system

Abstract

The system according to this embodiment aims to provide users with information on accommodations and services that meet their needs and budgets, and to automate reservations and inquiries. [Solution] The system according to the embodiment comprises a reception unit, a provision unit, and an automation unit. The reception unit receives information from the user regarding requests and budget. The provision unit provides information on accommodations or services that meet the user's requests and budget based on the information received by the reception unit. The automation unit makes reservations or inquiries about accommodations or services based on the information provided by the provision 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 the chatbot's 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 conventional technology, there was a problem that accommodation facilities and service information corresponding to user desires and budgets were not sufficiently provided, and the reservation and inquiry processes were complicated.

[0005] The system according to the embodiment aims to provide accommodation facilities and service information corresponding to user desires and budgets and automate reservations and inquiries.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a provision unit, and an automation unit. The reception unit receives information from the user regarding their requests and budget. Based on the information received by the reception unit, the provision unit provides information on accommodations or services that meet the user's requests and budget. Based on the information provided by the provision unit, the automation unit makes reservations or inquiries regarding accommodations or services. [Effects of the Invention]

[0007] The system according to this embodiment can provide information on accommodations and services that meet the user's needs and budget, and can automate reservations and inquiries. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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, the RAM 30, and the 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 virtual concierge according to an embodiment of the present invention is a system that provides information on accommodations and services within facilities. This system has the function of recommending activities and restaurants according to the user's requests and budget, and automating reservations and inquiries. First, the system receives information on the user's requests and budget. Next, based on the received information, the system provides information on accommodations or services according to the user's requests and budget. Furthermore, the system automates reservations or inquiries for accommodations or services based on the provided information. This system provides information by referring to a facility database and analyzing the user's requests and budget to provide information on appropriate activities or restaurants. The system also has the function of automatically performing reservation procedures and providing confirmation messages. Furthermore, the system can estimate the user's emotions and provide information according to the estimated emotions, and can also provide information by analyzing past inquiry history. When receiving inquiries, the system can also filter them based on the user's current situation and areas of interest. As a result, the AI ​​assistant virtual concierge can provide information on accommodations and services according to the user's requests and budget, and automate reservations and inquiries.

[0029] The AI ​​assistant for the virtual concierge according to this embodiment comprises a reception unit, a provision unit, and an automation unit. The reception unit receives information from the user regarding requests and budget. Requests include, for example, the type of accommodation and the type of service. Budgets include, for example, the price per night and the total budget. Based on the information received by the reception unit, the provision unit provides information on accommodations or services that meet the user's requests and budget. Accommodation information includes, for example, the name of the facility, location, rates, and facilities. Service information includes, for example, the type of service offered, rates, and available hours. The provision unit can provide information by referring to a database of facilities. Based on the information provided by the provision unit, the automation unit makes reservations or inquiries about accommodations or services. Reservation procedures include, for example, online reservations and telephone reservations. Confirmation messages include, for example, emails and SMS messages. This will enable the virtual concierge AI assistant to provide information on accommodations and services tailored to the user's needs and budget, and to automate bookings and inquiries.

[0030] The reception desk receives information from users regarding their requests and budget. Requests include, but are not limited to, the type of accommodation and services they require. Specifically, users can enter detailed information about the type of accommodation they desire (hotel, resort, guesthouse, etc.) and the services they want to be offered (spa, restaurant, tour guide, etc.). The reception desk can also accept user preferences and specific requests (e.g., non-smoking room, pet-friendly, room with ocean view, etc.). Budgets include, but are not limited to, the cost per night or the total budget. Users can enter their budget limit and desired price range, and can also consider price fluctuations during specific periods or seasons. The reception desk efficiently collects this information and stores it in a database. The information entered by users is analyzed by AI and used as foundational data to make optimal suggestions. For example, based on the user's requests and budget, the AI ​​analyzes past data and trends to identify the most suitable accommodations and services. This allows the reception desk to play a crucial role in providing personalized suggestions tailored to the user's needs.

[0031] The service provider provides information on accommodations or services that meet the user's needs and budget, based on the information received by the reception department. Accommodation information includes, but is not limited to, the name, location, price, and facilities of the facility. Specifically, the service provider provides detailed information on multiple accommodations in a format that makes comparison easy, according to the user's request. For example, it can display a list of facility names, locations, prices, facilities, available services, and user ratings and reviews. Service information includes, but is not limited to, the types of services offered, prices, and available hours. The service provider can provide information by referring to a database of facilities. The database is constantly updated with the latest information, allowing for the rapid provision of the information the user needs. Furthermore, the service provider uses AI to make the most suitable suggestions for the user's needs. For example, if a user wishes to stay in a specific area, the AI ​​will prioritize suggesting popular or highly-rated facilities in that area. It can also make personalized suggestions based on the user's past usage history and ratings. This allows the service provider to provide users with the most suitable accommodations and services, increasing their satisfaction.

