System

Abstract

To provide a system according to an embodiment that dynamically adjusts a sightseeing plan of a user and proposes an optimal sightseeing spot and service.SOLUTION: A system comprises a reception unit, a proposal unit, a reservation unit, and an adjustment unit. The reception unit receives input of a user. The proposal unit analyzes the information received by the reception unit and proposes sightseeing spots. The reservation unit reserves services on the basis of the information proposed by the proposal unit. The adjustment unit adjusts a route according to the progress state of sightseeing of the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately dynamically adjust a user's sightseeing plans and suggest optimal tourist spots and services, and there is room for improvement.

[0005] The system according to the embodiment aims to dynamically adjust a user's sightseeing plan and suggest optimal tourist spots and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a reservation unit, and an adjustment unit. The reception unit accepts input from a user. The proposal unit analyzes the information accepted by the reception unit and proposes tourist spots. The reservation unit reserves a service based on the information proposed by the proposal unit. The adjustment unit adjusts the route according to the progress of the user's sightseeing. Effect of the Invention

[0007] The system according to the embodiment can dynamically adjust a user's sightseeing plan and suggest optimal tourist spots and services. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The tourism support system according to the embodiment of the present invention is a system that suggests tourist spots based on user input, books services, and adjusts the route according to the progress of tourism. When a user inputs "I want to go skiing," this tourism support system has AI suggest places with good snow quality, and then books services such as accommodation and rentals according to the user's request. Furthermore, when a user inputs that they want to go to a specific city, the system suggests tourist spots such as museums and art galleries in that city, automatically plans a route based on the user's input date and time, and presents transportation costs and means of transportation. During the progress of tourism, if an unexpected situation occurs, such as a delay of 30 minutes from the schedule, a suspension of service, or traffic congestion, the AI ​​automatically adjusts the route. For example, when a user inputs "I want to go skiing," this information is sent to the reception unit. Next, there is an analysis unit that analyzes the information received by the reception unit, and the analysis unit analyzes the user's input information and suggests tourist spots. For example, the AI ​​suggests a ski resort with good snow quality. Next, there is a reservation unit that books services based on the proposed information, and when the user decides to go to the proposed ski resort, the reservation unit automatically books services such as accommodation and rentals. Furthermore, there is an adjustment unit that adjusts the route according to the progress of the user's sightseeing. If the user is 30 minutes behind schedule or an unexpected situation such as a suspension or traffic congestion occurs during sightseeing, the adjustment unit automatically adjusts the route. The reception unit also estimates the user's emotions, adjusts the timing of input reception based on the estimated emotions, analyzes past input history to suggest the optimal input method, and automatically completes the input content based on current location information. Furthermore, it analyzes the user's social media activities to suggest related input content, and provides an appropriate input interface based on device information. The analysis unit estimates the user's emotions and adjusts the expression method of the proposed content based on the estimated emotions. This allows the tourism support system to suggest tourist spots based on the user's input, book services, and adjust the route according to the progress of sightseeing.

[0029] The tourism support system according to the embodiment includes a reception unit, a proposal unit, a reservation unit, and an adjustment unit. The reception unit accepts user input. The user input includes, but is not limited to, text input, voice input, and image input. For example, when the user inputs "I want to go skiing," the reception unit accepts this information. The proposal unit analyzes the information accepted by the reception unit and proposes tourist spots. The proposal unit analyzes the user's input information, for example, using AI, and proposes tourist spots. For example, when the user inputs "I want to go skiing," the AI ​​proposes a ski resort with good snow quality. The reservation unit reserves services based on the information proposed by the proposal unit. For example, when the user decides to go to the proposed ski resort, the reservation unit automatically reserves services such as accommodation and rental. The adjustment unit adjusts the route according to the progress of the user's sightseeing. For example, when the user is 30 minutes behind schedule during sightseeing, or when an unexpected situation such as a suspension or traffic congestion occurs, the adjustment unit automatically adjusts the route. As a result, the tourism support system according to the embodiment can suggest tourist spots based on the user's input, book services, and adjust the route according to the progress of the tour.

[0030] The reception unit accepts user input. Examples of user input include, but are not limited to, text input, voice input, and image input. Specifically, in the text input, the user can input the desired sightseeing contents using a keyboard or a touch screen. In the voice input, the user dictates the desire through a microphone, and the contents are converted into text using voice recognition technology. In the image input, the user uploads photos of places he or she wants to go to or activities he or she is interested in, and the contents are analyzed using image analysis technology. For example, when the user inputs "I want to go skiing," this information is accepted. In order to process these inputs quickly and accurately, the reception unit utilizes advanced natural language processing technology, voice recognition technology, and image analysis technology. Furthermore, the reception unit can learn the user's past input history and preferences and provide more personalized services. For example, for a user who has requested a ski trip in the past, ski-related suggestions are preferentially displayed. In addition, the reception unit can analyze the user's input contents in real time and request additional information as necessary. For example, for a user who has input "I want to go skiing," the reception unit prepares to make more appropriate suggestions by asking for detailed information such as the desired area, budget, and whether or not there is a companion. This allows the reception unit to respond to a variety of input formats from users and receive information quickly and accurately.

[0031] The suggestion unit analyzes the information received by the reception unit and suggests tourist spots. The suggestion unit, for example, uses AI to analyze the information input by the user and suggests tourist spots. Specifically, the AI ​​uses natural language processing technology to understand the user's input content and searches the database for related tourist spots. For example, if the user inputs "I want to go skiing," the AI ​​analyzes information about ski resorts and suggests ski resorts with good snow quality. Furthermore, the suggestion unit can make more personalized suggestions by taking into account the user's past travel history and preferences. For example, based on data on places visited in the past and tourist spots evaluated, a new ski resort that is likely to be liked by the user is suggested. In addition, the suggestion unit selects the most suitable tourist spot by taking into account real-time data such as season, weather, and event information. For example, if the current weather is good or if an event is being held at a specific ski resort, the suggestion unit makes a suggestion based on that information. Furthermore, the suggestion unit presents multiple options based on the user's input content, allowing the user to select the tourist spot that interests them most. This allows the suggestion unit to quickly and accurately suggest the most suitable tourist spot according to the user's needs.

[0032] The reservation unit reserves services based on the information proposed by the suggestion unit. Specifically, when a user decides to go to a proposed ski resort, the reservation unit automatically reserves services such as accommodation and rentals. The reservation unit acquires reservation information for accommodations, transportation, and activities related to the proposed tourist spot from a database, and selects an optimal plan according to the user's wishes. For example, when a user decides to go to a ski resort, reservations for nearby hotels, ski equipment rental services, and lift tickets can be made all at once. Furthermore, the reservation unit can propose a more personalized reservation plan taking into consideration the user's input contents and past reservation history. For example, based on the accommodations used in the past and the preferred activities, the reservation unit preferentially proposes a plan that is likely to satisfy the user. In addition, the reservation unit checks availability in real time and determines the optimal reservation timing. For example, if a specific accommodation is fully booked, a flexible response according to the user's wishes can be made by suggesting another facility in the vicinity. Furthermore, the reservation unit also responds to changes and cancellations of reservation contents, providing flexible services according to the user's needs. In this way, the reservation unit can smoothly access the proposed tourist spots and support a comfortable travel experience.

