System

Abstract

To provide a system that enables a user to unitarily utilize a plurality of services when planning a trip using a natural language.SOLUTION: A system comprises a reception unit, an analysis unit, a proposal unit, a reservation unit, a notification unit, and a navigation unit. The reception unit receives input of a natural language from a user. The analysis unit analyzes the input of the natural language received by the reception unit and understands the user's intent. The proposal unit performs proposal or comparison of accommodations, restaurants, and sightseeing spots on the basis of the user's intent understood by the analysis unit. The reservation unit makes reservations for the accommodations, restaurants, or sightseeing spots proposed by the proposal unit. The notification unit notifies friends of the reservation information made by the reservation unit. The navigation unit performs navigation to the destination on the basis of the information notified by the notification unit.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 technology requires users to use multiple services when planning a trip, which can be time-consuming.

[0005] The system according to the embodiment aims to enable a user to use multiple services in an integrated manner when making travel plans in natural language. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a reservation unit, a communication unit, and a navigation unit. The reception unit accepts input in natural language from a user. The analysis unit analyzes the natural language input accepted by the reception unit and understands the user's intention. The proposal unit proposes or compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit. The reservation unit makes reservations for accommodations, restaurants, and tourist spots proposed by the proposal unit. The communication unit communicates information about the reservation made by the reservation unit to a friend. The navigation unit navigates to the local location based on the information communicated by the communication unit. Effect of the Invention

[0007] The system according to the embodiment can enable a user to use multiple services in an integrated manner when planning a trip in natural language. [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 a 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 system according to the embodiment of the present invention is a system in which, when a user communicates in natural language what they want to do, the system utilizes multiple services to realize the message. This system starts when the user sends a natural language message such as "I want to travel with my friends" through a messenger app. This message is analyzed by the generation AI to understand the user's intention. Next, the generation AI suggests and compares accommodations, restaurants, and tourist spots. For example, when a user inputs "I want to travel with my friends," the generation AI collects information on accommodations, restaurants, and tourist spots according to the destination and suggests them to the user. In addition, coordination with friends is made through the messenger app. The generation AI analyzes conversations between the user and friends and supports coordination of travel dates and destinations. Next, reservations are made for each facility. The generation AI automatically makes reservations for accommodations, restaurants, and tourist spots selected by the user. The generation AI also communicates decisions. For example, the travel dates and information on accommodations can be communicated to friends. In addition, the generation AI also supports navigation to the local area. The generation AI provides directions to the user until the user arrives at the travel destination, and also supports sharing information and photos on the local area. In this way, the present invention is a system that utilizes multiple services to realize what a user wants to do simply by communicating it in natural language. This allows the user to easily plan a trip and eliminates the need to make arrangements with friends, make reservations, navigate, etc. This allows the system to understand the user's intentions and consistently support trip planning, reservations, contact, and navigation.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a reservation unit, a contact unit, and a navigation unit. The reception unit accepts input in a natural language of the user. The input in the natural language of the user includes, for example, text input and voice input, but is not limited to such examples. The reception unit can accept the input in the natural language of the user through, for example, a messenger app. The analysis unit uses a generation AI to analyze the input in the natural language of the user accepted by the reception unit and understand the user's intention. The generation AI uses, for example, a text generation AI (for example, LLM) to analyze the input in the natural language of the user and understand the user's intention. The proposal unit proposes and compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit. The proposal unit collects, for example, information on accommodations, restaurants, and tourist spots, and proposes them to the user. The proposal unit can collect information by, for example, web scraping or using an API. The reservation unit makes reservations for accommodations, restaurants, and tourist spots proposed by the proposal unit. The reservation unit can make a reservation by, for example, online reservation or telephone reservation. The contact unit communicates the information reserved by the reservation unit to a friend. The contact unit can communicate the information by, for example, email or a messaging app. The navigation unit navigates to the location based on the information communicated by the contact unit. The navigation unit can provide route guidance by, for example, map display or voice guidance. In this way, the system according to the embodiment can consistently support trip planning, reservation, contact, and navigation based on the user's natural language input.

[0030] The reception unit accepts the input of the user's natural language. The input of the user's natural language includes, but is not limited to, for example, text input and voice input. The reception unit can accept the input of the user's natural language through, for example, a messenger app. Specifically, the user can use a smartphone or a personal computer to send a text message through a messenger app or a dedicated web interface. In addition, in the case of voice input, the user inputs a voice command using a microphone, and the system converts this into text using voice recognition technology. Furthermore, the reception unit provides an interface for accepting the user's input in real time and is designed to allow the user to operate smoothly. For example, when a user inputs a question or request, the system immediately returns feedback to improve the user experience. In addition, the reception unit is capable of handling multiple languages ​​and can handle input in different languages. This makes it a system that can also handle international users. Furthermore, the reception unit can provide more personalized services by saving the user's past input history and referring to it the next time the system is used. For example, for a user who has searched for a specific accommodation in the past, similar accommodations can be preferentially suggested. This allows the reception unit to efficiently accept the user's natural language input and improve the operability and convenience of the entire system.

[0031] The analysis unit uses the generation AI to analyze the natural language input received by the reception unit and understand the user's intention. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's natural language input and understand the user's intention. Specifically, the generation AI uses natural language processing technology to analyze the user's input text grammatically and semantically. For example, if a user inputs "Tell me about a recommended hotel in Tokyo next weekend," the generation AI extracts keywords such as "next weekend," "Tokyo," and "recommended hotel," and understands the user's intention as "I'm looking for a hotel to stay in Tokyo next weekend." In addition, the generation AI can refer to past conversation history and user profile information to understand the context. This allows it to grasp the user's intention more accurately and make appropriate suggestions. In addition, the generation AI has a model for generating appropriate responses to the user's input, and can quickly and accurately answer the user's questions. For example, if a user asks, "What are the recommended tourist spots in Tokyo?", the generation AI generates a specific answer such as "Tokyo Tower and Sensoji Temple are popular." In addition, the generative AI can not only understand the user's intent, but also analyze the user's emotions and tone. This allows the AI ​​to respond appropriately when the user is dissatisfied or when an urgent response is required. This allows the analysis unit to perform advanced analysis of the user's natural language input and accurately understand the user's intent.

[0032] The suggestion unit suggests and compares accommodations, restaurants, and tourist attractions based on the user's intention understood by the analysis unit. The suggestion unit, for example, collects information on accommodations, restaurants, and tourist attractions and suggests them to the user. Specifically, the suggestion unit can collect information by methods such as web scraping and API use. For example, the suggestion unit obtains the latest information from accommodation reservation sites, restaurant review sites, tourist information sites, etc., and provides it to the user. Furthermore, the suggestion unit can make optimal suggestions based on the collected information, tailored to the user's preferences, budget, length of stay, and other conditions. For example, if a user inputs "I'm looking for a hotel with a hot spring within a budget of 10,000 yen per night," the suggestion unit will list hotels that meet the conditions and suggest them to the user. In addition, the suggestion unit can make more personalized suggestions by referring to the user's past selections and ratings. For example, for a user who has previously preferred a specific area or type of accommodation, similar accommodations can be preferentially suggested. Furthermore, the suggestion unit can provide detailed information, ratings, and reviews of each proposal so that the user can compare multiple options. This allows the user to select the most suitable option from among the proposed options. The suggestion unit can also collect user feedback and continuously improve the accuracy and quality of the suggestions. This allows the suggestion unit to make optimal suggestions based on the user's intentions and improve user satisfaction.

[0033] The reservation unit makes reservations for the lodgings, restaurants, and tourist attractions proposed by the suggestion unit. The reservation unit can make reservations by, for example, online reservations, telephone reservations, and other methods. Specifically, the reservation unit works with reservation systems for the proposed lodgings, restaurants, and tourist attractions to automatically process the user's reservation requests. For example, if a user inputs "I would like to make a reservation at this hotel," the reservation unit accesses the hotel's online reservation system and completes the reservation by entering the necessary information. The reservation unit can also support procedures such as confirmation, change, and cancellation of reservations. For example, if a user inputs "I would like to change my reservation," the reservation unit proposes changeable dates and plans and changes the reservation according to the user's wishes. Furthermore, the reservation unit can manage the reservation status in real time and provide the user with the latest information. For example, when a reservation is confirmed, the reservation unit sends a confirmation email to the user and notifies the user of the reservation details. The reservation unit can also provide a smoother reservation procedure by saving the user's past reservation history and referring to it the next time the user makes a reservation. This allows the reservation unit to efficiently support the user's reservation procedure and improve user convenience.

[0034] The contact unit notifies the friend of the information reserved by the reservation unit. The contact unit can communicate the information by, for example, email, a messaging app, or other methods. Specifically, the contact unit notifies the friend or family designated by the user of the reservation details and details of the travel plan. For example, when the user inputs "Inform my friend of this reservation details," the contact unit transmits the reservation details to the friend's email address or message app account. The contact unit also has a contact management function, and can list the people with whom the user frequently contacts, so that the user can quickly contact them. Furthermore, the contact unit can customize the contact details, and can change the content and format of the message according to the user's desire. For example, when the user inputs "Attach a photo to this message," the contact unit attaches the designated photo to the message and sends it. The contact unit can also save the contact history and refer to the past contact details. This allows the user to check messages sent in the past and resend them as necessary. This allows the contact unit to quickly and accurately notify the friend of the user's reservation information and support sharing of travel plans.

