.

[0080] The search function can filter search results by considering the ratings and reviews of accommodations. For example, the search function may prioritize displaying highly-rated accommodations. It can also filter preferred accommodations based on the user's past ratings. For example, it may exclude accommodations with many negative reviews. This allows for more appropriate search results by considering accommodation ratings and reviews. Some or all of the above processing in the search function may be performed using AI, or not. For example, the search function may input accommodation ratings and reviews into an AI, which then filters the search results based on that information.

[0081] The search function can optimize search results by considering accommodation benefits and promotional information. For example, the search function may prioritize displaying accommodations with benefits. It can also prioritize displaying accommodations that are currently running promotions. For example, the search function may filter accommodations to offer benefits that match the user's preferences. This allows for more attractive search results by considering accommodation benefits and promotional information. Some or all of the above processing in the search function may be performed using AI, or not. For example, the search function may input accommodation benefits and promotional information into the AI, and the AI ​​may optimize the search results based on that information.

[0082] The search unit can estimate the user's emotions and determine the priority of search results based on the estimated emotions. For example, the search unit can estimate the user's emotions using facial recognition technology. For example, the search unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The search unit can also estimate the user's emotions using voice analysis technology. For example, the search unit can record the user's voice and estimate the emotions using voice analysis technology. By doing so, it is possible to suggest more appropriate accommodations by determining the priority of search results 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and determines the priority of search results based on those emotions.

[0083] The search unit can prioritize displaying relevant accommodations by referring to the user's past search history. For example, the search unit can prioritize displaying accommodations that the user has searched for in the past. The search unit can also prioritize displaying preferred accommodations based on the user's past search history. For example, the search unit can prioritize displaying accommodations that the user has given high ratings to in the past. In this way, relevant accommodations can be prioritized by referring to the user's past search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit may input the user's past search history into the AI, and the AI ​​may prioritize displaying relevant accommodations based on that data.

[0084] The search unit can suggest the most suitable accommodation by taking into account the user's geographical location. For example, the search unit may prioritize displaying accommodations close to the user's current location. It can also prioritize displaying accommodations close to the user's destination. For example, the search unit may prioritize displaying accommodations along the user's travel route. In this way, the search unit can suggest the most suitable accommodation by taking into account the user's geographical location. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit may input the user's geographical location information into the AI, and the AI ​​may suggest the most suitable accommodation based on that information.

[0085] The verification unit can estimate the user's emotions and adjust the display method of the reservation status based on the estimated emotions. The verification unit can estimate the user's emotions using, for example, facial recognition technology. For example, the verification unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The verification unit can also estimate the user's emotions using voice analysis technology. For example, the verification unit can record the user's voice and estimate the emotions using voice analysis technology. This allows for a more appropriate display method to be provided by adjusting the display method of the reservation status based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the display method of the reservation status based on those emotions.

[0086] The confirmation unit can update the availability status of accommodations in real time. For example, the confirmation unit can acquire and display the availability status of accommodations in real time. The confirmation unit can also update the availability status in real time when a reservation is confirmed. For example, the confirmation unit updates the availability status in real time when a cancellation occurs. This allows the system to provide the latest information by updating the availability status of accommodations in real time. Some or all of the above processes in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit inputs the availability status of accommodations into the AI, and the AI ​​updates the information in real time based on that information.

[0087] The verification unit can adjust the displayed content considering the accommodation's cancellation policy. For example, the verification unit may prioritize displaying accommodations with strict cancellation policies. Alternatively, the verification unit may prioritize displaying accommodations with lenient cancellation policies. For example, the verification unit may filter accommodations with cancellation policies that match the user's preferences. This allows for more appropriate display content by considering the accommodation's cancellation policy. Some or all of the above processing in the verification unit may be performed using AI, or not. For example, the verification unit may input the accommodation's cancellation policy into the AI, and the AI ​​may adjust the displayed content based on that information.

[0088] The verification unit can estimate the user's emotions and determine the priority of the reservation status based on the estimated emotions. The verification unit can estimate the user's emotions using, for example, facial recognition technology. For example, the verification unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The verification unit can also estimate the user's emotions using voice analysis technology. For example, the verification unit can record the user's voice and estimate the emotions using voice analysis technology. This allows the system to suggest more appropriate accommodations by determining the priority of the reservation status based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using, for example, AI, or not using AI. For example, the verification unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and determines the priority of the reservation status based on those emotions.

