system

Abstract

The system according to this embodiment aims to allow travelers to easily find the most suitable accommodation or hotel. [Solution] The system according to this embodiment comprises a reception unit, a proposal unit, and a provision unit. The reception unit receives input of travel itinerary, purpose, number of people, budget, and departure location. The proposal unit analyzes the information received by the reception unit and proposes the most suitable accommodation or hotel. The provision unit provides the information on accommodation or hotels proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 is a problem that it takes time for travelers to search for accommodation and hotels by themselves, and it is difficult to find an optimal choice.

[0005] The system according to the embodiment aims to enable travelers to easily find an optimal accommodation or hotel.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a provision unit. The reception unit receives inputs of travel schedule, purpose, number of people, budget, and departure location. The proposal unit analyzes the information received by the reception unit and proposes an optimal accommodation or hotel. The provision unit provides information on the accommodation or hotel proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment allows travelers to easily find the most suitable accommodation or hotel. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) The travel suggestion system according to an embodiment of the present invention is a system in which AI suggests the optimal accommodation and hotel simply by the user inputting the travel itinerary, purpose, number of people, budget, and departure point. The travel suggestion system is a mechanism in which AI suggests the optimal accommodation and hotel simply by the user inputting the travel itinerary, purpose, number of people, budget, and departure point. Specifically, it consists of the following steps. First, the user inputs the travel itinerary, purpose, number of people, budget, and departure point. Next, the AI ​​analyzes this information and suggests the optimal accommodation and hotel. This mechanism saves the user the trouble of searching for accommodation and hotels themselves. In addition, the suggested accommodation and hotel are the most suitable for the user's conditions, resulting in high satisfaction. Furthermore, the AI's suggestions are quick, saving the user time. For example, the user inputs the travel itinerary, purpose, number of people, budget, and departure point. At this time, the user only needs to input specific conditions. For example, the user inputs conditions such as "a trip from December 1st to 3rd, 2023, for sightseeing purposes, 2 people, a budget of 50,000 yen, and departure point is Tokyo." This information is input into the AI. Next, the AI ​​analyzes the input information. AI searches for the best accommodations and hotels based on travel itinerary, purpose, number of people, budget, and departure point. For example, if the purpose is sightseeing, the AI ​​prioritizes suggesting accommodations and hotels close to tourist attractions. It also selects accommodations and hotels according to the budget. This allows the AI ​​to suggest accommodations and hotels that best suit the user's conditions. Because the suggested accommodations and hotels are the best fit for the user's conditions, satisfaction is high. For example, by suggesting accommodations and hotels close to tourist attractions, users can maximize their time enjoying sightseeing. Also, by suggesting the best accommodations and hotels within the budget, users can minimize unnecessary expenses. Furthermore, the AI's suggestions are quick, saving users time. Users are spared the trouble of searching for accommodations and hotels themselves, allowing them to dedicate that time to other travel preparations. For example, they can spend time creating sightseeing plans or packing their luggage. This system not only saves users the trouble of searching for accommodations and hotels themselves, but also provides quick suggestions for the best accommodations and hotels, making travel planning proceed smoothly.Furthermore, the suggested accommodations and hotels are the most suitable for the user's requirements, resulting in high satisfaction. In addition, the AI-powered suggestions are quick, saving the user time. This allows users to plan more comfortable and efficient trips. Thus, the travel suggestion system can quickly and efficiently support the user's travel planning.

[0029] The travel suggestion system according to this embodiment comprises a reception unit, a suggestion unit, and a provision unit. The reception unit receives input of travel itinerary, purpose, number of people, budget, and departure location. The reception unit provides, for example, an interface for the user to input travel itinerary, purpose, number of people, budget, and departure location. The reception unit can accept input through, for example, a web form or a mobile application. The reception unit can also support multiple input methods, such as voice input and touch input. For example, the reception unit allows the user to input travel itinerary and purpose by voice. The reception unit can also allow intuitive input using a touchscreen. The suggestion unit analyzes the information received by the reception unit and suggests the most suitable accommodations and hotels. The suggestion unit, for example, uses AI to analyze the user's input information and searches for the most suitable accommodations and hotels. The suggestion unit, for example, prioritizes suggesting accommodations and hotels close to tourist destinations based on the user's travel itinerary and purpose. The suggestion unit can also select accommodations and hotels according to the budget. For example, the suggestion unit suggests the most suitable accommodations and hotels within the user's budget. The suggestion unit can, for example, use AI to make suggestions considering the user's past travel history and other users' ratings. The provision unit provides information on accommodations and hotels suggested by the suggestion unit. The provision unit can, for example, display detailed information about the suggested accommodations and hotels to the user. The provision unit can provide information such as photos, prices, availability, and reviews of the accommodations and hotels. The provision unit can also provide links or buttons for the user to book the suggested accommodations and hotels. For example, the provision unit can allow the user to book the suggested accommodations and hotels directly. As a result, the travel suggestion system according to the embodiment can provide information and suggest the most suitable accommodations and hotels using AI simply by the user inputting travel dates, purpose, number of people, budget, and departure point. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the suggestion unit can input the user's input information into a generating AI and have the generating AI suggest the most suitable accommodations and hotels.Some or all of the processing described above in the provisioning unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the provisioning unit can input the proposed accommodation and hotel information into a generating AI and have the generating AI perform the information provision.