[0032] The automation unit makes reservations or inquiries for accommodations or services based on information provided by the service provider. Reservation procedures include, but are not limited to, online or telephone reservations. Specifically, the automation unit automatically handles the reservation process for the accommodation or service selected by the user. For online reservations, it automatically fills out the reservation form based on the information entered by the user and completes the reservation. For telephone reservations, the AI ​​uses an automated voice response system to contact the accommodation or service provider directly and make the reservation. Confirmation messages include, but are not limited to, email or SMS. Once the reservation is complete, the automation unit sends a confirmation message to the user notifying them of the reservation details. This allows the user to review the reservation details and make changes or cancellations as needed. Furthermore, the automation unit tracks the reservation progress in real time and provides the user with the latest information. For example, if a reservation is confirmed or changed, the user is immediately notified and prompted to take action. The automation unit also uses AI to optimize reservations. For example, it adjusts reservations to avoid overlaps and notifies users on the waiting list when a vacancy becomes available. This allows the automated unit to process user reservations efficiently and accurately, improving convenience.

[0033] The service provider can provide information by referring to a facility database. This database may include, but is not limited to, information such as the name, location, price, and facilities of accommodations. It is desirable that the facility database be updated regularly. This ensures that accurate information can be provided by referring to the facility database. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not. For example, the service provider can input information obtained from the facility database into a generating AI and have the generating AI generate information to provide to the user.

[0034] The service provider can analyze the user's requests and budget and provide information on appropriate activities and restaurants. Activities include, but are not limited to, sports, sightseeing, and entertainment. Restaurant information includes, but is not limited to, the restaurant's name, location, menu, and prices. The service provider can use AI to analyze the user's requests and budget. For example, the service provider can provide information using an AI model that takes the user's requests and budget as input and outputs information on appropriate activities and restaurants. This allows the service provider to provide information on activities and restaurants that meet the user's requests and budget.

[0035] The automation unit can automatically perform reservation procedures and provide confirmation messages. Reservation procedures include, but are not limited to, online or telephone reservations. Confirmation messages include, but are not limited to, email or SMS. The automation unit can use AI to automate reservation procedures. For example, the automation unit can automate reservation procedures using an AI model that takes user reservation information as input and outputs the reservation procedure. This improves user convenience by automating the reservation procedure and providing confirmation messages.

[0036] The service provider can analyze a user's past inquiry history and provide accommodation or service information based on the analysis results. Past inquiry history includes, but is not limited to, the date, time, content, and outcome of the inquiry. The service provider can use AI to analyze past inquiry history. For example, the service provider can provide information using an AI model that takes past inquiry history as input and outputs analysis results. This allows for the provision of more appropriate information by analyzing past inquiry history.

[0037] The reception desk can filter inquiries based on the user's current situation and areas of interest. Current situation includes, but is not limited to, current location, purpose of travel, and whether or not the user is traveling with others. Areas of interest include, but is not limited to, hobbies, activities of interest, and preferred foods. The reception desk can use AI to perform this filtering. For example, the reception desk can provide information using an AI model that takes the user's current situation and areas of interest as input and outputs filtered results. This allows for the provision of more relevant information by filtering based on the user's current situation and areas of interest.

[0038] The reception department can analyze users' past requests and budget information to select the optimal reception method. Past requests and budget information includes, but is not limited to, the content of the request, the budget range, and the date and time. The reception department can use AI to analyze past requests and budget information. For example, the reception department can select a reception method using an AI model that takes past requests and budget information as input and outputs the optimal reception method. This allows for the selection of the optimal reception method by analyzing past requests and budget information.

[0039] The reception desk can filter requests and budgets based on the user's current travel plans and areas of interest. Current travel plans may include, but are not limited to, travel destinations, dates, and whether or not there are travel companions. Areas of interest may include, but are not limited to, outdoor activities and gourmet food. The reception desk can use AI to perform this filtering. For example, the reception desk can provide information using an AI model that takes the user's current travel plans and areas of interest as input and outputs filtered results. This allows for the reception of more appropriate requests and budgets by filtering based on current travel plans and areas of interest.

[0040] The reception desk can prioritize receiving highly relevant information when receiving requests and budgets, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data, address information, and location services. The reception desk can use AI to consider geographical location. For example, it can provide information using an AI model that takes the user's geographical location as input and outputs highly relevant information. This allows for the priority of receiving highly relevant information by considering geographical location.

[0041] The reception department can analyze users' social media activity and receive relevant information when receiving requests and budgets. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. The reception department can use AI to analyze social media activity. For example, the reception department can provide information using an AI model that takes users' social media activity as input and outputs relevant information. This allows the reception department to receive relevant information by analyzing social media activity.