[0089] The verification unit can display relevant information by referring to the user's past booking history. For example, the verification unit can display information about accommodations the user has booked in the past. The verification unit can also display preferred accommodations from the user's past booking history. For example, the verification unit can display information about accommodations the user has given high ratings to in the past. In this way, relevant information can be displayed by referring to the user's past booking history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit inputs the user's past booking history into the AI, and the AI ​​displays relevant information based on that data.

[0090] The verification unit can provide the optimal display method by considering the user's device information. For example, if the user is using a smartphone, the verification unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the verification unit can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the verification unit can provide a display method optimized for keyboard input. In this way, the system can provide the optimal display method by considering the user's device information. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit inputs the user's device information into the AI, and the AI ​​provides the optimal display method based on that information.

[0091] The reservation unit can estimate the user's emotions and adjust the reservation procedure based on the estimated emotions. For example, the reservation unit can estimate the user's emotions using facial recognition technology. For example, the reservation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reservation unit can also estimate the user's emotions using voice analysis technology. For example, the reservation unit can record the user's voice and estimate the emotions using voice analysis technology. This allows the reservation unit to provide a more appropriate reservation procedure by adjusting the procedure 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI, or not using AI. For example, the reservation unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the reservation procedure based on those emotions.

[0092] The reservation department can suggest the most suitable reservation method by referring to the user's past reservation history. For example, the reservation department may prioritize suggesting reservation methods the user has used in the past. Furthermore, the reservation department can also prioritize suggesting preferred accommodations based on the user's past reservation history. For example, the reservation department may prioritize suggesting accommodations that the user has given high ratings to in the past. This allows the system to suggest the most suitable reservation method by referring to the user's past reservation history. Some or all of the above processing in the reservation department may be performed using AI, or not. For example, the reservation department may input the user's past reservation history into the AI, which then suggests the most suitable reservation method based on that data.

[0093] The reservation department can optimize reservations by taking into account the accommodation's perks and promotions. For example, the reservation department can prioritize booking accommodations with perks. It can also prioritize booking accommodations that are currently running promotions. For example, the reservation department can prioritize booking accommodations that offer perks that match the user's preferences. This allows for the provision of more attractive reservations by considering the accommodation's perks and promotions. Some or all of the above processing in the reservation department may be performed using AI, or not. For example, the reservation department can input accommodation perks and promotions into the AI, and the AI ​​can optimize the reservation based on that information.

[0094] The reservation unit can estimate the user's emotions and determine reservation priorities based on the estimated emotions. For example, the reservation unit can estimate the user's emotions using facial recognition technology. For example, the reservation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reservation unit can also estimate the user's emotions using voice analysis technology. For example, the reservation unit can record the user's voice and estimate the emotions using voice analysis technology. This allows for the reservation of more appropriate accommodations by determining reservation priorities 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI or not using AI. For example, the reservation unit inputs the user's emotion data into an AI, the AI ​​estimates the emotions, and determines reservation priorities based on those emotions.

[0095] The reservation department can book the most suitable accommodation by taking into account the user's geographical location. For example, the reservation department can prioritize booking accommodations close to the user's current location. It can also prioritize booking accommodations close to the user's destination. For example, the reservation department can prioritize booking accommodations along the user's travel route. In this way, the most suitable accommodation can be booked by taking into account the user's geographical location. Some or all of the above processing in the reservation department may be performed using AI, or not. For example, the reservation department inputs the user's geographical location information into the AI, and the AI ​​uses that information to book the most suitable accommodation.

[0096] The booking department can analyze a user's social media activity and suggest relevant accommodations. For example, the booking department can suggest accommodations based on travel plans shared by the user on social media. It can also suggest accommodations based on places the user follows on social media. For example, the booking department can suggest accommodations based on places the user has checked into on social media. In this way, relevant accommodations can be suggested by analyzing the user's social media activity. Some or all of the above processing in the booking department may be performed using AI, for example, or not. For example, the booking department can input the user's social media activity into AI, and the AI ​​can suggest relevant accommodations based on that information.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The AI ​​hotel reservation assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodations based on the user's travel purpose. For example, for business travel, it prioritizes hotels with ample meeting rooms and business centers. For sightseeing travel, it can suggest accommodations close to tourist attractions. For family trips, it can suggest hotels with family rooms and kids' clubs. This allows the system to suggest the most suitable accommodations for each user's travel purpose.