[0033] The adjustment unit adjusts the route according to the progress of the user's sightseeing. Specifically, if the user is 30 minutes behind schedule during sightseeing or if an unexpected situation such as a suspension of service or traffic congestion occurs, the route is automatically adjusted. The adjustment unit collects traffic information and weather information in real time to grasp the user's current location and progress. For example, if the user is caught in traffic congestion on the way to a tourist spot, the adjustment unit proposes an alternative route to minimize the delay. In addition, the sightseeing plan can be flexibly changed based on information such as a sudden change in weather or the cancellation of an event. Furthermore, the adjustment unit collects user feedback and optimizes the plan in real time. For example, if a user wants to extend the stay time at a specific tourist spot, the travel time to the next destination is adjusted based on that information. In addition, the adjustment unit can also handle cases where multiple users use the service at the same time and adjust the schedule of the entire group. As a result, the adjustment unit can maximally support the user's sightseeing experience and flexibly respond to unexpected situations. Furthermore, the adjustment unit learns the user's past sightseeing history and preferences, and contributes to optimizing future sightseeing plans. As a result, the adjustment unit can make the user's sightseeing experience more comfortable and smooth, and improve satisfaction.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays tourist spots that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest tourist spots to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input data to the generation AI and cause the generation AI to suggest the optimal input method.

[0035] The reception unit can automatically complete the input contents based on the user's current location information. For example, when the user opens the app, the reception unit automatically acquires the current location and sets it as the starting point. In addition, when the user inputs a destination, the reception unit can also suggest the optimal candidate location in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the starting point. This can reduce the effort of inputting by automatically completing the input contents based on the user's current location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's current location data to the generation AI and cause the generation AI to automatically complete the input contents.

[0036] The reception unit can analyze the user's social media activity and suggest related input contents. The reception unit can suggest related tourist spots based on, for example, places shared by the user on social media. The reception unit can also analyze the user's social media postings and suggest input contents based on interests. Furthermore, the reception unit can also suggest related tourist spots based on places visited by the user's social media friends. In this way, related input contents can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related input contents.

[0037] The reception unit can provide an appropriate input interface based on the device information of the user. For example, when the user is using a smartphone, the reception unit can provide an input interface optimized for touch operations. In addition, when the user is using a tablet, the reception unit can also provide an input interface optimized for a large screen. Furthermore, when the user is using a smartwatch, the reception unit can also provide an input interface that is simple and highly visible. This can improve the user's convenience by providing an appropriate input interface based on the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the device information of the user to the generation AI and cause the generation AI to provide an appropriate input interface.

[0038] At the time of suggestion, the suggestion unit can suggest optimal tourist spots by referring to the user's past travel history. The suggestion unit can, for example, suggest related tourist spots based on tourist spots that the user has visited in the past. The suggestion unit can also predict and suggest places to visit in a particular season from the user's past travel history. Furthermore, the suggestion unit can also analyze the user's past travel patterns and suggest optimal tourist spots. In this way, optimal tourist spots can be suggested by referring to the user's past travel history. A part or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or can be performed without using AI. For example, the suggestion unit can input the user's past travel data to the generation AI and cause the generation AI to suggest optimal tourist spots.

[0039] At the time of suggestion, the suggestion unit can suggest the optimal tourist spot taking into consideration the user's current location information. For example, the suggestion unit preferentially suggests tourist spots close to the user's current location. The suggestion unit can also suggest tourist spots that are easily accessible from the user's current location. Furthermore, the suggestion unit can also suggest tourist spots that are suitable for the weather and season of the user's current location. In this way, the optimal tourist spot can be suggested by taking into consideration the user's current location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's current location data to the generation AI and cause the generation AI to suggest the optimal tourist spot.

[0040] The suggestion unit can customize the proposed content based on the user's interests and concerns at the time of proposal. For example, the suggestion unit can suggest related tourist spots based on tourist spots in which the user has shown interest in the past. The suggestion unit can also suggest tourist spots along a specific theme based on the user's interests and concerns. Furthermore, the suggestion unit can analyze the user's past search history and social media activities to make suggestions based on the interests. This allows the proposal content to be customized based on the user's interests and concerns, making it possible to make more appropriate suggestions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's interest data into the generation AI and cause the generation AI to customize the proposed content.

[0041] The suggestion unit can optimize the proposed content by referring to the user's past evaluations and feedback at the time of proposal. For example, the suggestion unit suggests related tourist spots based on tourist spots that the user has given high ratings to in the past. The suggestion unit can also analyze the user's past feedback and make a proposal that reflects improvements. Furthermore, the suggestion unit can also suggest optimal tourist spots based on the user's past evaluation history. This makes it possible to optimize the proposed content by referring to the user's past evaluations and feedback. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's evaluation data to the generation AI and cause the generation AI to optimize the proposed content.

[0042] At the time of suggestion, the suggestion unit can analyze the user's social media activity to suggest related tourist spots. For example, the suggestion unit can suggest related tourist spots based on places shared by the user on social media. The suggestion unit can also analyze the content of the user's social media posts to suggest tourist spots based on interests. Furthermore, the suggestion unit can also suggest related tourist spots based on places visited by the user's social media friends. In this way, related tourist spots can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related tourist spots.

[0043] The suggestion unit can provide an optimal suggestion method by considering the device information of the user at the time of suggestion. For example, when the user is using a smartphone, the suggestion unit provides a suggestion method that matches the screen size. In addition, when the user is using a tablet, the suggestion unit can also provide a suggestion method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the suggestion unit can also provide a simple and highly visible suggestion method. This makes it possible to provide an optimal suggestion method by considering the device information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the device information of the user to the generation AI and cause the generation AI to provide the optimal suggestion method.

[0044] When making a proposal, the suggestion unit can complement the proposed content by referring to the user's past travel plans. The suggestion unit, for example, suggests related tourist spots based on the contents of trips the user planned in the past. The suggestion unit can also predict and suggest places to visit in a particular season from the user's past travel plans. Furthermore, the suggestion unit can analyze the user's past travel plans and suggest optimal tourist spots. This makes it possible to complement the proposed content by referring to the user's past travel plans. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's past travel data into the generation AI and cause the generation AI to execute the complement of the proposed content.

[0045] The suggestion unit can suggest optimal tourist spots by taking into account the user's current weather information when making a suggestion. For example, the suggestion unit preferentially suggests indoor tourist spots when it is raining. The suggestion unit can also preferentially suggest outdoor tourist spots when it is sunny. Furthermore, the suggestion unit can also suggest ski resorts and snowy tourist spots on snowy days. This makes it possible to suggest optimal tourist spots by taking into account the user's current weather information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's weather data into the generation AI and cause the generation AI to execute the suggestion of optimal tourist spots.

[0046] At the time of reservation, the reservation unit can suggest an optimal reservation method by referring to the user's past reservation history. The reservation unit can, for example, suggest related accommodations based on accommodations used by the user in the past. The reservation unit can also predict and suggest accommodations to be used in a particular season from the user's past reservation history. Furthermore, the reservation unit can also analyze the user's past reservation patterns and suggest an optimal reservation method. In this way, the optimal reservation method can be suggested by referring to the user's past reservation history. A part or all of the above-mentioned processing in the reservation unit can be performed, for example, using AI or can be performed without using AI. For example, the reservation unit can input the user's past reservation data into the generation AI and cause the generation AI to execute a suggestion of an optimal reservation method.

[0047] The reservation unit can provide the optimal reservation method by taking into consideration the user's current location information at the time of reservation. For example, the reservation unit preferentially suggests accommodation facilities close to the user's current location. The reservation unit can also suggest accommodation facilities with good access from the user's current location. Furthermore, the reservation unit can also suggest accommodation facilities suitable for the weather and season of the user's current location. In this way, the optimal reservation method can be provided by taking into consideration the user's current location information. A part or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's current location data to the generation AI and cause the generation AI to provide the optimal reservation method.