[0042] The service provider can adjust the level of detail of information provided based on the importance of the accommodation and service. This importance includes, but is not limited to, user ratings, frequency of use, and price range. The service provider can use AI to adjust the level of detail. For example, the service provider can provide information using an AI model that takes the importance of the accommodation and service as input and outputs the level of detail. This allows for the provision of more relevant information by adjusting the level of detail based on the importance of the accommodation and service.

[0043] The service provider can apply different service provision algorithms depending on the category of accommodation and service at the time of provision. Categories include, but are not limited to, hotels, resorts, business hotels, restaurants, and activities. The service provider can use AI to apply the service provision algorithms. For example, the service provider can provide information using an AI model that takes accommodation and service categories as input and outputs a service provision algorithm. This allows for the provision of more appropriate information by applying different service provision algorithms depending on the category of accommodation and service.

[0044] The service provider can prioritize information based on the availability of accommodations and services at the time of provision. Availability includes, but is not limited to, seasons, event periods, and booking periods. The service provider can use AI to prioritize information. For example, the service provider can provide information using an AI model that takes the availability of accommodations and services as input and outputs a priority order for the information. This allows for the provision of more relevant information by prioritizing it based on availability.

[0045] The service provider can adjust the order of information based on the relevance of accommodations and services at the time of delivery. Relevance includes, but is not limited to, the degree of match with user requests and relevance to past usage history. The service provider can use AI to adjust the order of information. For example, the service provider can provide information using an AI model that takes the relevance of accommodations and services as input and outputs the order of information. This allows for the provision of more appropriate information by adjusting the order of information based on relevance.

[0046] The automation unit can analyze the user's past reservation history to select the optimal automation method during automation. Past reservation history includes, but is not limited to, the date, time, content, and results of reservations. The automation unit can use AI to analyze past reservation history. For example, the automation unit can select a reservation method using an AI model that takes past reservation history as input and outputs the optimal automation method. This allows for the selection of the optimal automation method by analyzing past reservation history.

[0047] The automation unit can customize the automation process based on the user's current travel plan. This current travel plan may include, but is not limited to, the travel destination, dates, and whether or not there are travel companions. The automation unit can use AI to customize the automation process. For example, it can suggest booking methods using an AI model that takes the user's current travel plan as input and outputs automation methods. This allows for more appropriate booking or inquiry automation by customizing the automation process based on the current travel plan.

[0048] The automation unit can select the optimal automation method by considering the user's geographical location information during automation. Geographical location information includes, but is not limited to, GPS data, address information, and location services. The automation unit can use AI to consider geographical location information. For example, the automation unit can propose a reservation method using an AI model that takes the user's geographical location information as input and outputs the optimal automation method. This allows for the selection of the optimal automation method by considering geographical location information.

[0049] The automation unit can analyze the user's social media activity during automation and propose automation methods. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. The automation unit can use AI to analyze social media activity. For example, the automation unit can propose a reservation method using an AI model that takes the user's social media activity as input and outputs automation methods. In this way, by analyzing social media activity, the optimal automation method can be proposed.

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

[0051] The service provider can analyze a user's past travel history and provide information on accommodations and services based on the analysis results. Past travel history includes, but is not limited to, places visited, length of stay, and services used. The service provider can use AI to analyze past travel history. For example, the service provider can provide information using an AI model that takes past travel history as input and outputs analysis results. This allows the service provider to provide information tailored to the user's preferences by analyzing past travel history.

[0052] The automation unit can adjust the automated booking or inquiry process based on the user's current schedule. This current schedule includes, but is not limited to, scheduled meetings, events, and travel time. The automation unit can utilize AI to consider the schedule. For example, it can suggest booking methods using an AI model that takes the user's schedule as input and outputs the optimal automation method. This allows for more appropriate automated booking or inquiry processes by adjusting the automation method based on the schedule.

[0053] The reception desk can adjust the method of receiving requests and budgets by taking into account the user's current weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed. The reception desk can use AI to take weather information into account. For example, the reception desk can provide information using an AI model that takes the user's current weather information as input and outputs the optimal method of receiving the request. This allows for more appropriate requests and budgets to be received by taking weather information into account.

[0054] The automation unit can analyze past user feedback and adjust the automated booking or inquiry process based on the analysis results. Past feedback may include, but is not limited to, satisfaction levels, areas for improvement, and requests. The automation unit can utilize AI to analyze past feedback. For example, it can suggest booking methods using an AI model that takes past feedback as input and outputs the optimal automation method. This allows for more appropriate automated booking or inquiry processes by analyzing past feedback.