[0035] The navigation unit navigates to the location based on the information communicated by the communication unit. The navigation unit can provide directions by, for example, displaying a map or providing voice guidance. Specifically, the navigation unit calculates an optimal route based on the user's current location and destination, and provides navigation in real time. For example, when the user inputs "Tell me the way to this hotel," the navigation unit uses a map application to display the optimal route and provides voice guidance. The navigation unit can also dynamically adjust the route by taking into account traffic conditions and weather information. For example, when there is congestion or road construction, the navigation unit can suggest a detour route so that the user can reach the destination smoothly. Furthermore, the navigation unit can also provide information on public transportation. For example, when the user inputs "Tell me how to get from the nearest station to the hotel," the navigation unit can suggest an optimal transfer route based on train and bus timetables. The navigation unit can also suggest various means of transportation, such as walking routes and bicycle routes, according to the user's preferences. In this way, the navigation unit can support the user to reach the destination smoothly, thereby improving the convenience of the trip.

[0036] The suggestion unit can collect information on accommodations, restaurants, and tourist attractions and suggest them to the user. The suggestion unit can collect information on accommodations, restaurants, and tourist attractions using, for example, web scraping technology. For example, the suggestion unit can collect information on accommodations from a specific website and suggest it to the user. The suggestion unit can also use an API to collect information on accommodations, restaurants, and tourist attractions. For example, the suggestion unit can use an API of a travel information providing service to obtain the latest information on accommodations, restaurants, and tourist attractions and suggest it to the user. This allows the suggestion unit to provide the user with information on appropriate accommodations, restaurants, and tourist attractions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the collected information into an AI model and generate optimal suggestions for the user.

[0037] The analysis unit can analyze the user's natural language input using the generation AI and understand the user's intention. The analysis unit can, for example, use the generation AI to analyze the user's natural language input. The generation AI can, for example, use a text generation AI (e.g., LLM) to analyze the user's natural language input and understand the user's intention. For example, if the user inputs "I want to travel with friends," the generation AI understands the intention and provides information related to travel. In addition, the generation AI can perform keyword extraction and context analysis based on the user's input content and more accurately understand the user's intention. For example, the generation AI extracts keywords such as "travel," "friends," and "accommodation" from the user's input content and understands the user's intention based on the keywords. This allows the analysis unit to accurately analyze the user's natural language input and understand the intention.

[0038] The reservation unit can make reservations for accommodations, restaurants, and tourist attractions selected by the user. The reservation unit, for example, uses an online reservation system to make reservations for accommodations, restaurants, and tourist attractions selected by the user. For example, the reservation unit accesses the online reservation system for the accommodation and completes the reservation by inputting the user's reservation information. The reservation unit can also make a telephone reservation. For example, the reservation unit makes a reservation by calling the restaurant selected by the user. Furthermore, the reservation unit can automatically make a reservation for the tourist attraction selected by the user. For example, the reservation unit accesses the online reservation system for the tourist attraction and completes the reservation by inputting the user's reservation information. In this way, the reservation unit can automatically make a reservation for the facility selected by the user. Some 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 reservation information into an AI model and select the optimal reservation method.

[0039] The contact unit can notify the friend of the travel schedule and the accommodation information. The contact unit can, for example, use email to notify the friend of the travel schedule and the accommodation information. For example, the contact unit sends information on the accommodation selected by the user to the friend by email. The contact unit can also use a message app to notify the friend of the travel schedule and the accommodation information. For example, the contact unit sends information on the restaurant selected by the user to the friend through a message app. Furthermore, the contact unit can also use a telephone to notify the friend of the travel schedule and the accommodation information. For example, the contact unit conveys information on the tourist spot selected by the user to the friend by telephone. This allows the contact unit to automatically notify the friend of the detailed information of the trip. A part or all of the above-mentioned processing in the contact unit may be performed, for example, using AI, or may be performed without using AI. For example, the contact unit can input the user's contact information into an AI model and select the optimal contact method.

[0040] The navigation unit can guide the user along the route until the user arrives at the travel destination. The navigation unit, for example, uses a map display to guide the user along the route until the user arrives at the travel destination. For example, the navigation unit uses a map application to display the route from the user's current location to the travel destination. The navigation unit can also use voice guidance to guide the user along the route until the user arrives at the travel destination. For example, the navigation unit uses voice guidance to guide the user along the route. Furthermore, the navigation unit can update the route in real time and guide the user along the optimal route. For example, the navigation unit acquires traffic information in real time and suggests the optimal route to the user. In this way, the navigation unit can support navigation until the user arrives at the travel destination. A part or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's current location information into an AI model and select the optimal navigation method.

[0041] The navigation unit can support the sharing of information and photos at the local site. The navigation unit can support the sharing of information and photos at the local site by using, for example, social media. For example, the navigation unit can support posting of photos taken by the user to social media. The navigation unit can also support the sharing of information and photos at the local site by using cloud storage. For example, the navigation unit can support uploading of photos taken by the user to cloud storage and sharing with friends. Furthermore, the navigation unit can support the sharing of information and photos at the local site by using a message app. For example, the navigation unit can support sending of photos taken by the user to friends through a message app. In this way, the navigation unit can support the sharing of information and photos at the local site. A part or all of the above-mentioned processing in the navigation unit may be performed by using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's photo data into an AI model and select an optimal sharing method.

[0042] The reception unit can analyze the user's past input history and select the reception method. The reception unit, for example, saves and analyzes the user's past input history as a text log. For example, the reception unit automatically displays phrases that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. For example, if the user used voice input a lot in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest phrases to be used in a specific time period from the user's past input history. For example, if the user inputted "Good morning" in a specific time period in the past, the reception unit displays "Good morning" as a candidate in that time period. This allows the reception unit to provide an optimal reception method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input history data into an AI model and select the optimal reception method.

[0043] The reception unit can perform filtering based on the user's current situation and field of interest when receiving input in natural language. The reception unit, for example, acquires the user's current situation from location information and performs filtering. For example, when the user is traveling, the reception unit preferentially accepts travel-related input. The reception unit can also acquire the user's field of interest from past search history and perform filtering. For example, the reception unit preferentially accepts input related to a theme that the user has searched for frequently in the past. Furthermore, the reception unit can acquire the user's current activity status in real time and perform filtering. For example, when the user is participating in a specific event, the reception unit preferentially accepts input related to the event. This allows the reception unit to filter the input based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information and search history data into an AI model and select an optimal filtering method.

[0044] The reception unit can preferentially receive highly relevant inputs in consideration of the geographical location information of the user when receiving inputs in natural language. The reception unit, for example, uses GPS data to acquire the geographical location information of the user and preferentially accepts highly relevant inputs. For example, when the user is in a specific city, the reception unit preferentially accepts information related to the city. The reception unit can also use Wi-Fi location information to acquire the geographical location information of the user and preferentially accept highly relevant inputs. For example, when the user is traveling, the reception unit preferentially accepts information related to the travel destination. Furthermore, when the user is in a specific facility, the reception unit can also preferentially accept information related to the facility. For example, when the user is in a specific event venue, the reception unit preferentially accepts information related to the event. This allows the reception unit to preferentially accept highly relevant inputs based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user into an AI model and select an optimal reception method.

[0045] The reception unit can analyze the social media activity of the user and receive related inputs when receiving inputs in natural language. The reception unit, for example, analyzes the social media activity of the user and receives related inputs. For example, the reception unit preferentially receives related inputs based on information shared by the user on social media. The reception unit can also preferentially receive related inputs based on information on accounts the user follows on social media. For example, the reception unit preferentially receives travel-related inputs based on information on travel-related accounts the user follows. Furthermore, the reception unit can also preferentially receive related inputs based on information on an event the user is participating in on social media. For example, the reception unit preferentially receives inputs related to the event based on information on a music festival the user is participating in. This allows the reception unit to receive related inputs based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user into an AI model and select an optimal reception method.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during the analysis. The analysis unit, for example, evaluates the importance of the input and adjusts the level of detail of the analysis based on the evaluation. For example, the analysis unit performs a detailed analysis on important inputs and a standard analysis on general inputs. The analysis unit can also perform a concise analysis on simple inputs. For example, when a user inputs "I want to prepare for an important meeting," the analysis unit performs a detailed analysis and provides necessary information. On the other hand, when a user inputs "I want to find a place to have lunch," the analysis unit performs a standard analysis and makes appropriate suggestions. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the input into an AI model and select an optimal analysis method.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. The analysis unit, for example, classifies the category of the input and applies different analysis algorithms accordingly. For example, the analysis unit applies a travel-specific analysis algorithm to travel-related input, and applies a food-specific analysis algorithm to food-related input. Also, the analysis unit can apply a tourist destination-specific analysis algorithm to tourist destination-related input. For example, when a user inputs "I want to find accommodation at my travel destination," the analysis unit applies a travel-specific analysis algorithm to suggest the most suitable accommodation. On the other hand, when a user inputs "I want to find a delicious restaurant," the analysis unit applies a food-specific analysis algorithm to suggest an appropriate restaurant. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the input. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category data of the input into an AI model and select the optimal analysis algorithm.