[0099] The AI ​​hotel reservation assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodation based on the user's health condition. For example, if the user has allergies, it will prioritize suggesting hotels that offer allergy-friendly rooms. If the user is undergoing rehabilitation, it can suggest hotels with excellent rehabilitation facilities. Additionally, if the user is health-conscious, it can suggest hotels that offer healthy meals. This allows the system to suggest the most suitable accommodation based on the user's health needs.

[0100] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodations based on the user's budget. For example, if the user is traveling on a low budget, it will prioritize suggesting cost-effective accommodations. If the user desires a luxury hotel, it can suggest luxury hotels. If the user has a mid-range budget, it can suggest accommodations that offer a good balance. This allows the system to suggest the most suitable accommodations according to the user's budget.

[0101] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can also suggest the most suitable accommodations based on the user's past travel patterns. For example, it can prioritize suggesting accommodations in cities the user has frequently visited in the past. It can also suggest accommodations that the user has previously given high ratings to. Additionally, it can consider benefits and promotions from accommodations the user has used in the past when making suggestions. This allows it to suggest the most suitable accommodations based on the user's past travel patterns.

[0102] The AI ​​hotel reservation assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can estimate the user's mood and suggest accommodation amenities based on that mood. For example, if the user wants to relax, it can suggest hotels with spas and massage services. If the user is in an active mood, it can suggest hotels with fitness centers and swimming pools. Additionally, if the user wants to enjoy time with family, it can suggest hotels with kids' clubs and family rooms. This allows the system to suggest the most suitable accommodation amenities based on the user's mood.

[0103] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can estimate the user's mood and suggest accommodation meal plans based on that mood. For example, if the user wants to relax, it can suggest hotels offering healthy meal plans. If the user is in an active mood, it can suggest hotels offering meal plans suitable for energy replenishment. Additionally, if the user wants to enjoy time with family, it can suggest hotels offering family-friendly meal plans. This allows the system to suggest the optimal accommodation meal plan based on the user's mood.

[0104] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can estimate the user's mood and suggest leisure activities at accommodations based on that mood. For example, if the user feels like relaxing, it can suggest hotels offering yoga or meditation classes. If the user is in an active mood, it can suggest hotels offering hiking or cycling tours. Additionally, if the user wants to enjoy time with family, it can suggest hotels offering family-friendly activities. This allows the system to suggest the most suitable leisure activities at accommodations based on the user's mood.

[0105] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can estimate the user's mood and suggest room types based on that mood. For example, if the user wants to relax, it can suggest hotels offering rooms in a quiet environment. If the user is in an active mood, it can suggest hotels with rooms that provide easy access to activities. Additionally, if the user wants to enjoy time with family, it can suggest hotels offering family rooms. This allows the system to suggest the most suitable room type based on the user's mood.

[0106] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can estimate the user's mood and suggest check-in and check-out times based on that mood. For example, if the user wants to relax, it can suggest hotels with late check-out times. If the user is in an active mood, it can suggest hotels with early check-in times. Additionally, if the user wants to enjoy time with family, it can suggest hotels with flexible check-in and check-out times. This allows the system to suggest the optimal check-in and check-out times for accommodations based on the user's mood.