[0055] The reception desk can adjust the method of receiving requests and budgets by taking into account the user's current device information. Device information includes, but is not limited to, the type of device being used, screen size, and operating method. The reception desk can use AI to take device information into account. For example, the reception desk can provide information using an AI model that takes the user's device information as input and outputs the optimal reception method. This makes it possible to receive requests and budgets more appropriately by taking device information into account.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives information from the user regarding their requests and budget. Requests may include, but are not limited to, the type of accommodation and services provided. Budgets may include, but are not limited to, the cost per night and the total budget. Step 2: Based on the information received by the reception department, the service department provides information on accommodations or services that meet the user's needs and budget. Accommodation information includes, but is not limited to, the name, location, rates, and facilities of the facility. Service information includes, but is not limited to, the types of services offered, rates, and available hours. The service department may provide information by referring to a database of facilities. Step 3: The automation unit makes a reservation or inquiry for accommodation or services based on the information provided by the service unit. Reservation procedures include, but are not limited to, online or telephone reservations. Confirmation messages include, but are not limited to, email or SMS messages.

[0058] (Example of form 2) An AI assistant virtual concierge according to an embodiment of the present invention is a system that provides information on accommodations and services within facilities. This system has the function of recommending activities and restaurants according to the user's requests and budget, and automating reservations and inquiries. First, the system receives information on the user's requests and budget. Next, based on the received information, the system provides information on accommodations or services according to the user's requests and budget. Furthermore, the system automates reservations or inquiries for accommodations or services based on the provided information. This system provides information by referring to a facility database and analyzing the user's requests and budget to provide information on appropriate activities or restaurants. The system also has the function of automatically performing reservation procedures and providing confirmation messages. Furthermore, the system can estimate the user's emotions and provide information according to the estimated emotions, and can also provide information by analyzing past inquiry history. When receiving inquiries, the system can also filter them based on the user's current situation and areas of interest. As a result, the AI ​​assistant virtual concierge can provide information on accommodations and services according to the user's requests and budget, and automate reservations and inquiries.

[0059] The AI ​​assistant for the virtual concierge according to this embodiment comprises a reception unit, a provision unit, and an automation unit. The reception unit receives information from the user regarding requests and budget. Requests include, for example, the type of accommodation and the type of service. Budgets include, for example, the price per night and the total budget. Based on the information received by the reception unit, the provision unit provides information on accommodations or services that meet the user's requests and budget. Accommodation information includes, for example, the name of the facility, location, rates, and facilities. Service information includes, for example, the type of service offered, rates, and available hours. The provision unit can provide information by referring to a database of facilities. Based on the information provided by the provision unit, the automation unit makes reservations or inquiries about accommodations or services. Reservation procedures include, for example, online reservations and telephone reservations. Confirmation messages include, for example, emails and SMS messages. This will enable the virtual concierge AI assistant to provide information on accommodations and services tailored to the user's needs and budget, and to automate bookings and inquiries.

[0060] The reception desk receives information from users regarding their requests and budget. Requests include, but are not limited to, the type of accommodation and services they require. Specifically, users can enter detailed information about the type of accommodation they desire (hotel, resort, guesthouse, etc.) and the services they want to be offered (spa, restaurant, tour guide, etc.). The reception desk can also accept user preferences and specific requests (e.g., non-smoking room, pet-friendly, room with ocean view, etc.). Budgets include, but are not limited to, the cost per night or the total budget. Users can enter their budget limit and desired price range, and can also consider price fluctuations during specific periods or seasons. The reception desk efficiently collects this information and stores it in a database. The information entered by users is analyzed by AI and used as foundational data to make optimal suggestions. For example, based on the user's requests and budget, the AI ​​analyzes past data and trends to identify the most suitable accommodations and services. This allows the reception desk to play a crucial role in providing personalized suggestions tailored to the user's needs.

[0061] The service provider provides information on accommodations or services that meet the user's needs and budget, based on the information received by the reception department. Accommodation information includes, but is not limited to, the name, location, price, and facilities of the facility. Specifically, the service provider provides detailed information on multiple accommodations in a format that makes comparison easy, according to the user's request. For example, it can display a list of facility names, locations, prices, facilities, available services, and user ratings and reviews. Service information includes, but is not limited to, the types of services offered, prices, and available hours. The service provider can provide information by referring to a database of facilities. The database is constantly updated with the latest information, allowing for the rapid provision of the information the user needs. Furthermore, the service provider uses AI to make the most suitable suggestions for the user's needs. For example, if a user wishes to stay in a specific area, the AI ​​will prioritize suggesting popular or highly-rated facilities in that area. It can also make personalized suggestions based on the user's past usage history and ratings. This allows the service provider to provide users with the most suitable accommodations and services, increasing their satisfaction.