[0048] The reservation unit can customize the reservation contents based on the user's interests and concerns at the time of reservation. For example, the reservation unit can suggest related accommodations based on accommodations in which the user has shown interest in the past. The reservation unit can also suggest accommodations according to a specific theme based on the user's interests and concerns. Furthermore, the reservation unit can analyze the user's past search history and social media activities and suggest reservation contents based on the interests. This allows the reservation contents to be customized based on the user's interests and concerns, making it possible to make a more appropriate reservation. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI or may be performed without using AI. For example, the reservation unit can input the user's interest data into the generation AI and cause the generation AI to customize the reservation contents.

[0049] The reservation unit can optimize the reservation contents by referring to the user's past evaluations and feedback at the time of reservation. For example, the reservation unit can suggest related accommodations based on accommodations that the user has given high ratings to in the past. The reservation unit can also analyze the user's past feedback and suggest reservation contents that reflect improvements. Furthermore, the reservation unit can also suggest optimal accommodations based on the user's past evaluation history. This makes it possible to optimize the reservation contents by referring to the user's past evaluations and feedback. A part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's evaluation data to the generation AI and cause the generation AI to optimize the reservation contents.

[0050] At the time of reservation, the reservation unit can analyze the user's social media activity and reserve related services. The reservation unit can suggest related accommodations based on, for example, places shared by the user on social media. The reservation unit can also analyze the content of the user's social media posts and suggest accommodations based on interests. Furthermore, the reservation unit can also suggest related accommodations based on places visited by the user's social media friends. In this way, related services can be reserved by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI or may be performed without using AI. For example, the reservation unit can input the user's social media data into the generation AI and cause the generation AI to execute reservations for related services.

[0051] The reservation unit can provide an optimal reservation method by taking into account the device information of the user at the time of reservation. For example, when the user is using a smartphone, the reservation unit provides a reservation method that matches the screen size. In addition, when the user is using a tablet, the reservation unit can also provide a reservation method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the reservation unit can also provide a reservation method that is simple and highly visible. In this way, the optimal reservation method can be provided by taking into account the device information of the user. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the device information of the user to the generation AI and cause the generation AI to provide the optimal reservation method.

[0052] The reservation unit can complement the reservation contents by referring to the user's past travel plans at the time of reservation. The reservation unit, for example, suggests related accommodations based on the contents of trips planned by the user in the past. The reservation unit can also predict and suggest accommodations to be used in a specific season from the user's past travel plans. Furthermore, the reservation unit can analyze the user's past travel plans and suggest optimal accommodations. In this way, the reservation contents can be complemented by referring to the user's past travel plans. A part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's past travel data into the generation AI and cause the generation AI to execute complementing of the reservation contents.

[0053] The reservation unit can reserve the optimal service by taking into consideration the user's current weather information at the time of reservation. For example, the reservation unit preferentially suggests indoor accommodations when it is raining. The reservation unit can also preferentially suggest outdoor accommodations when it is sunny. Furthermore, the reservation unit can also suggest accommodations close to ski resorts on snowy days. This allows the optimal service to be reserved by taking into consideration the user's current weather information. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's weather data into the generation AI and cause the generation AI to execute the reservation of the optimal service.

[0054] During adjustment, the adjustment unit can suggest an optimal route by referring to the user's past travel history. The adjustment unit can suggest an optimal route based on, for example, routes used by the user in the past. The adjustment unit can also suggest a route that avoids congestion from the user's past travel history. Furthermore, the adjustment unit can also analyze the user's past travel history and suggest the most efficient route. In this way, the optimal route can be suggested by referring to the user's past travel history. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past travel data to the generation AI and cause the generation AI to suggest an optimal route.

[0055] During adjustment, the adjustment unit can provide an optimal route by taking into account the user's current location information. For example, the adjustment unit preferentially proposes a route close to the user's current location. The adjustment unit can also propose a route with good access from the user's current location. Furthermore, the adjustment unit can also propose a route suitable for the weather and season of the user's current location. This makes it possible to provide an optimal route by taking into account the user's current location information. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's current location data to the generation AI and cause the generation AI to provide an optimal route.

[0056] During the adjustment, the adjustment unit can customize the route based on the user's interests. For example, the adjustment unit can suggest a related route based on tourist spots in which the user has shown interest in the past. The adjustment unit can also suggest a route along a specific theme based on the user's interests. Furthermore, the adjustment unit can analyze the user's past search history and social media activities to suggest a route based on the interests. This makes it possible to provide a more appropriate route by customizing the route based on the user's interests. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's interest data into the generation AI and cause the generation AI to customize the route.

[0057] During adjustment, the adjustment unit can optimize the route by referring to the user's past evaluations and feedback. The adjustment unit, for example, proposes a related route based on tourist spots that the user has given high ratings to in the past. The adjustment unit can also analyze the user's past feedback and propose a route that reflects improvements. Furthermore, the adjustment unit can also propose an optimal route based on the user's past evaluation history. This makes it possible to optimize the route by referring to the user's past evaluations and feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's evaluation data to the generation AI and cause the generation AI to optimize the route.

[0058] During the adjustment, the adjustment unit can analyze the user's social media activity to suggest a related route. The adjustment unit can suggest a related route based on, for example, a place shared by the user on social media. The adjustment unit can also analyze the content of the user's social media posts and suggest a route based on interests. Furthermore, the adjustment unit can also suggest a related route based on a place visited by the user's social media friends. In this way, a related route can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of a related route.

[0059] The adjustment unit can provide an optimal route by considering the device information of the user at the time of adjustment. For example, when the user is using a smartphone, the adjustment unit provides a route that matches the screen size. In addition, when the user is using a tablet, the adjustment unit can also provide a route optimized for a large screen. Furthermore, when the user is using a smartwatch, the adjustment unit can also provide a concise route with high visibility. In this way, the optimal route can be provided by considering the device information of the user. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the device information of the user to the generation AI and cause the generation AI to provide the optimal route.

[0060] During adjustment, the adjustment unit can complement the route by referring to the user's past travel plans. The adjustment unit, for example, suggests a related route based on the contents of a trip planned by the user in the past. The adjustment unit can also predict and suggest places to be visited in a specific season from the user's past travel plans. Furthermore, the adjustment unit can also analyze the user's past travel plans and suggest an optimal route. In this way, the route can be complemented by referring to the user's past travel plans. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past travel data into the generation AI and cause the generation AI to execute route complementation.

[0061] During adjustment, the adjustment unit can propose an optimal route by taking into account the user's current weather information. For example, in rainy weather, the adjustment unit preferentially proposes a route with a roof or an underground passage. In addition, the adjustment unit can also propose a route with a good view in fine weather. Furthermore, the adjustment unit can also propose a route that is not slippery on a snowy day. In this way, the optimal route can be proposed by taking into account the user's current weather information. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's weather data into the generation AI and cause the generation AI to execute the proposal of an optimal route.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The reservation unit can propose optimal services taking into consideration the user's budget. For example, if the user is planning a trip on a low budget, it can propose cost-effective accommodations and tourist spots. Also, if the user is planning a trip on a high budget, it can propose luxurious accommodations and tourist spots. Furthermore, it can provide specific discount and campaign information according to the user's budget. This makes it possible to propose optimal services according to the user's budget.

[0064] The adjustment unit can collect activity data during the user's trip and optimize the route in real time. For example, if the user stays for a long time at a particular tourist spot, the adjustment unit can suggest a route that shortens the travel time to the next destination. In addition, if the user finishes sightseeing earlier than planned, the adjustment unit can suggest additional tourist spots. Furthermore, the adjustment unit can suggest a route that avoids crowds based on the user's activity data. This makes it possible to provide the optimal route according to the user's situation during the trip.