[0048] The analysis unit can determine the priority of the analysis based on the time of input submission during the analysis. The analysis unit, for example, evaluates the time of input submission and determines the priority of the analysis based on the evaluation. For example, the analysis unit performs analysis on urgent input with priority, and performs analysis on general input with standard priority. Also, analysis can be postponed for input with low importance. For example, when the user inputs "Please provide information that is necessary immediately," the analysis unit performs analysis with priority and provides information quickly. On the other hand, when the user inputs "Please provide information that I want to check later," the analysis unit performs analysis with standard priority. This allows the analysis unit to determine the priority of the analysis based on the time of input submission. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input input submission time data into an AI model and select an optimal analysis method.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. The analysis unit, for example, evaluates the relevance of the input and adjusts the order of analysis based on the evaluation. For example, the analysis unit performs analysis preferentially on highly relevant inputs and performs analysis in a standard order on inputs of general relevance. Also, analysis can be postponed for inputs of low relevance. For example, when a user inputs "I want to find accommodations for my travel destination," the analysis unit performs analysis preferentially on highly relevant information and suggests the most suitable accommodations. On the other hand, when a user inputs "I want you to provide me with general information," the analysis unit performs analysis in a standard order. This allows the analysis unit to adjust the order of analysis based on the relevance of the input. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the input into an AI model and select the optimal analysis method.

[0050] The suggestion unit can adjust the level of detail of the proposal based on the importance of the accommodation, restaurant, and tourist destination at the time of proposal. The suggestion unit, for example, evaluates the importance of the accommodation, restaurant, and tourist destination, and adjusts the level of detail of the proposal based on the evaluation. For example, the suggestion unit makes detailed proposals for important accommodations and standard proposals for general restaurants. It can also make simple proposals for simple tourist destinations. For example, when a user inputs "I want to find a luxury hotel," the suggestion unit makes a detailed proposal and provides the necessary information. On the other hand, when a user inputs "I want to find a place for lunch," the suggestion unit makes a standard proposal and suggests an appropriate restaurant. This allows the suggestion unit to adjust the level of detail of the proposal depending on the importance of the accommodation, restaurant, and tourist destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the accommodation, restaurant, and tourist destination into an AI model and select the optimal suggestion method.

[0051] The suggestion unit can apply different proposal algorithms according to the categories of accommodations, restaurants, and tourist attractions at the time of proposal. The suggestion unit, for example, classifies categories of accommodations, restaurants, and tourist attractions, and applies different proposal algorithms accordingly. For example, the suggestion unit applies a proposal algorithm dedicated to luxury accommodations to luxury accommodations, and applies a proposal algorithm dedicated to general restaurants to general restaurants. Also, the suggestion unit can apply a proposal algorithm dedicated to tourist attractions to tourist attractions. For example, when a user inputs "I want to find a luxury hotel," the suggestion unit applies a proposal algorithm dedicated to luxury accommodations to suggest an optimal luxury hotel. On the other hand, when a user inputs "I want to find a casual restaurant," the suggestion unit applies a proposal algorithm dedicated to general restaurants to suggest an appropriate restaurant. This allows the suggestion unit to apply an appropriate proposal algorithm according to the categories of accommodations, restaurants, and tourist attractions. 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 category data of accommodations, restaurants, and tourist attractions into an AI model and select an optimal proposal algorithm.

[0052] The suggestion unit can determine the priority of the proposal based on the submission time of the accommodation, restaurant, and tourist attraction at the time of proposal. The suggestion unit, for example, evaluates the submission time of the accommodation, restaurant, and tourist attraction, and determines the priority of the proposal based on the evaluation. For example, the suggestion unit makes a proposal with priority for an emergency accommodation, and makes a proposal with standard priority for a general restaurant. Also, the suggestion unit can make a proposal later for a tourist attraction with low importance. For example, when the user inputs "I want to find a place to stay right now," the suggestion unit makes a proposal with priority and quickly provides a place to stay. On the other hand, when the user inputs "I want to find a tourist attraction that I want to check later," the suggestion unit makes a proposal with standard priority. This allows the suggestion unit to determine the priority of the proposal based on the submission time of the accommodation, restaurant, and tourist attraction. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the submission time data of the accommodation, restaurant, and tourist attraction into an AI model and select the optimal suggestion method.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the accommodations, restaurants, and tourist attractions at the time of suggestion. The suggestion unit, for example, evaluates the relevance of the accommodations, restaurants, and tourist attractions, and adjusts the order of suggestions based on the evaluation. For example, the suggestion unit makes suggestions preferentially to accommodations with high relevance, and makes suggestions in a standard order to restaurants with general relevance. Also, the suggestion unit can make suggestions later for tourist attractions with low relevance. For example, when a user inputs "I want to find accommodations for my travel destination," the suggestion unit makes suggestions preferentially to accommodations with high relevance. On the other hand, when a user inputs "I want to find general tourist attractions," the suggestion unit makes suggestions in a standard order. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the accommodations, restaurants, and tourist attractions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of accommodations, restaurants, and tourist attractions into an AI model and select an optimal suggestion method.

[0054] At the time of reservation, the reservation unit can select the optimal reservation method by analyzing the user's past reservation history. The reservation unit, for example, saves and analyzes the user's past reservation history as a text log. For example, the reservation unit preferentially suggests accommodations that the user has frequently used in the past. The reservation unit can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. For example, if the user has used online reservations a lot in the past, the reservation unit preferentially suggests online reservations. Furthermore, the reservation unit can predict and suggest accommodations to be used in a specific time period from the user's past reservation history. For example, the reservation unit suggests accommodations suitable for that time period based on accommodations that the user has used in a specific time period in the past. This allows the reservation unit to provide the optimal reservation method based on the user's past reservation history. Part or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's reservation history data into an AI model to select the optimal reservation method.

[0055] The reservation unit can customize the reservation means based on the user's current situation at the time of reservation. The reservation unit, for example, obtains the user's current situation from location information and customizes the reservation means based on the location information. For example, when the user is traveling, the reservation unit preferentially suggests local reservations. In addition, when the user is at work, the reservation unit can also preferentially suggest work-related reservations. For example, when the user inputs "I want to reserve a conference room" while at work, the reservation unit preferentially suggests work-related reservations. Furthermore, when the user is participating in a specific event, the reservation unit can also preferentially suggest reservations related to the event. For example, when the user is participating in a music festival, the reservation unit preferentially suggests reservations for accommodations and restaurants related to the event. This allows the reservation unit to customize the reservation means according to the user's current situation. Some 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 location information and current situation data into an AI model to select the optimal reservation means.

[0056] The reservation unit can select the optimal reservation method in consideration of the geographical location information of the user at the time of reservation. The reservation unit, for example, uses GPS data to acquire the geographical location information of the user and selects the optimal reservation method based on the information. For example, when the user is in a specific city, the reservation unit preferentially suggests accommodations related to the city. The reservation unit can also acquire the geographical location information of the user using Wi-Fi location information and select the optimal reservation method based on the information. For example, when the user is traveling, the reservation unit preferentially suggests accommodations related to the travel destination. Furthermore, when the user is in a specific facility, the reservation unit can also preferentially suggest accommodations related to the facility. For example, when the user is in a specific event venue, the reservation unit preferentially suggests accommodations related to the event. This allows the reservation unit to provide the optimal reservation method based on the geographical location 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 geographical location information of the user into an AI model and select the optimal reservation method.

[0057] The reservation unit can analyze the user's social media activity at the time of reservation and suggest a means of reservation. The reservation unit, for example, analyzes the user's social media activity and suggests a means of reservation based on the analysis. For example, the reservation unit preferentially suggests related accommodations based on information shared by the user on social media. The reservation unit can also preferentially suggest related accommodations based on information on accounts the user follows on social media. For example, the reservation unit suggests accommodations at travel destinations based on information on travel-related accounts the user follows. Furthermore, the reservation unit can also preferentially suggest related accommodations based on information on events the user is participating in on social media. For example, the reservation unit suggests accommodations related to the events based on information on music festivals the user is participating in. This allows the reservation unit to provide an optimal reservation means based on the user's social media activity. Some 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 social media activity data into an AI model and select the optimal reservation means.

[0058] The contact unit can select the optimal contact method by analyzing the user's past contact history at the time of contact. The contact unit, for example, saves and analyzes the user's past contact history as a text log. For example, the contact unit preferentially suggests a contact method (email, messenger, etc.) that the user has frequently used in the past. The contact unit can also preferentially suggest a contact method with a person with whom the user has contacted in the past. For example, the contact unit suggests contact by email again to a person with whom the user has contacted in the past by email. Furthermore, the contact unit can predict and suggest a contact method to be used in a specific time period from the user's past contact history. For example, if the user has used a messenger in a specific time period in the past, the contact unit suggests contact by messenger in that time period. This allows the contact unit to provide the optimal contact method based on the user's past contact history. A part or all of the above-mentioned processing in the contact unit may be performed using, for example, AI, or may be performed without using AI. For example, the contact unit can input the user's contact history data into an AI model and select the optimal contact method.

[0059] The contact unit can select the optimal contact method in consideration of the geographical location information of the user at the time of contact. The contact unit, for example, uses GPS data to acquire the geographical location information of the user and selects the optimal contact method based on the acquired information. For example, when the user is in a specific city, the contact unit preferentially proposes a contact method related to the city. The contact unit can also acquire the geographical location information of the user using Wi-Fi location information and select the optimal contact method based on the acquired information. For example, when the user is traveling, the contact unit preferentially proposes a contact method related to the travel destination. Furthermore, when the user is in a specific facility, the contact unit can also preferentially propose a contact method related to the facility. For example, when the user is in a specific event venue, the contact unit preferentially proposes a contact method related to the event. This allows the contact unit to provide the optimal contact method based on the geographical location information of the user. Part or all of the above-mentioned processing in the contact unit may be performed, for example, using AI, or may be performed without using AI. For example, the contact unit can input the geographical location information of the user into an AI model and select the optimal contact method.