[0062] The automation unit makes reservations or inquiries for accommodations or services based on information provided by the service provider. Reservation procedures include, but are not limited to, online or telephone reservations. Specifically, the automation unit automatically handles the reservation process for the accommodation or service selected by the user. For online reservations, it automatically fills out the reservation form based on the information entered by the user and completes the reservation. For telephone reservations, the AI ​​uses an automated voice response system to contact the accommodation or service provider directly and make the reservation. Confirmation messages include, but are not limited to, email or SMS. Once the reservation is complete, the automation unit sends a confirmation message to the user notifying them of the reservation details. This allows the user to review the reservation details and make changes or cancellations as needed. Furthermore, the automation unit tracks the reservation progress in real time and provides the user with the latest information. For example, if a reservation is confirmed or changed, the user is immediately notified and prompted to take action. The automation unit also uses AI to optimize reservations. For example, it adjusts reservations to avoid overlaps and notifies users on the waiting list when a vacancy becomes available. This allows the automated unit to process user reservations efficiently and accurately, improving convenience.

[0063] The service provider can provide information by referring to a facility database. This database may include, but is not limited to, information such as the name, location, price, and facilities of accommodations. It is desirable that the facility database be updated regularly. This ensures that accurate information can be provided by referring to the facility database. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not. For example, the service provider can input information obtained from the facility database into a generating AI and have the generating AI generate information to provide to the user.

[0064] The service provider can analyze the user's requests and budget and provide information on appropriate activities and restaurants. Activities include, but are not limited to, sports, sightseeing, and entertainment. Restaurant information includes, but is not limited to, the restaurant's name, location, menu, and prices. The service provider can use AI to analyze the user's requests and budget. For example, the service provider can provide information using an AI model that takes the user's requests and budget as input and outputs information on appropriate activities and restaurants. This allows the service provider to provide information on activities and restaurants that meet the user's requests and budget.

[0065] The automation unit can automatically perform reservation procedures and provide confirmation messages. Reservation procedures include, but are not limited to, online or telephone reservations. Confirmation messages include, but are not limited to, email or SMS. The automation unit can use AI to automate reservation procedures. For example, the automation unit can automate reservation procedures using an AI model that takes user reservation information as input and outputs the reservation procedure. This improves user convenience by automating the reservation procedure and providing confirmation messages.

[0066] The service provider can estimate the user's emotions and provide accommodation and service information tailored to the estimated emotions of the user. 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. The service provider can, for example, capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the service provider calculates an emotion score based on changes in facial expression. The service provider can also record the user's voice and estimate the emotion using voice analysis technology. For example, the service provider analyzes the tone and speed of the voice and calculates an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, the service provider calculates an emotion score based on fluctuations in heart rate. This allows the service provider to provide more appropriate services by providing information tailored to the user's emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input image data of the user captured by a camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0067] The service provider can analyze a user's past inquiry history and provide accommodation or service information based on the analysis results. Past inquiry history includes, but is not limited to, the date, time, content, and outcome of the inquiry. The service provider can use AI to analyze past inquiry history. For example, the service provider can provide information using an AI model that takes past inquiry history as input and outputs analysis results. This allows for the provision of more appropriate information by analyzing past inquiry history.

[0068] The reception desk can filter inquiries based on the user's current situation and areas of interest. Current situation includes, but is not limited to, current location, purpose of travel, and whether or not the user is traveling with others. Areas of interest include, but is not limited to, hobbies, activities of interest, and preferred foods. The reception desk can use AI to perform this filtering. For example, the reception desk can provide information using an AI model that takes the user's current situation and areas of interest as input and outputs filtered results. This allows for the provision of more relevant information by filtering based on the user's current situation and areas of interest.

[0069] The reception desk can estimate the user's emotions and adjust the method of receiving requests and budgets based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of requests and budgets. This allows for more appropriate requests and budgets to be received by adjusting the reception method according to the user's emotions. 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 can input user emotion data into a generative AI and have the generative AI adjust the method of receiving requests and budgets.

[0070] The reception department can analyze users' past requests and budget information to select the optimal reception method. Past requests and budget information includes, but is not limited to, the content of the request, the budget range, and the date and time. The reception department can use AI to analyze past requests and budget information. For example, the reception department can select a reception method using an AI model that takes past requests and budget information as input and outputs the optimal reception method. This allows for the selection of the optimal reception method by analyzing past requests and budget information.

[0071] The reception desk can filter requests and budgets based on the user's current travel plans and areas of interest. Current travel plans may include, but are not limited to, travel destinations, dates, and whether or not there are travel companions. Areas of interest may include, but are not limited to, outdoor activities and gourmet food. The reception desk can use AI to perform this filtering. For example, the reception desk can provide information using an AI model that takes the user's current travel plans and areas of interest as input and outputs filtered results. This allows for the reception of more appropriate requests and budgets by filtering based on current travel plans and areas of interest.