[0107] The hotel reservation support AI assistant is a system that searches for accommodations based on the user's preferences, checks availability in real time, and makes reservations automatically. Furthermore, it can estimate the user's mood and suggest special services from accommodations based on that mood. For example, if the user wants to relax, it can suggest hotels that offer room service or private dining. If the user is in an active mood, it can suggest hotels that offer activity booking services. Additionally, if the user wants to enjoy time with family, it can suggest hotels that offer babysitting services or kids' programs. This allows the system to suggest the most suitable accommodations and special services based on the user's mood.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives input from the user regarding their desired conditions. These conditions may include, but are not limited to, location, price, and services. The reception desk can accept input via voice or chat, for example. The reception desk can also simplify the input process by automatically obtaining the user's current location information. For example, the reception desk can obtain the user's current location information using GPS or Wi-Fi location information. Step 2: The search unit searches for accommodations based on the user's preferences received by the reception unit. The search unit can search for accommodations that match the user's preferences based on the user's past stay history and ratings, for example. For example, the search unit will prioritize searching for accommodations that have received high ratings in the past. The search unit can also estimate the user's emotions and search for accommodations that match those estimated emotions. For example, if the search unit thinks the user wants to relax, it will recommend accommodations with a relaxing environment. Step 3: The confirmation unit checks the reservation status of the search results obtained by the search unit in real time. For example, the confirmation unit can obtain and display the availability status of accommodations in real time. The confirmation unit can also update the availability status in real time when a reservation is confirmed or a cancellation occurs. Step 4: The reservation unit automatically reserves the available accommodations confirmed by the verification unit. The reservation unit can, for example, automatically reserve the most suitable accommodation based on the user's preferences.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts input via voice or chat. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for accommodations based on the user's desired conditions. For example, the confirmation unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the reservation status of the search results in real time. For example, the reservation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically reserves available accommodations. Each of the reception unit, search unit, confirmation unit, and reservation unit may also be implemented by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts voice input and input in chat format. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for accommodations based on the user's desired conditions. For example, the confirmation unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the reservation status of the search results in real time. For example, the reservation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically reserves available accommodations. Each of the reception unit, search unit, confirmation unit, and reservation unit may also be implemented by, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts voice input and chat-style input. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for accommodations based on the user's desired conditions. For example, the confirmation unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the reservation status of the search results in real time. For example, the reservation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically reserves available accommodations. Each of the reception unit, search unit, confirmation unit, and reservation unit may also be implemented by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts voice input and input in chat format. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for accommodations based on the user's desired conditions. For example, the confirmation unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the reservation status of the search results in real time. For example, the reservation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically reserves available accommodations. Each of the reception unit, search unit, confirmation unit, and reservation unit may also be implemented by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception desk that accepts input from users regarding their desired conditions, A search unit that searches for accommodations based on the desired conditions received by the reception unit, A confirmation unit that checks the reservation status of the search results obtained by the search unit in real time, The system includes a reservation unit that automatically reserves the bookable accommodations confirmed by the verification unit. A system characterized by the following features. (Note 2) The aforementioned search unit, Search for accommodations that match the user's preferences based on their past stay history and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It features an interface that accepts input via voice or chat. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It includes a security department that handles data anonymization and security measures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, It estimates the user's emotions and searches for accommodations that match those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system automatically retrieves the user's current location information to simplify input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting desired conditions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system automatically completes input based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Provides the optimal input method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is We analyze users' social media activity and suggest relevant conditions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, We estimate the user's emotions and adjust how search results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, Filter search results based on accommodation ratings and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, Optimize search results by considering accommodation benefits and promotional information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, The system prioritizes displaying relevant accommodations based on the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, We suggest the most suitable accommodation based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is The system estimates the user's emotions and adjusts how the reservation status is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is Real-time updates on accommodation availability. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is Adjust the displayed content to take into account the accommodation's cancellation policy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is The system estimates the user's emotions and prioritizes reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is The system displays relevant information by referencing the user's past booking history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is Provides the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the booking process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, We suggest the optimal booking method by referring to the user's past booking history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, Optimize your booking by taking into account accommodation benefits and promotional information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, The system takes the user's geographical location into consideration when booking the most suitable accommodation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, We analyze users' social media activity and suggest relevant accommodations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input from users regarding their desired conditions, A search unit that searches for accommodations based on the desired conditions received by the reception unit, A confirmation unit that checks the reservation status of the search results obtained by the search unit in real time, The system includes a reservation unit that automatically reserves the bookable accommodations confirmed by the verification unit. A system characterized by the following features.

2. The aforementioned search unit, Search for accommodations that match the user's preferences based on their past stay history and ratings. The system according to feature 1.

3. The aforementioned reception unit is It features an interface that accepts input via voice or chat. The system according to feature 1.

4. The aforementioned reception unit is It includes a security department that handles data anonymization and security measures. The system according to feature 1.

5. The aforementioned search unit, It estimates the user's emotions and searches for accommodations that match those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is The system automatically retrieves the user's current location information to simplify input. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting desired conditions based on those estimated emotions. The system according to feature 1.

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