[0060] During navigation, the navigation unit can select an optimal navigation method by analyzing the user's past movement history. The navigation unit, for example, saves and analyzes the user's past movement history as a text log. For example, the navigation unit proposes an optimal navigation method based on the route used by the user in the past. The navigation unit can also propose a navigation method that avoids congestion from the user's past movement history. For example, the navigation unit proposes an optimal route based on the route used by the user in the past to avoid congestion. Furthermore, the navigation unit can analyze the user's past movement history and propose the most efficient navigation method. For example, the navigation unit proposes an optimal navigation method based on the shortest route used by the user in the past. This allows the navigation unit to provide an optimal navigation method based on the user's past movement history. A part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's movement history data into an AI model and select an optimal navigation method.

[0061] The navigation unit can perform navigation by updating the user's current location information in real time during navigation. The navigation unit can update the user's current location information in real time using, for example, GPS data, and perform navigation based on the updated location. For example, the navigation unit can update the user's current location in real time while the user is moving and propose an optimal route. The navigation unit can also update the current location in real time and propose an optimal route as the user approaches the destination. For example, the navigation unit can obtain traffic information in real time as the user approaches the destination and propose an optimal route. Furthermore, if the user gets lost, the navigation unit can update the current location in real time and perform navigation again. For example, if the user gets lost, the navigation unit can update the current location in real time and re-propose the optimal route. This allows the navigation unit to perform navigation in real time based on the user's current location information. Part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's current location information into an AI model and select an optimal navigation method.

[0062] The navigation unit can select the optimal navigation method in consideration of the geographical location information of the user during navigation. The navigation unit, for example, uses GPS data to acquire the geographical location information of the user and selects the optimal navigation method based on the acquired information. For example, when the user is in a specific city, the navigation unit preferentially proposes a navigation method related to the city. The navigation unit can also acquire the geographical location information of the user using Wi-Fi location information and select the optimal navigation method based on the acquired information. For example, when the user is traveling, the navigation unit preferentially proposes a navigation method related to the travel destination. Furthermore, when the user is in a specific facility, the navigation unit can also preferentially propose a navigation method related to the facility. For example, when the user is in a specific event venue, the navigation unit preferentially proposes a navigation method related to the event. This allows the navigation unit to provide the optimal navigation method based on the geographical location information of the user. Part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the geographical location information of the user into an AI model and select the optimal navigation method.

[0063] The navigation unit can analyze the user's social media activities and suggest a navigation means during navigation. The navigation unit, for example, analyzes the user's social media activities and suggests a navigation means based on the results. For example, the navigation unit preferentially suggests a related navigation method based on information shared by the user on social media. The navigation unit can also preferentially suggest a related navigation method based on information on accounts the user follows on social media. For example, the navigation unit suggests a navigation method for a travel destination based on information on travel-related accounts the user follows. Furthermore, the navigation unit can also preferentially suggest a related navigation method based on information on an event the user is participating in on social media. For example, the navigation unit suggests a navigation method related to the event based on information on a music festival the user is participating in. This allows the navigation unit to provide an optimal navigation means based on the user's social media activities. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's social media activity data into an AI model to select an optimal navigation means.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past input history and automatically display phrases and keywords that the user frequently uses as candidates. For example, if the user frequently inputs "I want to travel with friends" in the past, the reception unit displays the phrase as a candidate. The reception unit can also preferentially suggest an input method (such as voice or text) that the user used in the past. For example, if the user used voice input a lot in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest a phrase to be used in a specific time period from the user's past input history. For example, if the user inputs "good morning" in a specific time period in the past, the reception unit displays "good morning" as a candidate for that time period. This allows the reception unit to provide an optimal reception method based on the user's past input history.

[0066] The suggestion unit can analyze the user's past selection history and give priority to suggesting accommodations, restaurants, and tourist spots that the user prefers. For example, if the user has frequently selected luxury hotels in the past, the suggestion unit will give priority to suggesting luxury hotels. In addition, the suggestion unit can also suggest similar restaurants based on the types of restaurants (Japanese, Italian, etc.) that the user has selected in the past. Furthermore, the suggestion unit can analyze the characteristics (natural scenery, historical buildings, etc.) of tourist spots that the user has visited in the past and suggest tourist spots with similar characteristics. This allows the suggestion unit to make more personalized suggestions based on the user's past selection history.

[0067] The analysis unit can automatically complete related information based on the user's input. For example, if the user inputs "I want to travel with friends," the analysis unit automatically completes related information such as "travel destination," "lodging," and "restaurant." In addition, if the user inputs "I want to find a good restaurant," the analysis unit can complete information such as "location of the restaurant," "type of cuisine," and "budget." Furthermore, if the user inputs "I want to visit a tourist spot," the analysis unit can complete information such as "characteristics of the tourist spot," "access method," and "stay time." This allows the analysis unit to provide more detailed information based on the user's input.

[0068] The reservation unit can analyze the user's past reservation history and give priority to suggesting the reservation method preferred by the user. For example, if the user has made many online reservations in the past, the reservation unit can give priority to suggesting online reservations. Also, if the user has made many telephone reservations in the past, the reservation unit can give priority to suggesting telephone reservations. Furthermore, if the user has made reservations during a specific time period in the past, the reservation unit can also suggest a reservation method suitable for that time period. For example, if the user has made reservations at night in the past, the reservation unit can suggest a reservation method suitable for nighttime. In this way, the reservation unit can provide the optimal reservation method based on the user's past reservation history.

[0069] The contact unit can analyze the user's past contact history and preferentially suggest the contact method that the user prefers. For example, if the user used email a lot in the past, the contact unit preferentially suggests email. Also, if the user used messenger apps a lot in the past, the contact unit can preferentially suggest messenger apps. Furthermore, if the user has contacted during a specific time period in the past, the contact unit can suggest a contact method suitable for that time period. For example, if the user has contacted in the morning in the past, the contact unit can suggest a contact method suitable for the morning. In this way, the contact unit can provide the optimal contact method based on the user's past contact history.

[0070] The navigation unit can analyze the user's past movement history and preferentially suggest a movement route that the user prefers. For example, the navigation unit can suggest an optimal navigation method based on routes that the user has used in the past. The navigation unit can also suggest an optimal route based on routes that the user has used in the past to avoid congestion. Furthermore, the navigation unit can also suggest an optimal navigation method based on the shortest route that the user has used in the past. In this way, the navigation unit can provide an optimal navigation method based on the user's past movement history.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The reception unit receives a natural language input from the user. The natural language input from the user includes, but is not limited to, a text input or a voice input. The reception unit can receive the natural language input from the user through, for example, a messenger app. Step 2: The analysis unit uses the generation AI to analyze the natural language input received by the reception unit and understand the user's intention. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's natural language input and understand the user's intention. Step 3: The suggestion unit suggests and compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit. The suggestion unit, for example, collects information on accommodations, restaurants, and tourist spots and suggests them to the user. The suggestion unit can collect information by, for example, web scraping or using an API. Step 4: The reservation unit makes reservations for the lodgings, restaurants, and tourist spots proposed by the suggestion unit. The reservation unit can make reservations, for example, online or by phone. Step 5: The contact section communicates the information reserved by the reservation section to the friend. The contact section can communicate the information by, for example, email, a messaging app, or other methods. Step 6: The navigation unit navigates to the location based on the information provided by the communication unit. The navigation unit can provide route guidance by, for example, displaying a map or providing voice guidance.

[0073] (Example 2) The system according to the embodiment of the present invention is a system in which, when a user communicates in natural language what they want to do, the system utilizes multiple services to realize the message. This system starts when the user sends a natural language message such as "I want to travel with my friends" through a messenger app. This message is analyzed by the generation AI to understand the user's intention. Next, the generation AI suggests and compares accommodations, restaurants, and tourist spots. For example, when a user inputs "I want to travel with my friends," the generation AI collects information on accommodations, restaurants, and tourist spots according to the destination and suggests them to the user. In addition, coordination with friends is made through the messenger app. The generation AI analyzes conversations between the user and friends and supports coordination of travel dates and destinations. Next, reservations are made for each facility. The generation AI automatically makes reservations for accommodations, restaurants, and tourist spots selected by the user. The generation AI also communicates decisions. For example, the travel dates and information on accommodations can be communicated to friends. In addition, the generation AI also supports navigation to the local area. The generation AI provides directions to the user until the user arrives at the travel destination, and also supports sharing information and photos on the local area. In this way, the present invention is a system that utilizes multiple services to realize what a user wants to do simply by communicating it in natural language. This allows the user to easily plan a trip and eliminates the need to make arrangements with friends, make reservations, navigate, etc. This allows the system to understand the user's intentions and consistently support trip planning, reservations, contact, and navigation.

[0074] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a reservation unit, a contact unit, and a navigation unit. The reception unit accepts input in a natural language of the user. The input in the natural language of the user includes, for example, text input and voice input, but is not limited to such examples. The reception unit can accept the input in the natural language of the user through, for example, a messenger app. The analysis unit uses a generation AI to analyze the input in the natural language of the user accepted by the reception unit and understand the user's intention. The generation AI uses, for example, a text generation AI (for example, LLM) to analyze the input in the natural language of the user and understand the user's intention. The proposal unit proposes and compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit. The proposal unit collects, for example, information on accommodations, restaurants, and tourist spots, and proposes them to the user. The proposal unit can collect information by, for example, web scraping or using an API. The reservation unit makes reservations for accommodations, restaurants, and tourist spots proposed by the proposal unit. The reservation unit can make a reservation by, for example, online reservation or telephone reservation. The contact unit communicates the information reserved by the reservation unit to a friend. The contact unit can communicate the information by, for example, email or a messaging app. The navigation unit navigates to the location based on the information communicated by the contact unit. The navigation unit can provide route guidance by, for example, map display or voice guidance. In this way, the system according to the embodiment can consistently support trip planning, reservation, contact, and navigation based on the user's natural language input.