[0030] The reception desk accepts input of travel itinerary, purpose, number of people, budget, and departure location. For example, the reception desk provides an interface for users to input travel itinerary, purpose, number of people, budget, and departure location. Specifically, input can be accepted via web forms or mobile applications. Web forms provide text boxes and dropdown menus for users to enter information through a browser. Mobile applications offer an interface optimized for smartphone and tablet screens, allowing users to easily input information using touch controls. The reception desk can also support multiple input methods, including voice and touch input. With voice input, users simply speak their travel itinerary and purpose into a microphone, and the system automatically converts the speech to text, completing the input. With touch input, users can intuitively input information using a touchscreen. For example, they can tap a calendar display to select travel itinerary or move a slider to set a budget. Furthermore, the reception desk verifies user input in real time and provides immediate feedback if there is missing or incorrect information. This allows users to input information smoothly and provide accurate data. The reception desk prioritizes user convenience, offering diverse input methods and an intuitive interface to ensure users can enter information without stress.

[0031] The Proposal Department analyzes the information received by the Reception Department and proposes the most suitable accommodations and hotels. For example, the Proposal Department uses AI to analyze user input information and search for the most suitable accommodations and hotels. Specifically, the AI ​​selects the most suitable accommodations and hotels from a vast database based on information such as the user's travel itinerary, purpose, number of people, budget, and departure point. The AI ​​uses natural language processing technology to understand the user's input and extract relevant keywords and conditions. For example, if a user enters "a place where we can relax on a family trip," the AI ​​will extract keywords such as "family trip," "relax," and "accommodation" and perform a search based on them. The AI ​​can also make suggestions considering the user's past travel history and other users' ratings. For example, it may prioritize suggesting accommodations and hotels that the user has given high ratings to in the past. It also selects highly reliable accommodations and hotels by referring to reviews and ratings from other users. Furthermore, the Proposal Department proposes the most suitable accommodations and hotels within the user's budget. The AI ​​obtains accommodation and hotel price information in real time and provides options that fit the user's budget. For example, even with a limited budget, we can suggest cost-effective accommodations and hotels. The suggestion department uses AI to quickly and accurately propose the best accommodations and hotels for the user's needs, supporting their travel planning.

[0032] The service provider provides information on accommodations and hotels suggested by the suggestion provider. Specifically, it displays detailed information about suggested accommodations and hotels to the user. For example, the service provider provides information such as photos of accommodations and hotels, rates, availability, and reviews. Based on this information, users can compare and consider accommodations and hotels. For example, they can view photos of accommodations and hotels to check the atmosphere of the facilities, or compare rates to find an option that fits their budget. The service provider can also provide links or buttons for users to book suggested accommodations and hotels. For example, the service provider can allow users to book suggested accommodations and hotels directly. Users can proceed with the booking process simply by clicking the provided links or buttons. Furthermore, the service provider considers user convenience and supports the booking process to ensure it proceeds smoothly. For example, it can provide functions to automatically fill in the necessary information in the booking form and to send booking confirmation emails. This allows users to book accommodations and hotels without any hassle. The service provider provides users with information on suggested accommodations and hotels quickly and accurately, supporting their travel planning.

[0033] The suggestion unit can propose the most suitable accommodations and hotels by considering the user's past travel history. For example, the suggestion unit can retrieve and analyze the user's past travel history from a database. For example, the suggestion unit can propose similar accommodations and hotels based on information about accommodations and hotels visited in the past. The suggestion unit can also analyze the user's preferred travel style (resort, sightseeing, business, etc.) from their past travel history and propose the most suitable accommodations and hotels. For example, the suggestion unit can prioritize suggesting accommodations and hotels that the user has given high ratings to in the past. In this way, by considering the user's past travel history, it can propose more suitable accommodations and hotels. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's past travel history into a generative AI and have the generative AI propose the most suitable accommodations and hotels.

[0034] The suggestion unit can propose the most suitable accommodations and hotels by considering the ratings of other users. For example, the suggestion unit can obtain and analyze the rating data of other users from a database. For example, the suggestion unit can prioritize suggesting accommodations and hotels that have received high ratings from other users. The suggestion unit can also analyze the content of other users' reviews and propose accommodations and hotels that best suit the user's conditions. For example, based on the ratings of other users, the suggestion unit can propose accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.). In this way, by considering the ratings of other users, it is possible to propose more reliable accommodations and hotels. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the rating data of other users into a generative AI and have the generative AI propose the most suitable accommodations and hotels.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can retrieve the user's past input history from a database and analyze it. For example, the reception desk can automatically display as suggestions the travel itinerary, purpose, number of people, budget, and departure location that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the travel itinerary, purpose, number of people, budget, and departure location to be used in a specific time slot based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history into a generative AI and have the generative AI suggest the optimal input method.

[0036] The input system can automatically complete input fields based on the user's current travel plan progress during input. For example, the input system can automatically complete the remaining input fields (number of people, budget, departure point) based on the travel itinerary and purpose already entered by the user. The input system can also complete input fields for the current travel plan by referring to the progress of trips the user has planned in the past. For example, the input system can retrieve travel information planned by the user in other applications and automatically complete the input fields. This reduces the effort required for input by completing input fields based on the user's travel plan progress. Some or all of the above processing in the input system may be performed using, for example, a generative AI, or without a generative AI. For example, the input system can input the user's travel plan progress into a generative AI and have the generative AI complete the input fields.

[0037] The reception desk can automatically display relevant input fields when the user is entering data, taking into account their geographical location. For example, if the user is in a specific region, the reception desk can automatically display input fields for accommodations and hotels related to that region. Furthermore, if the user is near a travel destination, the reception desk can suggest tourist attractions and event information for that region as input fields. For example, if the user is in a specific city, the reception desk can display transportation information and access methods for that city as input fields. This improves input efficiency by automatically displaying relevant input fields while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI display the relevant input fields.

[0038] The reception desk can analyze the user's social media activity during input and automatically suggest relevant input fields. For example, the reception desk can automatically suggest relevant input fields based on travel plans shared by the user on social media. It can also suggest input fields based on travel destinations and tourist spots followed by the user on social media. For example, the reception desk can automatically display input fields based on event information the user plans to attend shared on social media. By analyzing the user's social media activity, it can automatically suggest relevant input fields and improve input efficiency. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's social media activity into a generative AI and have the generative AI suggest relevant input fields.