[0072] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of requests and budgets to be accepted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the reception desk can prioritize requests and budgets that promote relaxation. Similarly, if the user is excited, the reception desk can prioritize requests and budgets that promote activity. If the user is tired, the reception desk can also prioritize requests and budgets that promote relaxation. This allows for more appropriate requests and budgets to be accepted by determining priorities according to the user's emotions. 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 can input user emotion data into a generative AI and have the generative AI determine the priority of requests and budgets.

[0073] The reception desk can prioritize receiving highly relevant information when receiving requests and budgets, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data, address information, and location services. The reception desk can use AI to consider geographical location. For example, it can provide information using an AI model that takes the user's geographical location as input and outputs highly relevant information. This allows for the priority of receiving highly relevant information by considering geographical location.

[0074] The reception department can analyze users' social media activity and receive relevant information when receiving requests and budgets. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. The reception department can use AI to analyze social media activity. For example, the reception department can provide information using an AI model that takes users' social media activity as input and outputs relevant information. This allows the reception department to receive relevant information by analyzing social media activity.

[0075] The information provider can estimate the user's emotions and adjust the way information is delivered based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is relaxed, the information provider can provide detailed information and broaden the range of choices. If the user is in a hurry, the information provider can provide concise information that gets straight to the point. If the user is excited, the information provider can provide visually appealing information. In this way, more appropriate information can be provided by adjusting the way information is delivered according to the user's emotions. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the way information is delivered.

[0076] The service provider can adjust the level of detail of information provided based on the importance of the accommodation and service. This importance includes, but is not limited to, user ratings, frequency of use, and price range. The service provider can use AI to adjust the level of detail. For example, the service provider can provide information using an AI model that takes the importance of the accommodation and service as input and outputs the level of detail. This allows for the provision of more relevant information by adjusting the level of detail based on the importance of the accommodation and service.

[0077] The service provider can apply different service provision algorithms depending on the category of accommodation and service at the time of provision. Categories include, but are not limited to, hotels, resorts, business hotels, restaurants, and activities. The service provider can use AI to apply the service provision algorithms. For example, the service provider can provide information using an AI model that takes accommodation and service categories as input and outputs a service provision algorithm. This allows for the provision of more appropriate information by applying different service provision algorithms depending on the category of accommodation and service.

[0078] The information provider can estimate the user's emotions and prioritize information based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is relaxed, the information provider can prioritize detailed information. If the user is in a hurry, the information provider can prioritize concise information. If the user is excited, the information provider can prioritize visually appealing information. By prioritizing information according to the user's emotions, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0079] The service provider can prioritize information based on the availability of accommodations and services at the time of provision. Availability includes, but is not limited to, seasons, event periods, and booking periods. The service provider can use AI to prioritize information. For example, the service provider can provide information using an AI model that takes the availability of accommodations and services as input and outputs a priority order for the information. This allows for the provision of more relevant information by prioritizing it based on availability.

[0080] The service provider can adjust the order of information based on the relevance of accommodations and services at the time of delivery. Relevance includes, but is not limited to, the degree of match with user requests and relevance to past usage history. The service provider can use AI to adjust the order of information. For example, the service provider can provide information using an AI model that takes the relevance of accommodations and services as input and outputs the order of information. This allows for the provision of more appropriate information by adjusting the order of information based on relevance.

[0081] The automation unit can estimate the user's emotions and adjust the automated booking or inquiry method based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is relaxed, the automation unit can provide detailed booking options and broaden the choices. If the user is in a hurry, the automation unit can provide concise booking options that get straight to the point. If the user is excited, the automation unit can provide visually appealing booking options. This allows for more appropriate automated booking or inquiry by adjusting the automation method according to the user's emotions. Some or all of the above processing in the automation unit may be performed using AI, for example, or not using AI. For example, the automation unit can input user emotion data into a generative AI and have the generative AI adjust the automated booking or inquiry method.

[0082] The automation unit can analyze the user's past reservation history to select the optimal automation method during automation. Past reservation history includes, but is not limited to, the date, time, content, and results of reservations. The automation unit can use AI to analyze past reservation history. For example, the automation unit can select a reservation method using an AI model that takes past reservation history as input and outputs the optimal automation method. This allows for the selection of the optimal automation method by analyzing past reservation history.

[0083] The automation unit can customize the automation process based on the user's current travel plan. This current travel plan may include, but is not limited to, the travel destination, dates, and whether or not there are travel companions. The automation unit can use AI to customize the automation process. For example, it can suggest booking methods using an AI model that takes the user's current travel plan as input and outputs automation methods. This allows for more appropriate booking or inquiry automation by customizing the automation process based on the current travel plan.