[0075] The reception unit accepts the input of the user's natural language. The input of the user's natural language includes, but is not limited to, for example, text input and voice input. The reception unit can accept the input of the user's natural language through, for example, a messenger app. Specifically, the user can use a smartphone or a personal computer to send a text message through a messenger app or a dedicated web interface. In addition, in the case of voice input, the user inputs a voice command using a microphone, and the system converts this into text using voice recognition technology. Furthermore, the reception unit provides an interface for accepting the user's input in real time and is designed to allow the user to operate smoothly. For example, when a user inputs a question or request, the system immediately returns feedback to improve the user experience. In addition, the reception unit is capable of handling multiple languages ​​and can handle input in different languages. This makes it a system that can also handle international users. Furthermore, the reception unit can provide more personalized services by saving the user's past input history and referring to it the next time the system is used. For example, for a user who has searched for a specific accommodation in the past, similar accommodations can be preferentially suggested. This allows the reception unit to efficiently accept the user's natural language input and improve the operability and convenience of the entire system.

[0076] The analysis unit uses the generation AI to analyze the natural language input received by the reception unit and understand the user's intention. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's natural language input and understand the user's intention. Specifically, the generation AI uses natural language processing technology to analyze the user's input text grammatically and semantically. For example, if a user inputs "Tell me about a recommended hotel in Tokyo next weekend," the generation AI extracts keywords such as "next weekend," "Tokyo," and "recommended hotel," and understands the user's intention as "I'm looking for a hotel to stay in Tokyo next weekend." In addition, the generation AI can refer to past conversation history and user profile information to understand the context. This allows it to grasp the user's intention more accurately and make appropriate suggestions. In addition, the generation AI has a model for generating appropriate responses to the user's input, and can quickly and accurately answer the user's questions. For example, if a user asks, "What are the recommended tourist spots in Tokyo?", the generation AI generates a specific answer such as "Tokyo Tower and Sensoji Temple are popular." In addition, the generative AI can not only understand the user's intent, but also analyze the user's emotions and tone. This allows the AI ​​to respond appropriately when the user is dissatisfied or when an urgent response is required. This allows the analysis unit to perform advanced analysis of the user's natural language input and accurately understand the user's intent.

[0077] The suggestion unit suggests and compares accommodations, restaurants, and tourist attractions based on the user's intention understood by the analysis unit. The suggestion unit, for example, collects information on accommodations, restaurants, and tourist attractions and suggests them to the user. Specifically, the suggestion unit can collect information by methods such as web scraping and API use. For example, the suggestion unit obtains the latest information from accommodation reservation sites, restaurant review sites, tourist information sites, etc., and provides it to the user. Furthermore, the suggestion unit can make optimal suggestions based on the collected information, tailored to the user's preferences, budget, length of stay, and other conditions. For example, if a user inputs "I'm looking for a hotel with a hot spring within a budget of 10,000 yen per night," the suggestion unit will list hotels that meet the conditions and suggest them to the user. In addition, the suggestion unit can make more personalized suggestions by referring to the user's past selections and ratings. For example, for a user who has previously preferred a specific area or type of accommodation, similar accommodations can be preferentially suggested. Furthermore, the suggestion unit can provide detailed information, ratings, and reviews of each proposal so that the user can compare multiple options. This allows the user to select the most suitable option from among the proposed options. The suggestion unit can also collect user feedback and continuously improve the accuracy and quality of the suggestions. This allows the suggestion unit to make optimal suggestions based on the user's intentions and improve user satisfaction.

[0078] The reservation unit makes reservations for the lodgings, restaurants, and tourist attractions proposed by the suggestion unit. The reservation unit can make reservations by, for example, online reservations, telephone reservations, and other methods. Specifically, the reservation unit works with reservation systems for the proposed lodgings, restaurants, and tourist attractions to automatically process the user's reservation requests. For example, if a user inputs "I want to make a reservation at this hotel," the reservation unit accesses the hotel's online reservation system and completes the reservation by entering the necessary information. The reservation unit can also support procedures such as confirmation, change, and cancellation of reservations. For example, if a user inputs "I want to change my reservation," the reservation unit proposes changeable dates and plans and changes the reservation according to the user's wishes. Furthermore, the reservation unit can manage the reservation status in real time and provide the user with the latest information. For example, when a reservation is confirmed, the reservation unit sends a confirmation email to the user and notifies the user of the reservation details. The reservation unit can also provide a smoother reservation procedure by saving the user's past reservation history and referring to it the next time the user makes a reservation. This allows the reservation unit to efficiently support the user's reservation procedure and improve user convenience.

[0079] The contact unit notifies the friend of the information reserved by the reservation unit. The contact unit can communicate the information by, for example, email, a messaging app, or other methods. Specifically, the contact unit notifies the friend or family designated by the user of the reservation details and details of the travel plan. For example, when the user inputs "Inform my friend of this reservation details," the contact unit transmits the reservation details to the friend's email address or message app account. The contact unit also has a contact management function, and can list the people with whom the user frequently contacts, so that the user can quickly contact them. Furthermore, the contact unit can customize the contact details, and can change the content and format of the message according to the user's desire. For example, when the user inputs "Attach a photo to this message," the contact unit attaches the designated photo to the message and sends it. The contact unit can also save the contact history and refer to the past contact details. This allows the user to check messages sent in the past and resend them as necessary. This allows the contact unit to quickly and accurately notify the friend of the user's reservation information and support sharing of travel plans.

[0080] The navigation unit navigates to the location based on the information communicated by the communication unit. The navigation unit can provide directions by, for example, displaying a map or providing voice guidance. Specifically, the navigation unit calculates an optimal route based on the user's current location and destination, and provides navigation in real time. For example, when the user inputs "Tell me the way to this hotel," the navigation unit uses a map application to display the optimal route and provides voice guidance. The navigation unit can also dynamically adjust the route by taking into account traffic conditions and weather information. For example, when there is congestion or road construction, the navigation unit can suggest a detour route so that the user can reach the destination smoothly. Furthermore, the navigation unit can also provide information on public transportation. For example, when the user inputs "Tell me how to get from the nearest station to the hotel," the navigation unit can suggest an optimal transfer route based on train and bus timetables. The navigation unit can also suggest various means of transportation, such as walking routes and bicycle routes, according to the user's preferences. In this way, the navigation unit can support the user to reach the destination smoothly, thereby improving the convenience of the trip.

[0081] The suggestion unit can collect information on accommodations, restaurants, and tourist attractions and suggest them to the user. The suggestion unit can collect information on accommodations, restaurants, and tourist attractions using, for example, web scraping technology. For example, the suggestion unit can collect information on accommodations from a specific website and suggest it to the user. The suggestion unit can also use an API to collect information on accommodations, restaurants, and tourist attractions. For example, the suggestion unit can use an API of a travel information providing service to obtain the latest information on accommodations, restaurants, and tourist attractions and suggest it to the user. This allows the suggestion unit to provide the user with information on appropriate accommodations, restaurants, and tourist attractions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the collected information into an AI model and generate optimal suggestions for the user.

[0082] The analysis unit can analyze the user's natural language input using the generation AI and understand the user's intention. The analysis unit can, for example, use the generation AI to analyze the user's natural language input. The generation AI can, for example, use a text generation AI (e.g., LLM) to analyze the user's natural language input and understand the user's intention. For example, if the user inputs "I want to travel with friends," the generation AI understands the intention and provides information related to travel. In addition, the generation AI can perform keyword extraction and context analysis based on the user's input content and more accurately understand the user's intention. For example, the generation AI extracts keywords such as "travel," "friends," and "accommodation" from the user's input content and understands the user's intention based on the keywords. This allows the analysis unit to accurately analyze the user's natural language input and understand the intention.

[0083] The reservation unit can make reservations for accommodations, restaurants, and tourist attractions selected by the user. The reservation unit, for example, uses an online reservation system to make reservations for accommodations, restaurants, and tourist attractions selected by the user. For example, the reservation unit accesses the online reservation system for the accommodation and completes the reservation by inputting the user's reservation information. The reservation unit can also make a telephone reservation. For example, the reservation unit makes a reservation by calling the restaurant selected by the user. Furthermore, the reservation unit can automatically make a reservation for the tourist attraction selected by the user. For example, the reservation unit accesses the online reservation system for the tourist attraction and completes the reservation by inputting the user's reservation information. In this way, the reservation unit can automatically make a reservation for the facility selected by the user. Some 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 reservation information into an AI model and select the optimal reservation method.

[0084] The contact unit can notify the friend of the travel schedule and the accommodation information. The contact unit can, for example, use email to notify the friend of the travel schedule and the accommodation information. For example, the contact unit sends information on the accommodation selected by the user to the friend by email. The contact unit can also use a message app to notify the friend of the travel schedule and the accommodation information. For example, the contact unit sends information on the restaurant selected by the user to the friend through a message app. Furthermore, the contact unit can also use a telephone to notify the friend of the travel schedule and the accommodation information. For example, the contact unit conveys information on the tourist spot selected by the user to the friend by telephone. This allows the contact unit to automatically notify the friend of the detailed information of the trip. A part or all of the above-mentioned processing in the contact unit may be performed, for example, using AI, or may be performed without using AI. For example, the contact unit can input the user's contact information into an AI model and select the optimal contact method.