[0039] The suggestion unit can analyze the user's past travel history to make optimal suggestions. For example, the suggestion unit can retrieve and analyze the user's past travel history from a database. For example, the suggestion unit can suggest similar accommodations or hotels based on places the user has visited in the past. The suggestion unit can also analyze the user's preferred travel style (resort, sightseeing, business, etc.) from their past travel history to make optimal suggestions. For example, the suggestion unit can prioritize suggesting accommodations or hotels that the user has given high ratings to in the past. This allows the system to make more suitable suggestions by analyzing the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past travel history into a generative AI and have the generative AI execute optimal suggestions.

[0040] The suggestion unit can improve the accuracy of its suggestions by considering the ratings and reviews of other users. For example, the suggestion unit can retrieve and analyze the rating data and review content of other users from a database. For example, the suggestion unit can prioritize suggesting accommodations and hotels that have received high ratings from other users. The suggestion unit can also analyze the content of other users' reviews and suggest accommodations and hotels that best suit the user's conditions. For example, the suggestion unit can suggest accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.) based on the ratings of other users. In this way, the accuracy of suggestions can be improved by considering the ratings and reviews of other users. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the rating data and review content of other users into a generative AI and have the generative AI perform the improvement of the accuracy of suggestions.

[0041] The suggestion unit can propose the most suitable accommodations and hotels by considering the user's geographical location information. For example, the suggestion unit can obtain and analyze the user's geographical location information from a database. If the user is in a specific region, the suggestion unit can prioritize suggesting accommodations and hotels close to that region. The suggestion unit can also suggest accommodations and hotels in a region if the user is near a travel destination. For example, if the user is in a specific city, the suggestion unit can suggest accommodations and hotels in that city. In this way, the optimal accommodations and hotels can be suggested by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal accommodations and hotels.

[0042] The suggestion unit can analyze the user's social media activity and suggest relevant accommodations and hotels when making suggestions. For example, the suggestion unit can retrieve and analyze the user's social media activity from a database. For example, the suggestion unit can suggest relevant accommodations and hotels based on travel plans shared by the user on social media. The suggestion unit can also suggest accommodations and hotels based on travel destinations and tourist spots followed by the user on social media. For example, the suggestion unit can suggest accommodations and hotels based on event information the user plans to attend, as shared on social media. In this way, by analyzing the user's social media activity, it is possible to suggest relevant accommodations and hotels. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI make suggestions for relevant accommodations and hotels.

[0043] The service provider can analyze the user's past browsing history at the time of delivery and provide the most relevant information. For example, the service provider can retrieve the user's past browsing history from a database and analyze it. For example, the service provider can provide relevant information based on information about accommodations and hotels that the user has previously viewed. The service provider can also analyze the user's preferred accommodations and hotels from their past browsing history and provide the most relevant information. For example, the service provider can prioritize providing information about accommodations and hotels that the user has previously given high ratings to. In this way, the service provider can provide the most relevant information by analyzing the user's past browsing history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past browsing history into a generative AI and have the generative AI perform the task of providing the most relevant information.

[0044] The information provider can customize the information at the time of delivery based on the user's current travel plan progress. For example, the provider can provide information on relevant accommodations and hotels based on the travel itinerary and purpose already planned by the user. The provider can also provide information related to the current travel plan by referring to the progress of travel the user has planned in the past. For example, the provider can obtain travel information planned by the user in other applications and provide relevant information. This improves user convenience by customizing information based on the progress of the user's travel plan. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's travel plan progress into a generative AI and have the generative AI perform the information customization.

[0045] The service provider can provide relevant information while considering the user's geographical location. For example, the service provider can obtain and analyze the user's geographical location from a database. For example, if the user is in a specific region, the service provider can provide information on accommodations and hotels related to that region. The service provider can also provide information on tourist attractions and events in a region if the user is near a travel destination. For example, if the service provider is in a specific city, the service provider can provide transportation information and access methods for that city. In this way, relevant information can be provided by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's geographical location into a generative AI and have the generative AI perform the provision of relevant information.

[0046] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can retrieve and analyze the user's social media activity from a database. For example, the service provider can provide information on relevant accommodations and hotels based on travel plans shared by the user on social media. The service provider can also provide information on accommodations and hotels based on travel destinations and tourist spots followed by the user on social media. For example, the service provider can provide information on relevant accommodations and hotels based on event information the user plans to attend posted on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the provision of relevant information.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The suggestion function can propose the most suitable accommodations and hotels by considering the user's past travel history. For example, the suggestion function retrieves and analyzes the user's past travel history from a database. Based on information about accommodations and hotels visited in the past, it suggests similar accommodations and hotels. It can also analyze the user's preferred travel style (resort, sightseeing, business, etc.) from their past travel history and suggest the most suitable accommodations and hotels. For example, it can prioritize suggesting accommodations and hotels that the user has given high ratings to in the past. Furthermore, based on the user's past travel history, it can suggest accommodations and hotels related to specific seasons or events. In this way, by considering the user's past travel history, it can suggest more suitable accommodations and hotels.

[0049] The suggestion department can propose the most suitable accommodations and hotels by considering the ratings of other users. For example, the suggestion department can retrieve and analyze the rating data of other users from a database. It will prioritize suggesting accommodations and hotels that have received high ratings from other users. It can also analyze the content of other users' reviews and propose accommodations and hotels that best suit the user's conditions. For example, it can suggest accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.). Furthermore, based on the ratings of other users, it can also suggest accommodations and hotels related to specific seasons or events. In this way, by considering the ratings of other users, it can propose accommodations and hotels that are more reliable.