[0065] The suggestion unit can suggest tourist spots taking into consideration the user's food preferences. For example, if the user is a vegetarian, tourist spots with vegetarian restaurants can be suggested. Also, if the user likes a particular dish, tourist spots where the dish can be enjoyed can be suggested. Furthermore, if the user has an allergy, tourist spots that take the allergy into consideration can be suggested. In this way, it is possible to suggest optimal tourist spots according to the user's food preferences.

[0066] The adjustment unit can monitor the user's means of transportation during the trip and adjust the route according to the means of transportation. For example, if the user uses public transportation, a route with fewer transfers can be proposed. If the user uses a private car, a route including tourist spots with parking lots can be proposed. Furthermore, if the user uses a bicycle, a route including bicycle-only roads can be proposed. This makes it possible to provide the optimal route according to the user's means of transportation.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, etc. For example, when the user inputs "I want to go skiing," this information is received. Step 2: The suggestion unit analyzes the information received by the reception unit and suggests tourist spots. For example, if the user inputs "I want to go skiing," the suggestion unit uses AI to analyze the information input by the user and suggests ski resorts with good snow quality. Step 3: The reservation unit reserves services based on the information suggested by the suggestion unit. For example, when the user decides to go to a suggested ski resort, the reservation unit automatically reserves services such as accommodation and rentals. Step 4: The adjustment unit adjusts the route according to the progress of the user's sightseeing. For example, if the user is 30 minutes behind schedule during sightseeing, or if an unexpected situation such as a service suspension or traffic congestion occurs, the route is automatically adjusted.

[0069] (Example 2) The tourism support system according to the embodiment of the present invention is a system that suggests tourist spots based on user input, books services, and adjusts the route according to the progress of tourism. When a user inputs "I want to go skiing," this tourism support system has AI suggest places with good snow quality, and then books services such as accommodation and rentals according to the user's request. Furthermore, when a user inputs that they want to go to a specific city, the system suggests tourist spots such as museums and art galleries in that city, automatically plans a route based on the user's input date and time, and presents transportation costs and means of transportation. During the progress of tourism, if an unexpected situation occurs, such as a delay of 30 minutes from the schedule, a suspension of service, or traffic congestion, the AI ​​automatically adjusts the route. For example, when a user inputs "I want to go skiing," this information is sent to the reception unit. Next, there is an analysis unit that analyzes the information received by the reception unit, and the analysis unit analyzes the user's input information and suggests tourist spots. For example, the AI ​​suggests a ski resort with good snow quality. Next, there is a reservation unit that books services based on the proposed information, and when the user decides to go to the proposed ski resort, the reservation unit automatically books services such as accommodation and rentals. Furthermore, there is an adjustment unit that adjusts the route according to the progress of the user's sightseeing. If the user is 30 minutes behind schedule or an unexpected situation such as a suspension or traffic congestion occurs during sightseeing, the adjustment unit automatically adjusts the route. The reception unit also estimates the user's emotions, adjusts the timing of input reception based on the estimated emotions, analyzes past input history to suggest the optimal input method, and automatically completes the input content based on current location information. Furthermore, it analyzes the user's social media activities to suggest related input content, and provides an appropriate input interface based on device information. The analysis unit estimates the user's emotions and adjusts the expression method of the proposed content based on the estimated emotions. This allows the tourism support system to suggest tourist spots based on the user's input, book services, and adjust the route according to the progress of sightseeing.

[0070] The tourism support system according to the embodiment includes a reception unit, a proposal unit, a reservation unit, and an adjustment unit. The reception unit accepts user input. The user input includes, but is not limited to, text input, voice input, and image input. For example, when the user inputs "I want to go skiing," the reception unit accepts this information. The proposal unit analyzes the information accepted by the reception unit and proposes tourist spots. The proposal unit analyzes the user's input information, for example, using AI, and proposes tourist spots. For example, when the user inputs "I want to go skiing," the AI ​​proposes a ski resort with good snow quality. The reservation unit reserves services based on the information proposed by the proposal unit. For example, when the user decides to go to the proposed ski resort, the reservation unit automatically reserves services such as accommodation and rental. The adjustment unit adjusts the route according to the progress of the user's sightseeing. For example, when the user is 30 minutes behind schedule during sightseeing, or when an unexpected situation such as a suspension or traffic congestion occurs, the adjustment unit automatically adjusts the route. As a result, the tourism support system according to the embodiment can suggest tourist spots based on the user's input, book services, and adjust the route according to the progress of the tour.

[0071] The reception unit accepts user input. Examples of user input include, but are not limited to, text input, voice input, and image input. Specifically, in the text input, the user can input the desired sightseeing contents using a keyboard or a touch screen. In the voice input, the user dictates the desire through a microphone, and the contents are converted into text using voice recognition technology. In the image input, the user uploads photos of places he or she wants to go to or activities he or she is interested in, and the contents are analyzed using image analysis technology. For example, when the user inputs "I want to go skiing," this information is accepted. In order to process these inputs quickly and accurately, the reception unit utilizes advanced natural language processing technology, voice recognition technology, and image analysis technology. Furthermore, the reception unit can learn the user's past input history and preferences and provide more personalized services. For example, for a user who has requested a ski trip in the past, ski-related suggestions are preferentially displayed. In addition, the reception unit can analyze the user's input contents in real time and request additional information as necessary. For example, for a user who has input "I want to go skiing," the reception unit prepares to make more appropriate suggestions by asking for detailed information such as the desired area, budget, and whether or not there is a companion. This allows the reception unit to respond to a variety of input formats from users and receive information quickly and accurately.

[0072] The suggestion unit analyzes the information received by the reception unit and suggests tourist spots. The suggestion unit, for example, uses AI to analyze the information input by the user and suggests tourist spots. Specifically, the AI ​​uses natural language processing technology to understand the user's input content and searches the database for related tourist spots. For example, if the user inputs "I want to go skiing," the AI ​​analyzes information about ski resorts and suggests ski resorts with good snow quality. Furthermore, the suggestion unit can make more personalized suggestions by taking into account the user's past travel history and preferences. For example, based on data on places visited in the past and tourist spots evaluated, a new ski resort that is likely to be liked by the user is suggested. In addition, the suggestion unit selects the most suitable tourist spot by taking into account real-time data such as season, weather, and event information. For example, if the current weather is good or if an event is being held at a specific ski resort, the suggestion unit makes a suggestion based on that information. Furthermore, the suggestion unit presents multiple options based on the user's input content, allowing the user to select the tourist spot that interests them most. This allows the suggestion unit to quickly and accurately suggest the most suitable tourist spot according to the user's needs.

[0073] The reservation unit reserves services based on the information proposed by the suggestion unit. Specifically, when a user decides to go to a proposed ski resort, the reservation unit automatically reserves services such as accommodation and rentals. The reservation unit acquires reservation information for accommodations, transportation, and activities related to the proposed tourist spot from a database, and selects an optimal plan according to the user's wishes. For example, when a user decides to go to a ski resort, reservations for nearby hotels, ski equipment rental services, and lift tickets can be made all at once. Furthermore, the reservation unit can propose a more personalized reservation plan taking into consideration the user's input contents and past reservation history. For example, based on the accommodations used in the past and the preferred activities, the reservation unit preferentially proposes a plan that is likely to satisfy the user. In addition, the reservation unit checks availability in real time and determines the optimal reservation timing. For example, if a specific accommodation is fully booked, a flexible response according to the user's wishes can be made by suggesting another facility in the vicinity. Furthermore, the reservation unit also responds to changes and cancellations of reservation contents, providing flexible services according to the user's needs. In this way, the reservation unit can smoothly access the proposed tourist spots and support a comfortable travel experience.