[0085] The navigation unit can guide the user along the route until the user arrives at the travel destination. The navigation unit, for example, uses a map display to guide the user along the route until the user arrives at the travel destination. For example, the navigation unit uses a map application to display the route from the user's current location to the travel destination. The navigation unit can also use voice guidance to guide the user along the route until the user arrives at the travel destination. For example, the navigation unit uses voice guidance to guide the user along the route. Furthermore, the navigation unit can update the route in real time and guide the user along the optimal route. For example, the navigation unit acquires traffic information in real time and suggests the optimal route to the user. In this way, the navigation unit can support navigation until the user arrives at the travel destination. A part or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's current location information into an AI model and select the optimal navigation method.

[0086] The navigation unit can support the sharing of information and photos at the local site. The navigation unit can support the sharing of information and photos at the local site by using, for example, social media. For example, the navigation unit can support posting of photos taken by the user to social media. The navigation unit can also support the sharing of information and photos at the local site by using cloud storage. For example, the navigation unit can support uploading of photos taken by the user to cloud storage and sharing with friends. Furthermore, the navigation unit can support the sharing of information and photos at the local site by using a message app. For example, the navigation unit can support sending of photos taken by the user to friends through a message app. In this way, the navigation unit can support the sharing of information and photos at the local site. A part or all of the above-mentioned processing in the navigation unit may be performed by using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's photo data into an AI model and select an optimal sharing method.

[0087] The reception unit can estimate the user's emotion and adjust the input reception timing of the natural language based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reception unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The reception unit can also estimate the user's emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This allows the reception unit to adjust the input reception timing of the natural language 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 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 a user's emotion data to the generation AI and cause the generation AI to adjust the timing of input reception based on the emotion.

[0088] The reception unit can analyze the user's past input history and select the reception method. The reception unit, for example, saves and analyzes the user's past input history as a text log. For example, the reception unit automatically displays phrases that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. For example, if the user used voice input a lot in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest phrases to be used in a specific time period from the user's past input history. For example, if the user inputted "Good morning" in a specific time period in the past, the reception unit displays "Good morning" as a candidate in that time period. This allows the reception unit to provide an optimal reception method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input history data into an AI model and select the optimal reception method.

[0089] The reception unit can perform filtering based on the user's current situation and field of interest when receiving input in natural language. The reception unit, for example, acquires the user's current situation from location information and performs filtering. For example, when the user is traveling, the reception unit preferentially accepts travel-related input. The reception unit can also acquire the user's field of interest from past search history and perform filtering. For example, the reception unit preferentially accepts input related to a theme that the user has searched for frequently in the past. Furthermore, the reception unit can acquire the user's current activity status in real time and perform filtering. For example, when the user is participating in a specific event, the reception unit preferentially accepts input related to the event. This allows the reception unit to filter the input based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information and search history data into an AI model and select an optimal filtering method.

[0090] The reception unit can estimate the user's emotion and determine the priority of the input to be received based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reception unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The reception unit can also estimate the user's emotion using a voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This allows the reception unit to determine the priority of the input 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 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 a user's emotion data to the generation AI and cause the generation AI to determine the priority of the input based on the emotion.

[0091] The reception unit can preferentially receive highly relevant inputs in consideration of the geographical location information of the user when receiving inputs in natural language. The reception unit, for example, uses GPS data to acquire the geographical location information of the user and preferentially accepts highly relevant inputs. For example, when the user is in a specific city, the reception unit preferentially accepts information related to the city. The reception unit can also use Wi-Fi location information to acquire the geographical location information of the user and preferentially accept highly relevant inputs. For example, when the user is traveling, the reception unit preferentially accepts information related to the travel destination. Furthermore, when the user is in a specific facility, the reception unit can also preferentially accept information related to the facility. For example, when the user is in a specific event venue, the reception unit preferentially accepts information related to the event. This allows the reception unit to preferentially accept highly relevant inputs based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user into an AI model and select an optimal reception method.

[0092] The reception unit can analyze the social media activity of the user and receive related inputs when receiving inputs in natural language. The reception unit, for example, analyzes the social media activity of the user and receives related inputs. For example, the reception unit preferentially receives related inputs based on information shared by the user on social media. The reception unit can also preferentially receive related inputs based on information on accounts the user follows on social media. For example, the reception unit preferentially receives travel-related inputs based on information on travel-related accounts the user follows. Furthermore, the reception unit can also preferentially receive related inputs based on information on an event the user is participating in on social media. For example, the reception unit preferentially receives inputs related to the event based on information on a music festival the user is participating in. This allows the reception unit to receive related inputs based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user into an AI model and select an optimal reception method.

[0093] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the expression method of the analysis 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed based on the emotion.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during the analysis. The analysis unit, for example, evaluates the importance of the input and adjusts the level of detail of the analysis based on the evaluation. For example, the analysis unit performs a detailed analysis on important inputs and a standard analysis on general inputs. The analysis unit can also perform a concise analysis on simple inputs. For example, when a user inputs "I want to prepare for an important meeting," the analysis unit performs a detailed analysis and provides necessary information. On the other hand, when a user inputs "I want to find a place to have lunch," the analysis unit performs a standard analysis and makes appropriate suggestions. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the input. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the input into an AI model and select an optimal analysis method.

[0095] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. The analysis unit, for example, classifies the category of the input and applies different analysis algorithms accordingly. For example, the analysis unit applies a travel-specific analysis algorithm to travel-related input, and applies a food-specific analysis algorithm to food-related input. Also, the analysis unit can apply a tourist destination-specific analysis algorithm to tourist destination-related input. For example, when a user inputs "I want to find accommodation at my travel destination," the analysis unit applies a travel-specific analysis algorithm to suggest the most suitable accommodation. On the other hand, when a user inputs "I want to find a delicious restaurant," the analysis unit applies a food-specific analysis algorithm to suggest an appropriate restaurant. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the input. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the category data of the input into an AI model and select the optimal analysis algorithm.

[0096] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the length of the analysis 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0097] The analysis unit can determine the priority of the analysis based on the time of input submission during the analysis. The analysis unit, for example, evaluates the time of input submission and determines the priority of the analysis based on the evaluation. For example, the analysis unit performs analysis on urgent input with priority, and performs analysis on general input with standard priority. Also, analysis can be postponed for input with low importance. For example, when the user inputs "Please provide information that is necessary immediately," the analysis unit performs analysis with priority and provides information quickly. On the other hand, when the user inputs "Please provide information that I want to check later," the analysis unit performs analysis with standard priority. This allows the analysis unit to determine the priority of the analysis based on the time of input submission. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input input submission time data into an AI model and select an optimal analysis method.

[0098] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. The analysis unit, for example, evaluates the relevance of the input and adjusts the order of analysis based on the evaluation. For example, the analysis unit performs analysis preferentially on highly relevant inputs and performs analysis in a standard order on inputs of general relevance. Also, analysis can be postponed for inputs of low relevance. For example, when a user inputs "I want to find accommodations for my travel destination," the analysis unit performs analysis preferentially on highly relevant information and suggests the most suitable accommodations. On the other hand, when a user inputs "I want you to provide me with general information," the analysis unit performs analysis in a standard order. This allows the analysis unit to adjust the order of analysis based on the relevance of the input. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the input into an AI model and select the optimal analysis method.

[0099] The suggestion unit can estimate the user's emotion and adjust the proposed expression method based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The suggestion unit can also estimate the user's emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate. This allows the suggestion unit to adjust the proposed expression method 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 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 a user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestion is expressed based on the emotion.

[0100] The suggestion unit can adjust the level of detail of the proposal based on the importance of the accommodation, restaurant, and tourist destination at the time of proposal. The suggestion unit, for example, evaluates the importance of the accommodation, restaurant, and tourist destination, and adjusts the level of detail of the proposal based on the evaluation. For example, the suggestion unit makes detailed proposals for important accommodations and standard proposals for general restaurants. It can also make simple proposals for simple tourist destinations. For example, when a user inputs "I want to find a luxury hotel," the suggestion unit makes a detailed proposal and provides the necessary information. On the other hand, when a user inputs "I want to find a place for lunch," the suggestion unit makes a standard proposal and suggests an appropriate restaurant. This allows the suggestion unit to adjust the level of detail of the proposal depending on the importance of the accommodation, restaurant, and tourist destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the accommodation, restaurant, and tourist destination into an AI model and select the optimal suggestion method.

[0101] The suggestion unit can apply different proposal algorithms according to the categories of accommodations, restaurants, and tourist attractions at the time of proposal. The suggestion unit, for example, classifies categories of accommodations, restaurants, and tourist attractions, and applies different proposal algorithms accordingly. For example, the suggestion unit applies a proposal algorithm dedicated to luxury accommodations to luxury accommodations, and applies a proposal algorithm dedicated to general restaurants to general restaurants. Also, the suggestion unit can apply a proposal algorithm dedicated to tourist attractions to tourist attractions. For example, when a user inputs "I want to find a luxury hotel," the suggestion unit applies a proposal algorithm dedicated to luxury accommodations to suggest an optimal luxury hotel. On the other hand, when a user inputs "I want to find a casual restaurant," the suggestion unit applies a proposal algorithm dedicated to general restaurants to suggest an appropriate restaurant. This allows the suggestion unit to apply an appropriate proposal algorithm according to the categories of accommodations, restaurants, and tourist attractions. 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 category data of accommodations, restaurants, and tourist attractions into an AI model and select an optimal proposal algorithm.