[0050] The suggestion function can propose the most suitable accommodations and hotels by considering the user's geographical location. For example, the suggestion function retrieves and analyzes the user's geographical location from a database. If the user is in a specific region, it prioritizes suggesting accommodations and hotels close to that region. Furthermore, if the user is near their travel destination, it can suggest accommodations and hotels in that region. For example, if the user is in a specific city, it will suggest accommodations and hotels in that city. In addition, based on the user's geographical location, it can suggest accommodations and hotels related to specific seasons or events. This allows the system to propose the most suitable accommodations and hotels by considering the user's geographical location.

[0051] The reception desk can analyze a user's past input history and suggest the optimal input method. For example, the reception desk retrieves and analyzes the user's past input history from a database. It automatically displays suggested travel dates, purposes, number of people, budget, and departure locations that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, it can predict and suggest travel dates, purposes, number of people, budget, and departure locations to be used during a specific time period based on the user's past input history. Furthermore, it can suggest input items related to specific seasons or events based on the user's past input history. In this way, the efficiency of input can be improved by suggesting the optimal input method based on the user's past input history.

[0052] The proposal department can improve the accuracy of its suggestions by considering the ratings and reviews of other users. For example, the proposal department can retrieve and analyze the rating data and review content of other users from a database. It can then prioritize suggesting accommodations and hotels that have received high ratings from other users. It can also analyze the content of other users' reviews and suggest accommodations and hotels that best suit the user's conditions. For example, it can suggest accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.). Furthermore, based on the ratings of other users, it can suggest accommodations and hotels related to specific seasons or events. In this way, the accuracy of suggestions can be improved by considering the ratings and reviews of other users.

[0053] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can retrieve and analyze the user's social media activity from a database. Based on travel plans shared by the user on social media, it can provide information on relevant accommodations and hotels. It can also provide information on accommodations and hotels based on travel destinations and tourist spots that the user follows on social media. For example, based on event information the user plans to attend shared on social media, it can provide information on relevant accommodations and hotels. Furthermore, based on the user's social media activity, it can provide information related to specific seasons or events. In this way, relevant information can be provided by analyzing the user's social media activity.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk accepts input of travel itinerary, purpose, number of people, budget, and departure location. The reception desk provides an interface for users to input their travel itinerary, purpose, number of people, budget, and departure location. For example, input can be accepted via a web form or mobile application, and multiple input methods such as voice input and touch input are supported. Step 2: The suggestion department analyzes the information received by the reception department and proposes the most suitable accommodations and hotels. The suggestion department uses AI to analyze the user's input information and searches for accommodations close to tourist destinations and hotels that fit the budget. Furthermore, it can also make suggestions considering the user's past travel history and reviews from other users. Step 3: The provider provides information on accommodations and hotels suggested by the suggestion team. The provider displays detailed information, photos, rates, availability, reviews, etc., for the accommodations and hotels, and provides links or buttons for users to book the suggested accommodations and hotels.

[0056] (Example of form 2) The travel suggestion system according to an embodiment of the present invention is a system in which AI suggests the optimal accommodation and hotel simply by the user inputting the travel itinerary, purpose, number of people, budget, and departure point. The travel suggestion system is a mechanism in which AI suggests the optimal accommodation and hotel simply by the user inputting the travel itinerary, purpose, number of people, budget, and departure point. Specifically, it consists of the following steps. First, the user inputs the travel itinerary, purpose, number of people, budget, and departure point. Next, the AI ​​analyzes this information and suggests the optimal accommodation and hotel. This mechanism saves the user the trouble of searching for accommodation and hotels themselves. In addition, the suggested accommodation and hotel are the most suitable for the user's conditions, resulting in high satisfaction. Furthermore, the AI's suggestions are quick, saving the user time. For example, the user inputs the travel itinerary, purpose, number of people, budget, and departure point. At this time, the user only needs to input specific conditions. For example, the user inputs conditions such as "a trip from December 1st to 3rd, 2023, for sightseeing purposes, 2 people, a budget of 50,000 yen, and departure point is Tokyo." This information is input into the AI. Next, the AI ​​analyzes the input information. AI searches for the best accommodations and hotels based on travel itinerary, purpose, number of people, budget, and departure point. For example, if the purpose is sightseeing, the AI ​​prioritizes suggesting accommodations and hotels close to tourist attractions. It also selects accommodations and hotels according to the budget. This allows the AI ​​to suggest accommodations and hotels that best suit the user's conditions. Because the suggested accommodations and hotels are the best fit for the user's conditions, satisfaction is high. For example, by suggesting accommodations and hotels close to tourist attractions, users can maximize their time enjoying sightseeing. Also, by suggesting the best accommodations and hotels within the budget, users can minimize unnecessary expenses. Furthermore, the AI's suggestions are quick, saving users time. Users are spared the trouble of searching for accommodations and hotels themselves, allowing them to dedicate that time to other travel preparations. For example, they can spend time creating sightseeing plans or packing their luggage. This system not only saves users the trouble of searching for accommodations and hotels themselves, but also provides quick suggestions for the best accommodations and hotels, making travel planning proceed smoothly.Furthermore, the suggested accommodations and hotels are the most suitable for the user's requirements, resulting in high satisfaction. In addition, the AI-powered suggestions are quick, saving the user time. This allows users to plan more comfortable and efficient trips. Thus, the travel suggestion system can quickly and efficiently support the user's travel planning.