[0074] The adjustment unit adjusts the route according to the progress of the user's sightseeing. Specifically, if the user is 30 minutes behind schedule during sightseeing or if an unexpected situation such as a suspension of service or traffic congestion occurs, the route is automatically adjusted. The adjustment unit collects traffic information and weather information in real time to grasp the user's current location and progress. For example, if the user is caught in traffic congestion on the way to a tourist spot, the adjustment unit proposes an alternative route to minimize the delay. In addition, the sightseeing plan can be flexibly changed based on information such as a sudden change in weather or the cancellation of an event. Furthermore, the adjustment unit collects user feedback and optimizes the plan in real time. For example, if a user wants to extend the stay time at a specific tourist spot, the travel time to the next destination is adjusted based on that information. In addition, the adjustment unit can also handle cases where multiple users use the service at the same time and adjust the schedule of the entire group. As a result, the adjustment unit can maximally support the user's sightseeing experience and flexibly respond to unexpected situations. Furthermore, the adjustment unit learns the user's past sightseeing history and preferences, and contributes to optimizing future sightseeing plans. As a result, the adjustment unit can make the user's sightseeing experience more comfortable and smooth, and improve satisfaction.

[0075] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated emotion. For example, when the user is stressed, the reception unit delays the timing of input reception to provide time for the user to relax. In addition, when the user is excited, the reception unit can quickly accept input and immediately make a suggestion. Furthermore, when the user is tired, the reception unit can adjust the timing of input reception and provide an easy input method. In this way, by adjusting the timing of input reception according to the user's emotion, it is possible to accept input at a more appropriate timing. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0076] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays tourist spots that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest tourist spots to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input data to the generation AI and cause the generation AI to suggest the optimal input method.

[0077] The reception unit can automatically complete the input contents based on the user's current location information. For example, when the user opens the app, the reception unit automatically acquires the current location and sets it as the starting point. In addition, when the user inputs a destination, the reception unit can also suggest the optimal candidate location in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the starting point. This can reduce the effort of inputting by automatically completing the input contents based on the user's current location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's current location data to the generation AI and cause the generation AI to automatically complete the input contents.

[0078] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated emotion. For example, when the user is in a hurry, the reception unit preferentially displays important input contents. In addition, the reception unit can also provide detailed input options when the user is relaxed. Furthermore, the reception unit can preferentially display simple input contents when the user is stressed. In this way, important contents can be preferentially displayed by determining the priority of the input contents according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0079] The reception unit can analyze the user's social media activity and suggest related input contents. The reception unit can suggest related tourist spots based on, for example, places shared by the user on social media. The reception unit can also analyze the user's social media postings and suggest input contents based on interests. Furthermore, the reception unit can also suggest related tourist spots based on places visited by the user's social media friends. In this way, related input contents can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related input contents.

[0080] The reception unit can provide an appropriate input interface based on the device information of the user. For example, when the user is using a smartphone, the reception unit can provide an input interface optimized for touch operations. In addition, when the user is using a tablet, the reception unit can also provide an input interface optimized for a large screen. Furthermore, when the user is using a smartwatch, the reception unit can also provide an input interface that is simple and highly visible. This can improve the user's convenience by providing an appropriate input interface based on the device information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the device information of the user to the generation AI and cause the generation AI to provide an appropriate input interface.

[0081] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposed content based on the estimated emotion. For example, when the user is relaxed, the suggestion unit makes a suggestion including detailed information. Also, when the user is in a hurry, the suggestion unit can make a concise suggestion that focuses on the main points. Furthermore, when the user is excited, the suggestion unit can make a visually attractive suggestion. This allows a more appropriate suggestion to be made by adjusting the expression method of the proposed content according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] At the time of suggestion, the suggestion unit can suggest optimal tourist spots by referring to the user's past travel history. The suggestion unit can, for example, suggest related tourist spots based on tourist spots that the user has visited in the past. The suggestion unit can also predict and suggest places to visit in a particular season from the user's past travel history. Furthermore, the suggestion unit can also analyze the user's past travel patterns and suggest optimal tourist spots. In this way, optimal tourist spots can be suggested by referring to the user's past travel history. A part or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or can be performed without using AI. For example, the suggestion unit can input the user's past travel data to the generation AI and cause the generation AI to suggest optimal tourist spots.

[0083] At the time of suggestion, the suggestion unit can suggest the optimal tourist spot taking into consideration the user's current location information. For example, the suggestion unit preferentially suggests tourist spots close to the user's current location. The suggestion unit can also suggest tourist spots that are easily accessible from the user's current location. Furthermore, the suggestion unit can also suggest tourist spots that are suitable for the weather and season of the user's current location. In this way, the optimal tourist spot can be suggested by taking into consideration the user's current location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's current location data to the generation AI and cause the generation AI to suggest the optimal tourist spot.

[0084] The suggestion unit can customize the proposed content based on the user's interests and concerns at the time of proposal. For example, the suggestion unit can suggest related tourist spots based on tourist spots in which the user has shown interest in the past. The suggestion unit can also suggest tourist spots along a specific theme based on the user's interests and concerns. Furthermore, the suggestion unit can analyze the user's past search history and social media activities to make suggestions based on the interests. This allows the proposal content to be customized based on the user's interests and concerns, making it possible to make more appropriate suggestions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's interest data into the generation AI and cause the generation AI to customize the proposed content.

[0085] The suggestion unit can optimize the proposed content by referring to the user's past evaluations and feedback at the time of proposal. For example, the suggestion unit suggests related tourist spots based on tourist spots that the user has given high ratings to in the past. The suggestion unit can also analyze the user's past feedback and make a proposal that reflects improvements. Furthermore, the suggestion unit can also suggest optimal tourist spots based on the user's past evaluation history. This makes it possible to optimize the proposed content by referring to the user's past evaluations and feedback. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's evaluation data to the generation AI and cause the generation AI to optimize the proposed content.

[0086] The suggestion unit can estimate the user's emotion and determine the priority of the proposed contents based on the estimated emotion. For example, when the user is in a hurry, the suggestion unit preferentially displays important proposed contents. In addition, the suggestion unit can also provide detailed proposed contents when the user is relaxed. Furthermore, when the user is excited, the suggestion unit can preferentially display visually attractive proposed contents. In this way, important contents can be preferentially displayed by determining the priority of the proposed contents according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] At the time of suggestion, the suggestion unit can analyze the user's social media activity to suggest related tourist spots. For example, the suggestion unit can suggest related tourist spots based on places shared by the user on social media. The suggestion unit can also analyze the content of the user's social media posts to suggest tourist spots based on interests. Furthermore, the suggestion unit can also suggest related tourist spots based on places visited by the user's social media friends. In this way, related tourist spots can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related tourist spots.

[0088] The suggestion unit can provide an optimal suggestion method by considering the device information of the user at the time of suggestion. For example, when the user is using a smartphone, the suggestion unit provides a suggestion method that matches the screen size. In addition, when the user is using a tablet, the suggestion unit can also provide a suggestion method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the suggestion unit can also provide a simple and highly visible suggestion method. This makes it possible to provide an optimal suggestion method by considering the device information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the device information of the user to the generation AI and cause the generation AI to provide the optimal suggestion method.