[0102] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The suggestion unit can also estimate the user's emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate. This allows the suggestion unit to adjust the length of the proposal 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 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 a user's emotion data to the generation AI and cause the generation AI to adjust the length of suggestions based on the emotion.

[0103] The suggestion unit can determine the priority of the proposal based on the submission time of the accommodation, restaurant, and tourist attraction at the time of proposal. The suggestion unit, for example, evaluates the submission time of the accommodation, restaurant, and tourist attraction, and determines the priority of the proposal based on the evaluation. For example, the suggestion unit makes a proposal with priority for an emergency accommodation, and makes a proposal with standard priority for a general restaurant. Also, the suggestion unit can make a proposal later for a tourist attraction with low importance. For example, when the user inputs "I want to find a place to stay right now," the suggestion unit makes a proposal with priority and quickly provides a place to stay. On the other hand, when the user inputs "I want to find a tourist attraction that I want to check later," the suggestion unit makes a proposal with standard priority. This allows the suggestion unit to determine the priority of the proposal based on the submission time of the accommodation, restaurant, and tourist attraction. Part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the submission time data of the accommodation, restaurant, and tourist attraction into an AI model and select the optimal suggestion method.

[0104] The suggestion unit can adjust the order of suggestions based on the relevance of the accommodations, restaurants, and tourist attractions at the time of suggestion. The suggestion unit, for example, evaluates the relevance of the accommodations, restaurants, and tourist attractions, and adjusts the order of suggestions based on the evaluation. For example, the suggestion unit makes suggestions preferentially to accommodations with high relevance, and makes suggestions in a standard order to restaurants with general relevance. Also, the suggestion unit can make suggestions later for tourist attractions with low relevance. For example, when a user inputs "I want to find accommodations for my travel destination," the suggestion unit makes suggestions preferentially to accommodations with high relevance. On the other hand, when a user inputs "I want to find general tourist attractions," the suggestion unit makes suggestions in a standard order. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the accommodations, restaurants, and tourist attractions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of accommodations, restaurants, and tourist attractions into an AI model and select an optimal suggestion method.

[0105] The reservation unit can estimate the user's emotion and adjust the reservation method based on the estimated user's emotion. The reservation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reservation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The reservation unit can also estimate the user's emotion using voice analysis technology. For example, the reservation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the reservation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reservation unit calculates an emotion score based on the fluctuation of the heart rate. This allows the reservation unit to adjust the reservation method 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 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 a user's emotion data into the generation AI and have the generation AI adjust the reservation method based on the emotion.

[0106] At the time of reservation, the reservation unit can select the optimal reservation method by analyzing the user's past reservation history. The reservation unit, for example, saves and analyzes the user's past reservation history as a text log. For example, the reservation unit preferentially suggests accommodations that the user has frequently used in the past. The reservation unit can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. For example, if the user has used online reservations a lot in the past, the reservation unit preferentially suggests online reservations. Furthermore, the reservation unit can predict and suggest accommodations to be used in a specific time period from the user's past reservation history. For example, the reservation unit suggests accommodations suitable for that time period based on accommodations that the user has used in a specific time period in the past. This allows the reservation unit to provide the optimal reservation method based on the user's past reservation history. Part or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input the user's reservation history data into an AI model to select the optimal reservation method.

[0107] The reservation unit can customize the reservation means based on the user's current situation at the time of reservation. The reservation unit, for example, obtains the user's current situation from location information and customizes the reservation means based on the location information. For example, when the user is traveling, the reservation unit preferentially suggests local reservations. In addition, when the user is at work, the reservation unit can also preferentially suggest work-related reservations. For example, when the user inputs "I want to reserve a conference room" while at work, the reservation unit preferentially suggests work-related reservations. Furthermore, when the user is participating in a specific event, the reservation unit can also preferentially suggest reservations related to the event. For example, when the user is participating in a music festival, the reservation unit preferentially suggests reservations for accommodations and restaurants related to the event. This allows the reservation unit to customize the reservation means according to the user's current situation. Some 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 location information and current situation data into an AI model to select the optimal reservation means.

[0108] The reservation unit can estimate the user's emotion and determine the priority of reservations based on the estimated user's emotion. The reservation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reservation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The reservation unit can also estimate the user's emotion using voice analysis technology. For example, the reservation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the reservation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reservation unit calculates an emotion score based on the fluctuation of the heart rate. This allows the reservation unit to determine the priority of reservations 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 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 a user's emotion data into the generation AI and have the generation AI determine reservation priorities based on emotions.

[0109] The reservation unit can select the optimal reservation method in consideration of the geographical location information of the user at the time of reservation. The reservation unit, for example, uses GPS data to acquire the geographical location information of the user and selects the optimal reservation method based on the information. For example, when the user is in a specific city, the reservation unit preferentially suggests accommodations related to the city. The reservation unit can also acquire the geographical location information of the user using Wi-Fi location information and select the optimal reservation method based on the information. For example, when the user is traveling, the reservation unit preferentially suggests accommodations related to the travel destination. Furthermore, when the user is in a specific facility, the reservation unit can also preferentially suggest accommodations related to the facility. For example, when the user is in a specific event venue, the reservation unit preferentially suggests accommodations related to the event. This allows the reservation unit to provide the optimal reservation method based on the geographical location 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 geographical location information of the user into an AI model and select the optimal reservation method.

[0110] The reservation unit can analyze the user's social media activity at the time of reservation and suggest a means of reservation. The reservation unit, for example, analyzes the user's social media activity and suggests a means of reservation based on the analysis. For example, the reservation unit preferentially suggests related accommodations based on information shared by the user on social media. The reservation unit can also preferentially suggest related accommodations based on information on accounts the user follows on social media. For example, the reservation unit suggests accommodations at travel destinations based on information on travel-related accounts the user follows. Furthermore, the reservation unit can also preferentially suggest related accommodations based on information on events the user is participating in on social media. For example, the reservation unit suggests accommodations related to the events based on information on music festivals the user is participating in. This allows the reservation unit to provide an optimal reservation means based on the user's social media activity. Some 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 social media activity data into an AI model and select the optimal reservation means.

[0111] The communication unit can estimate the user's emotion and adjust the method of contact based on the estimated user's emotion. The communication unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the communication unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The communication unit can also estimate the user's emotion using voice analysis technology. For example, the communication unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the communication unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the communication unit calculates an emotion score based on the fluctuation of the heart rate. This allows the communication unit to adjust the method of contact 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 communication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the communication unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the communication method based on the emotion.

[0112] The contact unit can select the optimal contact method by analyzing the user's past contact history at the time of contact. The contact unit, for example, saves and analyzes the user's past contact history as a text log. For example, the contact unit preferentially suggests a contact method (email, messenger, etc.) that the user has frequently used in the past. The contact unit can also preferentially suggest a contact method with a person with whom the user has contacted in the past. For example, the contact unit suggests contact by email again to a person with whom the user has contacted in the past by email. Furthermore, the contact unit can predict and suggest a contact method to be used in a specific time period from the user's past contact history. For example, if the user has used a messenger in a specific time period in the past, the contact unit suggests contact by messenger in that time period. This allows the contact unit to provide the optimal contact method based on the user's past contact history. A part or all of the above-mentioned processing in the contact unit may be performed using, for example, AI, or may be performed without using AI. For example, the contact unit can input the user's contact history data into an AI model and select the optimal contact method.

[0113] The communication unit can estimate the user's emotion and determine the priority of the communication based on the estimated user's emotion. The communication unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the communication unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The communication unit can also estimate the user's emotion using voice analysis technology. For example, the communication unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the communication unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the communication unit calculates an emotion score based on the fluctuation of the heart rate. This allows the communication unit to determine the priority of the communication 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 communication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the communication unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of communication based on the emotion.

[0114] The contact unit can select the optimal contact method in consideration of the geographical location information of the user at the time of contact. The contact unit, for example, uses GPS data to acquire the geographical location information of the user and selects the optimal contact method based on the acquired information. For example, when the user is in a specific city, the contact unit preferentially proposes a contact method related to the city. The contact unit can also acquire the geographical location information of the user using Wi-Fi location information and select the optimal contact method based on the acquired information. For example, when the user is traveling, the contact unit preferentially proposes a contact method related to the travel destination. Furthermore, when the user is in a specific facility, the contact unit can also preferentially propose a contact method related to the facility. For example, when the user is in a specific event venue, the contact unit preferentially proposes a contact method related to the event. This allows the contact unit to provide the optimal contact method based on the geographical location information of the user. Part or all of the above-mentioned processing in the contact unit may be performed, for example, using AI, or may be performed without using AI. For example, the contact unit can input the geographical location information of the user into an AI model and select the optimal contact method.

[0115] The navigation unit can estimate the user's emotion and adjust the navigation method based on the estimated user's emotion. The navigation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the navigation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The navigation unit can also estimate the user's emotion using a voice analysis technology. For example, the navigation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the navigation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on the fluctuation of the heart rate. This allows the navigation unit to adjust the navigation method 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 navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the navigation method based on the emotion.