[0057] The travel suggestion system according to this embodiment comprises a reception unit, a suggestion unit, and a provision unit. The reception unit receives input of travel itinerary, purpose, number of people, budget, and departure location. The reception unit provides, for example, an interface for the user to input travel itinerary, purpose, number of people, budget, and departure location. The reception unit can accept input through, for example, a web form or a mobile application. The reception unit can also support multiple input methods, such as voice input and touch input. For example, the reception unit allows the user to input travel itinerary and purpose by voice. The reception unit can also allow intuitive input using a touchscreen. The suggestion unit analyzes the information received by the reception unit and suggests the most suitable accommodations and hotels. The suggestion unit, for example, uses AI to analyze the user's input information and searches for the most suitable accommodations and hotels. The suggestion unit, for example, prioritizes suggesting accommodations and hotels close to tourist destinations based on the user's travel itinerary and purpose. The suggestion unit can also select accommodations and hotels according to the budget. For example, the suggestion unit suggests the most suitable accommodations and hotels within the user's budget. The suggestion unit can, for example, use AI to make suggestions considering the user's past travel history and other users' ratings. The provision unit provides information on accommodations and hotels suggested by the suggestion unit. The provision unit can, for example, display detailed information about the suggested accommodations and hotels to the user. The provision unit can provide information such as photos, prices, availability, and reviews of the accommodations and hotels. The provision unit can also provide links or buttons for the user to book the suggested accommodations and hotels. For example, the provision unit can allow the user to book the suggested accommodations and hotels directly. As a result, the travel suggestion system according to the embodiment can provide information and suggest the most suitable accommodations and hotels using AI simply by the user inputting travel dates, purpose, number of people, budget, and departure point. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the suggestion unit can input the user's input information into a generating AI and have the generating AI suggest the most suitable accommodations and hotels.Some or all of the processing described above in the provisioning unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the provisioning unit can input the proposed accommodation and hotel information into a generating AI and have the generating AI perform the information provision.

[0058] The reception desk accepts input of travel itinerary, purpose, number of people, budget, and departure location. For example, the reception desk provides an interface for users to input travel itinerary, purpose, number of people, budget, and departure location. Specifically, input can be accepted via web forms or mobile applications. Web forms provide text boxes and dropdown menus for users to enter information through a browser. Mobile applications offer an interface optimized for smartphone and tablet screens, allowing users to easily input information using touch controls. The reception desk can also support multiple input methods, including voice and touch input. With voice input, users simply speak their travel itinerary and purpose into a microphone, and the system automatically converts the speech to text, completing the input. With touch input, users can intuitively input information using a touchscreen. For example, they can tap a calendar display to select travel itinerary or move a slider to set a budget. Furthermore, the reception desk verifies user input in real time and provides immediate feedback if there is missing or incorrect information. This allows users to input information smoothly and provide accurate data. The reception desk prioritizes user convenience, offering diverse input methods and an intuitive interface to ensure users can enter information without stress.

[0059] The Proposal Department analyzes the information received by the Reception Department and proposes the most suitable accommodations and hotels. For example, the Proposal Department uses AI to analyze user input information and search for the most suitable accommodations and hotels. Specifically, the AI ​​selects the most suitable accommodations and hotels from a vast database based on information such as the user's travel itinerary, purpose, number of people, budget, and departure point. The AI ​​uses natural language processing technology to understand the user's input and extract relevant keywords and conditions. For example, if a user enters "a place where we can relax on a family trip," the AI ​​will extract keywords such as "family trip," "relax," and "accommodation" and perform a search based on them. The AI ​​can also make suggestions considering the user's past travel history and other users' ratings. For example, it may prioritize suggesting accommodations and hotels that the user has given high ratings to in the past. It also selects highly reliable accommodations and hotels by referring to reviews and ratings from other users. Furthermore, the Proposal Department proposes the most suitable accommodations and hotels within the user's budget. The AI ​​obtains accommodation and hotel price information in real time and provides options that fit the user's budget. For example, even with a limited budget, we can suggest cost-effective accommodations and hotels. The suggestion department uses AI to quickly and accurately propose the best accommodations and hotels for the user's needs, supporting their travel planning.

[0060] The service provider provides information on accommodations and hotels suggested by the suggestion provider. Specifically, it displays detailed information about suggested accommodations and hotels to the user. For example, the service provider provides information such as photos of accommodations and hotels, rates, availability, and reviews. Based on this information, users can compare and consider accommodations and hotels. For example, they can view photos of accommodations and hotels to check the atmosphere of the facilities, or compare rates to find an option that fits their budget. The service provider can also provide links or buttons for users to book suggested accommodations and hotels. For example, the service provider can allow users to book suggested accommodations and hotels directly. Users can proceed with the booking process simply by clicking the provided links or buttons. Furthermore, the service provider considers user convenience and supports the booking process to ensure it proceeds smoothly. For example, it can provide functions to automatically fill in the necessary information in the booking form and to send booking confirmation emails. This allows users to book accommodations and hotels without any hassle. The service provider provides users with information on suggested accommodations and hotels quickly and accurately, supporting their travel planning.

[0061] The suggestion unit can propose the most suitable accommodations and hotels by considering the user's past travel history. For example, the suggestion unit can retrieve and analyze the user's past travel history from a database. For example, the suggestion unit can propose similar accommodations and hotels based on information about accommodations and hotels visited in the past. The suggestion unit can also analyze the user's preferred travel style (resort, sightseeing, business, etc.) from their past travel history and propose the most suitable accommodations and hotels. For example, the suggestion unit can prioritize suggesting accommodations and hotels that the user has given high ratings to in the past. In this way, by considering the user's past travel history, it can propose more suitable accommodations and hotels. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's past travel history into a generative AI and have the generative AI propose the most suitable accommodations and hotels.