[0089] When making a proposal, the suggestion unit can complement the proposed content by referring to the user's past travel plans. The suggestion unit, for example, suggests related tourist spots based on the contents of trips the user planned in the past. The suggestion unit can also predict and suggest places to visit in a particular season from the user's past travel plans. Furthermore, the suggestion unit can analyze the user's past travel plans and suggest optimal tourist spots. This makes it possible to complement the proposed content by referring to the user's past travel plans. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's past travel data into the generation AI and cause the generation AI to execute the complement of the proposed content.

[0090] The suggestion unit can suggest optimal tourist spots by taking into account the user's current weather information when making a suggestion. For example, the suggestion unit preferentially suggests indoor tourist spots when it is raining. The suggestion unit can also preferentially suggest outdoor tourist spots when it is sunny. Furthermore, the suggestion unit can also suggest ski resorts and snowy tourist spots on snowy days. This makes it possible to suggest optimal tourist spots by taking into account the user's current weather information. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's weather data into the generation AI and cause the generation AI to execute the suggestion of optimal tourist spots.

[0091] The reservation unit can estimate the user's emotion and adjust the timing of the reservation based on the estimated emotion. For example, if the user is stressed, the reservation unit can delay the timing of the reservation to provide time for the user to relax. In addition, if the user is excited, the reservation unit can also quickly make a reservation and immediately confirm it. Furthermore, if the user is tired, the reservation unit can adjust the timing of the reservation and provide an easy reservation method. In this way, by adjusting the timing of the reservation according to the user's emotion, the reservation can be made at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] At the time of reservation, the reservation unit can suggest an optimal reservation method by referring to the user's past reservation history. The reservation unit can, for example, suggest related accommodations based on accommodations used by the user in the past. The reservation unit can also predict and suggest accommodations to be used in a particular season from the user's past reservation history. Furthermore, the reservation unit can also analyze the user's past reservation patterns and suggest an optimal reservation method. In this way, the optimal reservation method can be suggested by referring to the user's past reservation history. A part or all of the above-mentioned processing in the reservation unit can be performed, for example, using AI or can be performed without using AI. For example, the reservation unit can input the user's past reservation data into the generation AI and cause the generation AI to execute a suggestion of an optimal reservation method.

[0093] The reservation unit can provide the optimal reservation method by taking into consideration the user's current location information at the time of reservation. For example, the reservation unit preferentially suggests accommodation facilities close to the user's current location. The reservation unit can also suggest accommodation facilities with good access from the user's current location. Furthermore, the reservation unit can also suggest accommodation facilities suitable for the weather and season of the user's current location. In this way, the optimal reservation method can be provided by taking into consideration the user's current location information. A part or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's current location data to the generation AI and cause the generation AI to provide the optimal reservation method.

[0094] The reservation unit can customize the reservation contents based on the user's interests and concerns at the time of reservation. For example, the reservation unit can suggest related accommodations based on accommodations in which the user has shown interest in the past. The reservation unit can also suggest accommodations according to a specific theme based on the user's interests and concerns. Furthermore, the reservation unit can analyze the user's past search history and social media activities and suggest reservation contents based on the interests. This allows the reservation contents to be customized based on the user's interests and concerns, making it possible to make a more appropriate reservation. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI or may be performed without using AI. For example, the reservation unit can input the user's interest data into the generation AI and cause the generation AI to customize the reservation contents.

[0095] The reservation unit can optimize the reservation contents by referring to the user's past evaluations and feedback at the time of reservation. For example, the reservation unit can suggest related accommodations based on accommodations that the user has given high ratings to in the past. The reservation unit can also analyze the user's past feedback and suggest reservation contents that reflect improvements. Furthermore, the reservation unit can also suggest optimal accommodations based on the user's past evaluation history. This makes it possible to optimize the reservation contents by referring to the user's past evaluations and feedback. A part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's evaluation data to the generation AI and cause the generation AI to optimize the reservation contents.

[0096] The reservation unit can estimate the user's emotion and determine the priority of the reservation contents based on the estimated emotion. For example, when the user is in a hurry, the reservation unit preferentially displays important reservation contents. In addition, the reservation unit can also provide detailed reservation contents when the user is relaxed. Furthermore, the reservation unit can preferentially display visually attractive reservation contents when the user is excited. In this way, important contents can be preferentially displayed by determining the priority of the reservation contents according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reservation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] At the time of reservation, the reservation unit can analyze the user's social media activity and reserve related services. The reservation unit can suggest related accommodations based on, for example, places shared by the user on social media. The reservation unit can also analyze the content of the user's social media posts and suggest accommodations based on interests. Furthermore, the reservation unit can also suggest related accommodations based on places visited by the user's social media friends. In this way, related services can be reserved by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI or may be performed without using AI. For example, the reservation unit can input the user's social media data into the generation AI and cause the generation AI to execute reservations for related services.

[0098] The reservation unit can provide an optimal reservation method by taking into account the device information of the user at the time of reservation. For example, when the user is using a smartphone, the reservation unit provides a reservation method that matches the screen size. In addition, when the user is using a tablet, the reservation unit can also provide a reservation method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the reservation unit can also provide a reservation method that is simple and highly visible. In this way, the optimal reservation method can be provided by taking into account the device information of the user. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the device information of the user to the generation AI and cause the generation AI to provide the optimal reservation method.

[0099] The reservation unit can complement the reservation contents by referring to the user's past travel plans at the time of reservation. The reservation unit, for example, suggests related accommodations based on the contents of trips planned by the user in the past. The reservation unit can also predict and suggest accommodations to be used in a specific season from the user's past travel plans. Furthermore, the reservation unit can analyze the user's past travel plans and suggest optimal accommodations. In this way, the reservation contents can be complemented by referring to the user's past travel plans. A part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's past travel data into the generation AI and cause the generation AI to execute complementing of the reservation contents.

[0100] The reservation unit can reserve the optimal service by taking into consideration the user's current weather information at the time of reservation. For example, the reservation unit preferentially suggests indoor accommodations when it is raining. The reservation unit can also preferentially suggest outdoor accommodations when it is sunny. Furthermore, the reservation unit can also suggest accommodations close to ski resorts on snowy days. This allows the optimal service to be reserved by taking into consideration the user's current weather information. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's weather data into the generation AI and cause the generation AI to execute the reservation of the optimal service.

[0101] The adjustment unit can estimate the user's emotion and adjust the timing of route adjustment based on the estimated emotion. For example, when the user is stressed, the adjustment unit delays the timing of route adjustment to provide time for the user to relax. In addition, when the user is excited, the adjustment unit can quickly adjust the route and immediately confirm it. Furthermore, when the user is tired, the adjustment unit can adjust the timing of route adjustment and provide a simple route. In this way, the route can be adjusted at a more appropriate timing by adjusting the timing of route adjustment according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0102] During adjustment, the adjustment unit can suggest an optimal route by referring to the user's past travel history. The adjustment unit can suggest an optimal route based on, for example, routes used by the user in the past. The adjustment unit can also suggest a route that avoids congestion from the user's past travel history. Furthermore, the adjustment unit can also analyze the user's past travel history and suggest the most efficient route. In this way, the optimal route can be suggested by referring to the user's past travel history. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past travel data to the generation AI and cause the generation AI to suggest an optimal route.

[0103] During adjustment, the adjustment unit can provide an optimal route by taking into account the user's current location information. For example, the adjustment unit preferentially proposes a route close to the user's current location. The adjustment unit can also propose a route with good access from the user's current location. Furthermore, the adjustment unit can also propose a route suitable for the weather and season of the user's current location. This makes it possible to provide an optimal route by taking into account the user's current location information. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's current location data to the generation AI and cause the generation AI to provide an optimal route.