[0084] The automation unit can estimate the user's emotions and determine automation priorities based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is relaxed, the automation unit can prioritize providing detailed booking options. If the user is in a hurry, the automation unit can prioritize providing concise booking options that get straight to the point. If the user is excited, the automation unit can prioritize providing visually appealing booking options. This allows for more appropriate automated booking or inquiry processing by determining automation priorities according to the user's emotions. Some or all of the above processing in the automation unit may be performed using AI, for example, or not using AI. For example, the automation unit can input user emotion data into a generative AI and have the generative AI determine automation priorities.

[0085] The automation unit can select the optimal automation method by considering the user's geographical location information during automation. Geographical location information includes, but is not limited to, GPS data, address information, and location services. The automation unit can use AI to consider geographical location information. For example, the automation unit can propose a reservation method using an AI model that takes the user's geographical location information as input and outputs the optimal automation method. This allows for the selection of the optimal automation method by considering geographical location information.

[0086] The automation unit can analyze the user's social media activity during automation and propose automation methods. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. The automation unit can use AI to analyze social media activity. For example, the automation unit can propose a reservation method using an AI model that takes the user's social media activity as input and outputs automation methods. In this way, by analyzing social media activity, the optimal automation method can be proposed.

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

[0088] The reception desk can monitor the user's current health status and adjust the way requests and budgets are received based on that status. For example, if the user is tired, a simple interface can be provided, minimizing the input steps. If the user is energetic, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is stressed, requests and budgets that promote relaxation can be prioritized. This allows for more appropriate requests and budgets to be received by adjusting the reception method according to the user's health status.

[0089] The service provider can analyze a user's past travel history and provide information on accommodations and services based on the analysis results. Past travel history includes, but is not limited to, places visited, length of stay, and services used. The service provider can use AI to analyze past travel history. For example, the service provider can provide information using an AI model that takes past travel history as input and outputs analysis results. This allows the service provider to provide information tailored to the user's preferences by analyzing past travel history.

[0090] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is relaxed, it can select a timing to deliver detailed information. If the user is in a hurry, it can quickly deliver concise information. Furthermore, if the user is excited, it can select a timing to deliver visually appealing information. By adjusting the timing of information delivery according to the user's emotions, more appropriate information can be provided.

[0091] The automation unit can adjust the automated booking or inquiry process based on the user's current schedule. This current schedule includes, but is not limited to, scheduled meetings, events, and travel time. The automation unit can utilize AI to consider the schedule. For example, it can suggest booking methods using an AI model that takes the user's schedule as input and outputs the optimal automation method. This allows for more appropriate automated booking or inquiry processes by adjusting the automation method based on the schedule.

[0092] The information provider can estimate the user's emotions and adjust the information display format based on those emotions. For example, if the user is relaxed, detailed text information can be displayed. If the user is in a hurry, information can be displayed in a concise bulleted list format. Furthermore, if the user is excited, a visually appealing graphical display format can be selected. In this way, by adjusting the information display format according to the user's emotions, more appropriate information can be provided.

[0093] The reception desk can adjust the method of receiving requests and budgets by taking into account the user's current weather information. Weather information includes, but is not limited to, temperature, precipitation, and wind speed. The reception desk can use AI to take weather information into account. For example, the reception desk can provide information using an AI model that takes the user's current weather information as input and outputs the optimal method of receiving the request. This allows for more appropriate requests and budgets to be received by taking weather information into account.

[0094] The information delivery unit can estimate the user's emotions and filter information based on those estimated emotions. For example, if the user is relaxed, it can prioritize providing information on relaxing activities and services. If the user is excited, it can prioritize providing information on active activities and services. Furthermore, if the user is stressed, it can prioritize providing information that helps relieve stress. In this way, by filtering information according to the user's emotions, more appropriate information can be provided.

[0095] The automation unit can analyze past user feedback and adjust the automated booking or inquiry process based on the analysis results. Past feedback may include, but is not limited to, satisfaction levels, areas for improvement, and requests. The automation unit can utilize AI to analyze past feedback. For example, it can suggest booking methods using an AI model that takes past feedback as input and outputs the optimal automation method. This allows for more appropriate automated booking or inquiry processes by analyzing past feedback.

[0096] The information delivery system can estimate the user's emotions and prioritize information based on those emotions. For example, if the user is relaxed, detailed information can be prioritized. If the user is in a hurry, concise information can be prioritized. Furthermore, if the user is excited, visually appealing information can be prioritized. By prioritizing information according to the user's emotions, more relevant information can be provided.