[0062] The suggestion unit can propose the most suitable accommodations and hotels by considering the ratings of other users. For example, the suggestion unit can obtain and analyze the rating data of other users from a database. For example, the suggestion unit can prioritize suggesting accommodations and hotels that have received high ratings from other users. The suggestion unit can also analyze the content of other users' reviews and propose accommodations and hotels that best suit the user's conditions. For example, based on the ratings of other users, the suggestion unit can propose accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.). In this way, by considering the ratings of other users, it is possible to propose more reliable accommodations and hotels. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the rating data of other users into a generative AI and have the generative AI propose the most suitable accommodations and hotels.

[0063] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This improves user convenience by optimizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0064] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can retrieve the user's past input history from a database and analyze it. For example, the reception desk can automatically display as suggestions the travel itinerary, purpose, number of people, budget, and departure location that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the travel itinerary, purpose, number of people, budget, and departure location to be used in a specific time slot based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history into a generative AI and have the generative AI suggest the optimal input method.

[0065] The input system can automatically complete input fields based on the user's current travel plan progress during input. For example, the input system can automatically complete the remaining input fields (number of people, budget, departure point) based on the travel itinerary and purpose already entered by the user. The input system can also complete input fields for the current travel plan by referring to the progress of trips the user has planned in the past. For example, the input system can retrieve travel information planned by the user in other applications and automatically complete the input fields. This reduces the effort required for input by completing input fields based on the user's travel plan progress. Some or all of the above processing in the input system may be performed using, for example, a generative AI, or without a generative AI. For example, the input system can input the user's travel plan progress into a generative AI and have the generative AI complete the input fields.

[0066] The reception unit can estimate the user's emotions and dynamically change the priority of input items based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This improves the efficiency of input by changing the priority of input items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0067] The reception desk can automatically display relevant input fields when the user is entering data, taking into account their geographical location. For example, if the user is in a specific region, the reception desk can automatically display input fields for accommodations and hotels related to that region. Furthermore, if the user is near a travel destination, the reception desk can suggest tourist attractions and event information for that region as input fields. For example, if the user is in a specific city, the reception desk can display transportation information and access methods for that city as input fields. This improves input efficiency by automatically displaying relevant input fields while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI display the relevant input fields.

[0068] The reception desk can analyze the user's social media activity during input and automatically suggest relevant input fields. For example, the reception desk can automatically suggest relevant input fields based on travel plans shared by the user on social media. It can also suggest input fields based on travel destinations and tourist spots followed by the user on social media. For example, the reception desk can automatically display input fields based on event information the user plans to attend shared on social media. By analyzing the user's social media activity, it can automatically suggest relevant input fields and improve input efficiency. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's social media activity into a generative AI and have the generative AI suggest relevant input fields.

[0069] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in facial expressions. The proposal unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the voice and calculate an emotion score. The proposal unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on fluctuations in heart rate. By adjusting the way the proposal is presented according to the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposed unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The suggestion unit can analyze the user's past travel history to make optimal suggestions. For example, the suggestion unit can retrieve and analyze the user's past travel history from a database. For example, the suggestion unit can suggest similar accommodations or hotels based on places the user has visited in the past. The suggestion unit can also analyze the user's preferred travel style (resort, sightseeing, business, etc.) from their past travel history to make optimal suggestions. For example, the suggestion unit can prioritize suggesting accommodations or hotels that the user has given high ratings to in the past. This allows the system to make more suitable suggestions by analyzing the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past travel history into a generative AI and have the generative AI execute optimal suggestions.

[0071] The suggestion unit can improve the accuracy of its suggestions by considering the ratings and reviews of other users. For example, the suggestion unit can retrieve and analyze the rating data and review content of other users from a database. For example, the suggestion unit can prioritize suggesting accommodations and hotels that have received high ratings from other users. The suggestion unit can also analyze the content of other users' reviews and suggest accommodations and hotels that best suit the user's conditions. For example, the suggestion unit can suggest accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.) based on the ratings of other users. In this way, the accuracy of suggestions can be improved by considering the ratings and reviews of other users. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the rating data and review content of other users into a generative AI and have the generative AI perform the improvement of the accuracy of suggestions.

[0072] The suggestion unit can estimate the user's emotions and dynamically change the priority of suggestions based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate. This improves user satisfaction by changing the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposed unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The suggestion unit can propose the most suitable accommodations and hotels by considering the user's geographical location information. For example, the suggestion unit can obtain and analyze the user's geographical location information from a database. If the user is in a specific region, the suggestion unit can prioritize suggesting accommodations and hotels close to that region. The suggestion unit can also suggest accommodations and hotels in a region if the user is near a travel destination. For example, if the user is in a specific city, the suggestion unit can suggest accommodations and hotels in that city. In this way, the optimal accommodations and hotels can be suggested by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal accommodations and hotels.

[0074] The suggestion unit can analyze the user's social media activity and suggest relevant accommodations and hotels when making suggestions. For example, the suggestion unit can retrieve and analyze the user's social media activity from a database. For example, the suggestion unit can suggest relevant accommodations and hotels based on travel plans shared by the user on social media. The suggestion unit can also suggest accommodations and hotels based on travel destinations and tourist spots followed by the user on social media. For example, the suggestion unit can suggest accommodations and hotels based on event information the user plans to attend, as shared on social media. In this way, by analyzing the user's social media activity, it is possible to suggest relevant accommodations and hotels. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI make suggestions for relevant accommodations and hotels.

[0075] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate the emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate 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 emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This improves user convenience by adjusting the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using a generative AI, for example, or without a generative 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.

[0076] The service provider can analyze the user's past browsing history at the time of delivery and provide the most relevant information. For example, the service provider can retrieve the user's past browsing history from a database and analyze it. For example, the service provider can provide relevant information based on information about accommodations and hotels that the user has previously viewed. The service provider can also analyze the user's preferred accommodations and hotels from their past browsing history and provide the most relevant information. For example, the service provider can prioritize providing information about accommodations and hotels that the user has previously given high ratings to. In this way, the service provider can provide the most relevant information by analyzing the user's past browsing history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past browsing history into a generative AI and have the generative AI perform the task of providing the most relevant information.