[0104] During the adjustment, the adjustment unit can customize the route based on the user's interests. For example, the adjustment unit can suggest a related route based on tourist spots in which the user has shown interest in the past. The adjustment unit can also suggest a route along a specific theme based on the user's interests. Furthermore, the adjustment unit can analyze the user's past search history and social media activities to suggest a route based on the interests. This makes it possible to provide a more appropriate route by customizing the route based on the user's interests. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's interest data into the generation AI and cause the generation AI to customize the route.

[0105] During adjustment, the adjustment unit can optimize the route by referring to the user's past evaluations and feedback. The adjustment unit, for example, proposes a related route based on tourist spots that the user has given high ratings to in the past. The adjustment unit can also analyze the user's past feedback and propose a route that reflects improvements. Furthermore, the adjustment unit can also propose an optimal route based on the user's past evaluation history. This makes it possible to optimize the route by referring to the user's past evaluations and feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's evaluation data to the generation AI and cause the generation AI to optimize the route.

[0106] The adjustment unit can estimate the user's emotion and determine the priority of the route adjustment based on the estimated emotion. For example, when the user is in a hurry, the adjustment unit prioritizes important route adjustment. In addition, the adjustment unit can also provide detailed route adjustment when the user is relaxed. Furthermore, when the user is excited, the adjustment unit can prioritize visually attractive route adjustment. In this way, important content can be preferentially displayed by determining the priority of the route adjustment according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0107] During the adjustment, the adjustment unit can analyze the user's social media activity to suggest a related route. The adjustment unit can suggest a related route based on, for example, a place shared by the user on social media. The adjustment unit can also analyze the content of the user's social media posts and suggest a route based on interests. Furthermore, the adjustment unit can also suggest a related route based on a place visited by the user's social media friends. In this way, a related route can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of a related route.

[0108] The adjustment unit can provide an optimal route by considering the device information of the user at the time of adjustment. For example, when the user is using a smartphone, the adjustment unit provides a route that matches the screen size. In addition, when the user is using a tablet, the adjustment unit can also provide a route optimized for a large screen. Furthermore, when the user is using a smartwatch, the adjustment unit can also provide a concise route with high visibility. In this way, the optimal route can be provided by considering the device information of the user. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the device information of the user to the generation AI and cause the generation AI to provide the optimal route.

[0109] During adjustment, the adjustment unit can complement the route by referring to the user's past travel plans. The adjustment unit, for example, suggests a related route based on the contents of a trip planned by the user in the past. The adjustment unit can also predict and suggest places to be visited in a specific season from the user's past travel plans. Furthermore, the adjustment unit can also analyze the user's past travel plans and suggest an optimal route. In this way, the route can be complemented by referring to the user's past travel plans. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's past travel data into the generation AI and cause the generation AI to execute route complementation.

[0110] During adjustment, the adjustment unit can propose an optimal route by taking into account the user's current weather information. For example, in rainy weather, the adjustment unit preferentially proposes a route with a roof or an underground passage. In addition, the adjustment unit can also propose a route with a good view in fine weather. Furthermore, the adjustment unit can also propose a route that is not slippery on a snowy day. In this way, the optimal route can be proposed by taking into account the user's current weather information. A part or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the user's weather data into the generation AI and cause the generation AI to execute the proposal of an optimal route.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The suggestion unit can monitor the user's health condition and adjust the content of the suggestions. For example, if the user feels tired, it can suggest tourist spots where the user can relax. Also, if the user is active, it can suggest tourist spots with a lot of activity. Furthermore, if the user has a specific health problem, it can suggest tourist spots that take that problem into consideration. In this way, it is possible to suggest optimal tourist spots according to the user's health condition.

[0113] The reservation unit can propose optimal services taking into consideration the user's budget. For example, if the user is planning a trip on a low budget, it can propose cost-effective accommodations and tourist spots. Also, if the user is planning a trip on a high budget, it can propose luxurious accommodations and tourist spots. Furthermore, it can provide specific discount and campaign information according to the user's budget. This makes it possible to propose optimal services according to the user's budget.

[0114] The adjustment unit can collect activity data during the user's trip and optimize the route in real time. For example, if the user stays for a long time at a particular tourist spot, the adjustment unit can suggest a route that shortens the travel time to the next destination. In addition, if the user finishes sightseeing earlier than planned, the adjustment unit can suggest additional tourist spots. Furthermore, the adjustment unit can suggest a route that avoids crowds based on the user's activity data. This makes it possible to provide the optimal route according to the user's situation during the trip.

[0115] The suggestion unit can suggest tourist spots taking into consideration the user's food preferences. For example, if the user is a vegetarian, tourist spots with vegetarian restaurants can be suggested. Also, if the user likes a particular dish, tourist spots where the dish can be enjoyed can be suggested. Furthermore, if the user has an allergy, tourist spots that take the allergy into consideration can be suggested. In this way, it is possible to suggest optimal tourist spots according to the user's food preferences.

[0116] The adjustment unit can monitor the user's mood during the trip and adjust the route according to the mood. For example, if the user is tired, a route including places where the user can rest can be suggested. Also, if the user is excited, a route with a lot of activity can be suggested. Furthermore, if the user is stressed, a route that allows the user to relax can be suggested. In this way, it is possible to provide an optimal route according to the user's mood.

[0117] The suggestion unit can monitor the energy level of the user during the trip and suggest tourist spots according to the energy level. For example, if the user is full of energy, tourist spots with many activities can be suggested. Also, if the user is exhausted, tourist spots where the user can relax can be suggested. Furthermore, timings for meals and rest can be suggested according to the user's energy level. This makes it possible to suggest optimal tourist spots according to the user's energy level.

[0118] The reservation unit can monitor the user's physical condition during the trip and adjust the reservation contents according to the user's physical condition. For example, if the user feels unwell, the reservation unit can suggest accommodations with flexible cancellation policies. Also, if the user is in good health, the reservation unit can suggest accommodations with many activities. Furthermore, the reservation unit can suggest accommodations that provide specific health services according to the user's physical condition. This makes it possible to provide optimal reservation contents according to the user's physical condition.

[0119] The adjustment unit can monitor the stress level of the user during the trip and adjust the route according to the stress level. For example, if the user is in a high stress state, a route including tourist spots where the user can relax can be suggested. Also, if the user is in a low stress state, a route with many activities can be suggested. Furthermore, the timing of breaks can be suggested according to the user's stress level. In this way, the optimal route according to the user's stress level can be provided.

[0120] The suggestion unit can monitor the sleep data of the user during the trip and suggest tourist spots based on the sleep data. For example, if the user is not getting enough sleep, the suggestion unit can suggest tourist spots where the user can relax. Also, if the user is getting enough sleep, the suggestion unit can suggest tourist spots with a lot of activity. Furthermore, the suggestion unit can suggest the timing of rest based on the user's sleep data. This makes it possible to suggest optimal tourist spots according to the user's sleep data.