[0097] The reception desk can adjust the method of receiving requests and budgets by taking into account the user's current device information. Device information includes, but is not limited to, the type of device being used, screen size, and operating method. The reception desk can use AI to take device information into account. For example, the reception desk can provide information using an AI model that takes the user's device information as input and outputs the optimal reception method. This makes it possible to receive requests and budgets more appropriately by taking device information into account.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk receives information from the user regarding their requests and budget. Requests may include, but are not limited to, the type of accommodation and services provided. Budgets may include, but are not limited to, the cost per night and the total budget. Step 2: Based on the information received by the reception department, the service department provides information on accommodations or services that meet the user's needs and budget. Accommodation information includes, but is not limited to, the name, location, rates, and facilities of the facility. Service information includes, but is not limited to, the types of services offered, rates, and available hours. The service department may provide information by referring to a database of facilities. Step 3: The automation unit makes a reservation or inquiry for accommodation or services based on the information provided by the service unit. Reservation procedures include, but are not limited to, online or telephone reservations. Confirmation messages include, but are not limited to, email or SMS messages.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0101] 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.

[0102] 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.

[0103] For example, the reception unit can receive information regarding requests and budgets from users using the reception device 38 of the smart device 14. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides information on accommodations or services that meet the user's requests and budget by referring to the facility database 24. The automation unit is implemented by the specific processing unit 290 of the data processing device 12 and makes reservations or inquiries about accommodations or services based on the information provided by the provision unit. For example, a confirmation message is provided to the user using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] For example, the reception unit can receive information regarding the user's requests and budget using the microphone 238 of the smart glasses 214. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on accommodations or services that meet the user's requests and budget by referring to the facility database 24. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes reservations or inquiries about accommodations or services based on the information provided by the provision unit. For example, a confirmation message is provided to the user using the speaker 240 of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] For example, the reception unit can receive information regarding the user's requests and budget using the microphone 238 of the headset terminal 314. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on accommodations or services that meet the user's requests and budget by referring to the facility database 24. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes reservations or inquiries about accommodations or services based on the information provided by the provision unit. For example, a confirmation message is provided to the user using the display 343 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] For example, the reception unit can receive information regarding requests and budgets from users using the microphone 238 of the robot 414. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on accommodations or services that meet the user's requests and budget by referring to the facility database 24. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes reservations or inquiries about accommodations or services based on the information provided by the provision unit. For example, a confirmation message is provided to the user using the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A reception desk that receives requests and budget information from users, Based on the information received by the aforementioned reception department, a provision department provides information on accommodations or services that meet the user's needs and budget. The system includes an automated unit that makes reservations or inquiries about accommodations or services based on the information provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide information by referring to the facility's database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We analyze user requests and budgets to provide information on appropriate activities and restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automation unit, The booking process is automated, and a confirmation message is provided. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It estimates the user's emotions and provides accommodation and service information tailored to the estimated user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We analyze the user's past inquiry history and provide accommodation or service information based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving an inquiry, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We estimate the user's emotions and adjust how requests and budgets are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past requests and budget information to select the appropriate method of acceptance. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving requests and budgets, filtering is performed based on the user's current travel plans and areas of interest. 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 requests and budgets to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving requests and budgets, the system prioritizes receiving information that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving requests and budgets, we analyze users' social media activity and collect highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the accommodation and service. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing services, different provisioning algorithms are applied depending on the category of accommodation and service. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and prioritizes information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, prioritize it based on the availability of accommodations and services. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, we adjust the order based on the relevance of the accommodation and service. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, It estimates the user's emotions and adjusts how bookings or inquiries are automated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, During automation, the system analyzes the user's past booking history to select the appropriate automation method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, During automation, the automation methods are adjusted based on the user's current travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, During automation, the appropriate automation method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automation unit, During automation, the system analyzes users' social media activity and suggests highly relevant automation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 receives requests and budget information from users, Based on the information received by the aforementioned reception department, a provision department provides information on accommodations or services that meet the user's requests and budget. The system includes an automated unit that makes reservations or inquiries about accommodations or services based on the information provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provide information by referring to the facility's database. The system according to feature 1.

3. The aforementioned supply unit is, We analyze user requests and budgets to provide information on appropriate activities and restaurants. The system according to feature 1.

4. The aforementioned automation unit, The booking process is automated, and a confirmation message is provided. The system according to feature 1.

5. The aforementioned supply unit is, It estimates the user's emotions and provides accommodation and service information tailored to the estimated user's emotions. The system according to feature 1.

6. The aforementioned supply unit is, We analyze the user's past inquiry history and provide accommodation or service information based on the analysis results. The system according to feature 1.

7. The aforementioned reception unit is When receiving an inquiry, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is We estimate the user's emotions and adjust how requests and budgets are received based on those estimated emotions. The system according to feature 1.

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