[0077] The information provider can customize the information at the time of delivery based on the user's current travel plan progress. For example, the provider can provide information on relevant accommodations and hotels based on the travel itinerary and purpose already planned by the user. The provider can also provide information related to the current travel plan by referring to the progress of travel the user has planned in the past. For example, the provider can obtain travel information planned by the user in other applications and provide relevant information. This improves user convenience by customizing information based on the progress of the user's travel plan. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's travel plan progress into a generative AI and have the generative AI perform the information customization.

[0078] The service provider can estimate the user's emotions and dynamically change the priority of information based on the estimated user emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This improves user convenience by changing the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative 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.

[0079] The service provider can provide relevant information while considering the user's geographical location. For example, the service provider can obtain and analyze the user's geographical location from a database. For example, if the user is in a specific region, the service provider can provide information on accommodations and hotels related to that region. The service provider can also provide information on tourist attractions and events in a region if the user is near a travel destination. For example, if the service provider is in a specific city, the service provider can provide transportation information and access methods for that city. In this way, relevant information can be provided by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's geographical location into a generative AI and have the generative AI perform the provision of relevant information.

[0080] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can retrieve and analyze the user's social media activity from a database. For example, the service provider can provide information on relevant accommodations and hotels based on travel plans shared by the user on social media. The service provider can also provide information on accommodations and hotels based on travel destinations and tourist spots followed by the user on social media. For example, the service provider can provide information on relevant accommodations and hotels based on event information the user plans to attend posted on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the provision of relevant information.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The suggestion function can estimate the user's emotions and adjust the presentation of the suggestions based on those emotions. For example, the suggestion function can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Based on changes in facial expressions, it calculates an emotion score and displays the suggestions in more detail if the user is excited, and more concisely if they are calm. It can also record the user's voice and estimate their emotions using voice analysis technology. It analyzes the tone and speed of their voice to calculate an emotion score. For example, if the user is excited, it displays the suggestions in a more visually appealing way, and provides text-centric information if they are calm. Furthermore, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on fluctuations in heart rate, it calculates an emotion score and displays the suggestions in a relaxed tone if the user is relaxed, and uses a reassuring presentation if they are tense. By adjusting the presentation of suggestions according to the user's emotions, it is possible to improve user satisfaction.

[0083] The suggestion function can propose the most suitable accommodations and hotels by considering the user's past travel history. For example, the suggestion function retrieves and analyzes the user's past travel history from a database. Based on information about accommodations and hotels visited in the past, it suggests similar accommodations and hotels. It can also analyze the user's preferred travel style (resort, sightseeing, business, etc.) from their past travel history and suggest the most suitable accommodations and hotels. For example, it can prioritize suggesting accommodations and hotels that the user has given high ratings to in the past. Furthermore, based on the user's past travel history, it can suggest accommodations and hotels related to specific seasons or events. In this way, by considering the user's past travel history, it can suggest more suitable accommodations and hotels.

[0084] The service provider can estimate the user's emotions and adjust the way information is displayed based on those emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Based on changes in facial expressions, it calculates an emotion score and, if the user is excited, displays information in a more visually appealing way; if the user is calm, it provides text-based information. It can also record the user's voice and estimate their emotions using voice analysis technology. It analyzes the tone and speed of the voice and calculates an emotion score. For example, if the user is excited, it displays information in a more visually appealing way; if the user is calm, it provides text-based information. Furthermore, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on fluctuations in heart rate, it calculates an emotion score and, if the user is relaxed, displays information in a relaxed tone; if the user is tense, it adopts a reassuring presentation. This allows the service provider to improve user convenience by adjusting the way information is displayed according to the user's emotions.

[0085] The suggestion department can propose the most suitable accommodations and hotels by considering the ratings of other users. For example, the suggestion department can retrieve and analyze the rating data of other users from a database. It will prioritize suggesting accommodations and hotels that have received high ratings from other users. It can also analyze the content of other users' reviews and propose accommodations and hotels that best suit the user's conditions. For example, it can suggest accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.). Furthermore, based on the ratings of other users, it can also suggest accommodations and hotels related to specific seasons or events. In this way, by considering the ratings of other users, it can propose accommodations and hotels that are more reliable.

[0086] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on the changes in facial expressions, and if the user is excited, the input interface is made more visually appealing; if calm, the design is changed to a simpler one. It can also record the user's voice and estimate their emotions using voice analysis technology. The tone and speed of the voice are analyzed to calculate an emotion score. For example, if the user is excited, the input interface is made more visually appealing; if calm, the design is changed to a simpler one. Furthermore, the system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on fluctuations in heart rate, and if the user is relaxed, the input interface is changed to a relaxed tone; if tense, the design is changed to one that provides a sense of security. In this way, the user convenience can be improved by optimizing the input interface according to the user's emotions.

[0087] The suggestion function can propose the most suitable accommodations and hotels by considering the user's geographical location. For example, the suggestion function retrieves and analyzes the user's geographical location from a database. If the user is in a specific region, it prioritizes suggesting accommodations and hotels close to that region. Furthermore, if the user is near their travel destination, it can suggest accommodations and hotels in that region. For example, if the user is in a specific city, it will suggest accommodations and hotels in that city. In addition, based on the user's geographical location, it can suggest accommodations and hotels related to specific seasons or events. This allows the system to propose the most suitable accommodations and hotels by considering the user's geographical location.