[0121] The adjustment unit can monitor the user's means of transportation during the trip and adjust the route according to the means of transportation. For example, if the user uses public transportation, a route with fewer transfers can be proposed. If the user uses a private car, a route including tourist spots with parking lots can be proposed. Furthermore, if the user uses a bicycle, a route including bicycle-only roads can be proposed. This makes it possible to provide the optimal route according to the user's means of transportation.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, etc. For example, when the user inputs "I want to go skiing," this information is received. Step 2: The suggestion unit analyzes the information received by the reception unit and suggests tourist spots. For example, if the user inputs "I want to go skiing" by using AI to analyze the information input by the user, the suggestion unit suggests ski resorts with good snow quality. Step 3: The reservation unit reserves services based on the information suggested by the suggestion unit. For example, when a user decides to go to a suggested ski resort, the reservation unit automatically reserves services such as accommodation and rentals. Step 4: The adjustment unit adjusts the route according to the progress of the user's sightseeing. For example, if the user is 30 minutes behind schedule during sightseeing, or if an unexpected situation such as a service suspension or traffic congestion occurs, the route is automatically adjusted.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0126] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives text input and voice input from the user. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI and suggests tourist spots. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and automatically reserves services such as accommodation and rentals based on the proposed information. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and adjusts the route according to the progress of the tour. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0135] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the user's input information using AI and suggests tourist spots. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and automatically reserves services such as accommodation and rentals based on the proposed information. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and adjusts the route according to the progress of the sightseeing. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and receives voice input from the user. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the user's input information using AI and suggests tourist spots. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and automatically reserves services such as accommodation and rentals based on the proposed information. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and adjusts the route according to the progress of the tour. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The suggestion unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the input information of the user using AI and suggests tourist spots. The reservation unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and automatically reserves services such as accommodation and rentals based on the proposed information. The adjustment unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and adjusts the route according to the progress of the sightseeing. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0178] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0182] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] In addition, 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 upon request from the data processing device 12.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0189] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0193] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0195] (Appendix 1) A reception unit that receives input from a user; A suggestion unit that analyzes the information received by the reception unit and suggests tourist spots; a reservation unit that reserves a service based on the information proposed by the proposal unit; An adjustment unit that adjusts the route according to the progress of the user's sightseeing. A system characterized by: (Appendix 2) The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. 2. The system of claim 1, (Appendix 3) The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 4) The reception unit is Auto-complete input based on the user's current location 2. The system of claim 1, (Appendix 5) The reception unit is Infer user emotions and prioritize inputs based on the inferred emotions 2. The system of claim 1, (Appendix 6) The reception unit is Analyze your social media activity and suggest relevant entries 2. The system of claim 1, (Appendix 7) The reception unit is Providing an appropriate input interface based on the user's device information 2. The system of claim 1, (Appendix 8) The suggestion unit, Infer the user's emotions and adjust the way suggestions are presented based on the inferred emotions 2. The system of claim 1, (Appendix 9) The suggestion unit, When making suggestions, the system refers to the user's past travel history to suggest appropriate tourist spots. 2. The system of claim 1, (Appendix 10) The suggestion unit, When making suggestions, the system considers the user's current location information to suggest appropriate tourist spots. 2. The system of claim 1, (Appendix 11) The suggestion unit, When making suggestions, tailor the suggestions based on the user's interests 2. The system of claim 1, (Appendix 12) The suggestion unit, When making suggestions, the system refers to the user's past ratings and feedback to make the suggestions more appropriate. 2. The system of claim 1, (Appendix 13) The suggestion unit, Infer user emotions and prioritize suggestions based on the inferred emotions 2. The system of claim 1, (Appendix 14) The suggestion unit, When making suggestions, the app analyzes users' social media activity to suggest relevant tourist spots. 2. The system of claim 1, (Appendix 15) The suggestion unit, When making suggestions, provide appropriate suggestions by taking into account the user's device information 2. The system of claim 1, (Appendix 16) The suggestion unit, When making suggestions, the system complements the suggestions by referencing the user's past travel plans. 2. The system of claim 1, (Appendix 17) The suggestion unit, When making suggestions, the system considers the user's current weather information to suggest appropriate tourist spots. 2. The system of claim 1, (Appendix 18) The reservation unit: Infer user emotions and adjust reservation timing based on the inferred emotions 2. The system of claim 1, (Appendix 19) The reservation unit: When making a reservation, the system will suggest the best way to make a reservation by referring to the user's past reservation history. 2. The system of claim 1, (Appendix 20) The reservation unit: When making a reservation, provide an appropriate reservation method by taking into account the user's current location information 2. The system of claim 1, (Appendix 21) The reservation unit: Customize your booking based on your interests at the time of booking 2. The system of claim 1, (Appendix 22) The reservation unit: When making a reservation, refer to the user's past ratings and feedback to make the reservation more appropriate. 2. The system of claim 1, (Appendix 23) The reservation unit: Infer user emotions and prioritize reservation contents based on the inferred emotions 2. The system of claim 1, (Appendix 24) The reservation unit: When making a booking, analyze your social media activity to book relevant services 2. The system of claim 1, (Appendix 25) The reservation unit: When making a reservation, provide an appropriate reservation method considering the user's device information 2. The system of claim 1, (Appendix 26) The reservation unit: When booking, reference the user's past travel plans to complete the booking 2. The system of claim 1, (Appendix 27) The reservation unit: When making a reservation, consider the user's current weather information to book the appropriate service 2. The system of claim 1, (Appendix 28) The adjustment unit is Estimate the user's emotions and adjust the timing of route adjustment based on the estimated emotions. 2. The system of claim 1, (Appendix 29) The adjustment unit is When making a trip, the system will suggest suitable routes by referencing the user's past travel history. 2. The system of claim 1, (Appendix 30) The adjustment unit is When making adjustments, the system takes into account the user's current location to provide an appropriate route 2. The system of claim 1, (Appendix 31) The adjustment unit is Customize your route based on your interests as you make adjustments 2. The system of claim 1, (Appendix 32) The adjustment unit is When making adjustments, the route is optimized by referring to the user's past ratings and feedback. 2. The system of claim 1, (Appendix 33) The adjustment unit is Estimate the user's emotions and prioritize route adjustments based on the estimated emotions. 2. The system of claim 1, (Appendix 34) The adjustment unit is When making a trip, the app analyzes users' social media activity to suggest relevant routes. 2. The system of claim 1, (Appendix 35) The adjustment unit is During coordination, the system takes into account the user's device information to provide an appropriate route 2. The system of claim 1, (Appendix 36) The adjustment unit is When making adjustments, the route is completed by referencing the user's past travel plans. 2. The system of claim 1, (Appendix 37) The adjustment unit is When making adjustments, the system will suggest an appropriate route taking into account the user's current weather information. 2. The system of claim 1, [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input from a user; A suggestion unit that analyzes the information received by the reception unit and suggests tourist spots; a reservation unit that reserves a service based on the information proposed by the proposal unit; An adjustment unit that adjusts the route according to the progress of the user's sightseeing. A system characterized by:

2. The reception unit is Estimating the emotion of the user and adjusting the timing of input acceptance based on the estimated emotion.

2. The system of claim 1 .

3. The reception unit is Analyze the user's past input history and suggest an appropriate input method 2. The system of claim 1 .

4. The reception unit is Auto-complete input based on the user's current location 2. The system of claim 1 .

5. The reception unit is Estimating the user's emotion and prioritizing the input contents based on the estimated emotion.

2. The system of claim 1 .

6. The reception unit is Analyze the user's social media activity and suggest relevant entries 2. The system of claim 1 .

7. The reception unit is Providing an appropriate input interface based on the user's device information 2. The system of claim 1 .

8. The suggestion unit, Estimating the user's emotion and adjusting the presentation of the proposal content based on the estimated emotion.

2. The system of claim 1 .

9. The suggestion unit, When making a suggestion, the system refers to the user's past travel history to suggest appropriate tourist spots.

2. The system of claim 1 .

10. The suggestion unit, When making suggestions, appropriate tourist spots are suggested taking into consideration the user's current location information.

2. The system of claim 1 .

Images