[0088] The service provider can estimate the user's emotions and dynamically change the priority of information based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on changes in facial expressions, and if the user is excited, important information is displayed first; if the user is calm, detailed information is displayed first. It can also record the user's voice and estimate their emotions using voice analysis technology. The tone and speed of the voice are analyzed to calculate an emotion score. For example, if the user is excited, important information is displayed first; if the user is calm, detailed information is displayed first. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. An emotion score is calculated based on fluctuations in heart rate, and if the user is relaxed, information is displayed in a relaxed tone; if the user is tense, a reassuring expression is adopted. This improves user convenience by changing the priority of information according to the user's emotions.

[0089] The reception desk can analyze a user's past input history and suggest the optimal input method. For example, the reception desk retrieves and analyzes the user's past input history from a database. It automatically displays suggested travel dates, purposes, number of people, budget, and departure locations that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, it can predict and suggest travel dates, purposes, number of people, budget, and departure locations to be used during a specific time period based on the user's past input history. Furthermore, it can suggest input items related to specific seasons or events based on the user's past input history. In this way, the efficiency of input can be improved by suggesting the optimal input method based on the user's past input history.

[0090] The proposal department can improve the accuracy of its suggestions by considering the ratings and reviews of other users. For example, the proposal department can retrieve and analyze the rating data and review content of other users from a database. It can then prioritize suggesting accommodations and hotels that have received high ratings from other users. It can also analyze the content of other users' reviews and suggest accommodations and hotels that best suit the user's conditions. For example, it can suggest accommodations and hotels that excel in specific conditions (cleanliness, service, location, etc.). Furthermore, based on the ratings of other users, it can suggest accommodations and hotels related to specific seasons or events. In this way, the accuracy of suggestions can be improved by considering the ratings and reviews of other users.

[0091] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can retrieve and analyze the user's social media activity from a database. Based on travel plans shared by the user on social media, it can provide information on relevant accommodations and hotels. It can also provide information on accommodations and hotels based on travel destinations and tourist spots that the user follows on social media. For example, based on event information the user plans to attend shared on social media, it can provide information on relevant accommodations and hotels. Furthermore, based on the user's social media activity, it can provide information related to specific seasons or events. In this way, relevant information can be provided by analyzing the user's social media activity.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The reception desk accepts input of travel itinerary, purpose, number of people, budget, and departure location. The reception desk provides an interface for users to input their travel itinerary, purpose, number of people, budget, and departure location. For example, input can be accepted via a web form or mobile application, and multiple input methods such as voice input and touch input are supported. Step 2: The suggestion department analyzes the information received by the reception department and proposes the most suitable accommodations and hotels. The suggestion department uses AI to analyze the user's input information and searches for accommodations close to tourist destinations and hotels that fit the budget. Furthermore, it can also make suggestions considering the user's past travel history and reviews from other users. Step 3: The provider provides information on accommodations and hotels suggested by the suggestion team. The provider displays detailed information, photos, rates, availability, reviews, etc., for the accommodations and hotels, and provides links or buttons for users to book the suggested accommodations and hotels.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input travel itinerary, purpose, number of people, budget, and departure point. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses AI to analyze the user's input information and propose the most suitable accommodation or hotel. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the user with detailed information on the proposed accommodation or hotel. 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.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, enabling the user to input travel itinerary, purpose, number of people, budget, and departure point by voice. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the user's input information and proposes the most suitable accommodation or hotel. The provision unit is implemented by, for example, the speaker 240 of the smart glasses 214, which provides the user with detailed information about the proposed accommodation or hotel by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] 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.

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

[0117] The 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.

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

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

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

[0121] Figure 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.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0124] In the 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.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0128] The data processing system 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.

[0129] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input travel itinerary, purpose, number of people, budget, and departure point by voice. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the user's input information and proposes the most suitable accommodation or hotel. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the user with detailed information about the proposed accommodation or hotel. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] 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.

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

[0133] The 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.

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

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input travel itinerary, purpose, number of people, budget, and departure point by voice. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the user's input information and proposes the most suitable accommodation or hotel. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides the user with detailed information about the proposed accommodation or hotel by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] (Note 1) The reception desk accepts inputs regarding travel itinerary, purpose, number of people, budget, and departure location. The reception department analyzes the information received and proposes the most suitable accommodations and hotels, The system comprises a provisioning unit that provides information on accommodations and hotels proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We suggest the most suitable accommodations and hotels based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We suggest the best accommodations and hotels, taking into account reviews from other users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is As you input data, the system automatically completes the input fields based on the user's current travel plan progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the priority of input fields based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter data, the system automatically displays relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During input, the system analyzes the user's social media activity and automatically suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, we analyze the user's past travel history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, we take into account the ratings and reviews of other users to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and dynamically changes the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, we take the user's geographical location into consideration to propose the most suitable accommodations and hotels. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and suggest relevant accommodations and hotels. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, we analyze the user's past browsing history to deliver the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the information will be customized based on the user's current travel plan progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and dynamically changes the priority of information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, relevant information will be provided, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0166] 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. The reception desk accepts inputs regarding travel itinerary, purpose, number of people, budget, and departure location. The reception department analyzes the information received and proposes the most suitable accommodations and hotels, The system comprises a provisioning unit that provides information on accommodations and hotels proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We suggest the most suitable accommodations and hotels based on the user's past travel history. The system according to feature 1.

3. The aforementioned proposal section is, We suggest the best accommodations and hotels, taking into account reviews from other users. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

5. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

6. The aforementioned reception unit is As you input data, the system automatically completes the input fields based on the user's current travel plan progress. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the priority of input fields based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When users enter data, the system automatically displays relevant input fields based on their geographical location. The system according to feature 1.

9. The aforementioned reception unit is During input, the system analyzes the user's social media activity and automatically suggests relevant input fields. The system according to feature 1.

10. The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system according to feature 1.

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