[0116] During navigation, the navigation unit can select an optimal navigation method by analyzing the user's past movement history. The navigation unit, for example, saves and analyzes the user's past movement history as a text log. For example, the navigation unit proposes an optimal navigation method based on the route used by the user in the past. The navigation unit can also propose a navigation method that avoids congestion from the user's past movement history. For example, the navigation unit proposes an optimal route based on the route used by the user in the past to avoid congestion. Furthermore, the navigation unit can analyze the user's past movement history and propose the most efficient navigation method. For example, the navigation unit proposes an optimal navigation method based on the shortest route used by the user in the past. This allows the navigation unit to provide an optimal navigation method based on the user's past movement history. A part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's movement history data into an AI model and select an optimal navigation method.

[0117] The navigation unit can perform navigation by updating the user's current location information in real time during navigation. The navigation unit can update the user's current location information in real time using, for example, GPS data, and perform navigation based on the updated location. For example, the navigation unit can update the user's current location in real time while the user is moving and propose an optimal route. The navigation unit can also update the current location in real time and propose an optimal route as the user approaches the destination. For example, the navigation unit can obtain traffic information in real time as the user approaches the destination and propose an optimal route. Furthermore, if the user gets lost, the navigation unit can update the current location in real time and perform navigation again. For example, if the user gets lost, the navigation unit can update the current location in real time and re-propose the optimal route. This allows the navigation unit to perform navigation in real time based on the user's current location information. Part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's current location information into an AI model and select an optimal navigation method.

[0118] The navigation unit can estimate the user's emotion and adjust the voice guidance of the navigation based on the estimated user's emotion. The navigation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the navigation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The navigation unit can also estimate the user's emotion using a voice analysis technology. For example, the navigation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the navigation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on the fluctuation of the heart rate. This allows the navigation unit to adjust the voice guidance of the navigation 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 navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input the user's emotional data into the generation AI and have the generation AI adjust the voice guidance based on the emotion.

[0119] The navigation unit can select the optimal navigation method in consideration of the geographical location information of the user during navigation. The navigation unit, for example, uses GPS data to acquire the geographical location information of the user and selects the optimal navigation method based on the acquired information. For example, when the user is in a specific city, the navigation unit preferentially proposes a navigation method related to the city. The navigation unit can also acquire the geographical location information of the user using Wi-Fi location information and select the optimal navigation method based on the acquired information. For example, when the user is traveling, the navigation unit preferentially proposes a navigation method related to the travel destination. Furthermore, when the user is in a specific facility, the navigation unit can also preferentially propose a navigation method related to the facility. For example, when the user is in a specific event venue, the navigation unit preferentially proposes a navigation method related to the event. This allows the navigation unit to provide the optimal navigation method based on the geographical location information of the user. Part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the geographical location information of the user into an AI model and select the optimal navigation method.

[0120] The navigation unit can analyze the user's social media activities and suggest a navigation means during navigation. The navigation unit, for example, analyzes the user's social media activities and suggests a navigation means based on the results. For example, the navigation unit preferentially suggests a related navigation method based on information shared by the user on social media. The navigation unit can also preferentially suggest a related navigation method based on information on accounts the user follows on social media. For example, the navigation unit suggests a navigation method for a travel destination based on information on travel-related accounts the user follows. Furthermore, the navigation unit can also preferentially suggest a related navigation method based on information on an event the user is participating in on social media. For example, the navigation unit suggests a navigation method related to the event based on information on a music festival the user is participating in. This allows the navigation unit to provide an optimal navigation means based on the user's social media activities. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's social media activity data into an AI model to select an optimal navigation means.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The reception unit can analyze the user's past input history and automatically display phrases and keywords that the user frequently uses as candidates. For example, if the user frequently inputs "I want to travel with friends" in the past, the reception unit displays the phrase as a candidate. The reception unit can also preferentially suggest an input method (such as voice or text) that the user used in the past. For example, if the user used voice input a lot in the past, the reception unit preferentially suggests voice input. Furthermore, the reception unit can predict and suggest a phrase to be used in a specific time period from the user's past input history. For example, if the user inputs "good morning" in a specific time period in the past, the reception unit displays "good morning" as a candidate for that time period. This allows the reception unit to provide an optimal reception method based on the user's past input history.

[0123] The suggestion unit can analyze the user's past selection history and give priority to suggesting accommodations, restaurants, and tourist spots that the user prefers. For example, if the user has frequently selected luxury hotels in the past, the suggestion unit will give priority to suggesting luxury hotels. In addition, the suggestion unit can also suggest similar restaurants based on the types of restaurants (Japanese, Italian, etc.) that the user has selected in the past. Furthermore, the suggestion unit can analyze the characteristics (natural scenery, historical buildings, etc.) of tourist spots that the user has visited in the past and suggest tourist spots with similar characteristics. This allows the suggestion unit to make more personalized suggestions based on the user's past selection history.

[0124] The analysis unit can automatically complete related information based on the user's input. For example, if the user inputs "I want to travel with friends," the analysis unit automatically completes related information such as "travel destination," "lodging," and "restaurant." In addition, if the user inputs "I want to find a good restaurant," the analysis unit can complete information such as "location of the restaurant," "type of cuisine," and "budget." Furthermore, if the user inputs "I want to visit a tourist spot," the analysis unit can complete information such as "characteristics of the tourist spot," "access method," and "stay time." This allows the analysis unit to provide more detailed information based on the user's input.

[0125] The reservation unit can analyze the user's past reservation history and give priority to suggesting the reservation method preferred by the user. For example, if the user has made many online reservations in the past, the reservation unit can give priority to suggesting online reservations. Also, if the user has made many telephone reservations in the past, the reservation unit can give priority to suggesting telephone reservations. Furthermore, if the user has made reservations during a specific time period in the past, the reservation unit can also suggest a reservation method suitable for that time period. For example, if the user has made reservations at night in the past, the reservation unit can suggest a reservation method suitable for nighttime. In this way, the reservation unit can provide the optimal reservation method based on the user's past reservation history.

[0126] The contact unit can analyze the user's past contact history and preferentially suggest the contact method that the user prefers. For example, if the user used email a lot in the past, the contact unit preferentially suggests email. Also, if the user used messenger apps a lot in the past, the contact unit can preferentially suggest messenger apps. Furthermore, if the user has contacted during a specific time period in the past, the contact unit can suggest a contact method suitable for that time period. For example, if the user has contacted in the morning in the past, the contact unit can suggest a contact method suitable for the morning. In this way, the contact unit can provide the optimal contact method based on the user's past contact history.

[0127] The navigation unit can analyze the user's past movement history and preferentially suggest a movement route that the user prefers. For example, the navigation unit can suggest an optimal navigation method based on routes that the user has used in the past. The navigation unit can also suggest an optimal route based on routes that the user has used in the past to avoid congestion. Furthermore, the navigation unit can also suggest an optimal navigation method based on the shortest route that the user has used in the past. In this way, the navigation unit can provide an optimal navigation method based on the user's past movement history.

[0128] The reception unit can estimate the user's emotion and adjust the timing of accepting natural language input based on the estimated emotion of the user. For example, when the user is stressed, the reception unit can delay the timing of accepting input. When the user is relaxed, the reception unit can advance the timing of accepting input. Furthermore, when the user is excited, the reception unit can adjust the timing of accepting input and wait until the user calms down. This allows the reception unit to adjust the timing of accepting natural language input according to the emotion of the user.

[0129] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, if the user feels anxious, the analysis unit can provide the analysis result using gentle expressions. If the user is excited, the analysis unit can provide the analysis result using concise expressions. If the user is relaxed, the analysis unit can provide the analysis result using detailed expressions. This allows the analysis unit to adjust the expression method of the analysis according to the user's emotion.

[0130] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is tired, the suggestion unit can make the suggestion using a concise expression. When the user is excited, the suggestion unit can make the suggestion using a detailed expression. When the user is relaxed, the suggestion unit can make the suggestion using a soft expression. This allows the suggestion unit to adjust the way of expressing the suggestion according to the emotion of the user.

[0131] The reservation unit can estimate the user's emotion and adjust the reservation method based on the estimated user's emotion. For example, if the user is stressed, the reservation unit can suggest a simple reservation method. Also, if the user is relaxed, the reservation unit can suggest a detailed reservation method. Furthermore, if the user is excited, the reservation unit can suggest a quick reservation method. In this way, the reservation unit can adjust the reservation method according to the user's emotion.

[0132] The process flow of the second embodiment will be briefly described below.

[0133] Step 1: The reception unit receives a natural language input from the user. The natural language input from the user includes, but is not limited to, a text input or a voice input. The reception unit can receive the natural language input from the user through, for example, a messenger app. Step 2: The analysis unit uses the generation AI to analyze the natural language input received by the reception unit and understand the user's intention. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's natural language input and understand the user's intention. Step 3: The suggestion unit suggests and compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit. The suggestion unit, for example, collects information on accommodations, restaurants, and tourist spots and suggests them to the user. The suggestion unit can collect information by, for example, web scraping or using an API. Step 4: The reservation unit makes reservations for the lodgings, restaurants, and tourist spots proposed by the suggestion unit. The reservation unit can make reservations, for example, online or by phone. Step 5: The contact section communicates the information reserved by the reservation section to the friend. The contact section can communicate the information by, for example, email, a messaging app, or other methods. Step 6: The navigation unit navigates to the location based on the information transmitted by the communication unit. The navigation unit can provide route guidance by, for example, displaying a map or providing voice guidance.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements including the reception unit, analysis unit, proposal unit, reservation unit, contact unit, and navigation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts input of the user's natural language. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's intention using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on accommodations, restaurants, and tourist spots and proposes them to the user. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes reservations for the proposed facilities. The contact unit communicates reservation information to friends via the communication I / F 44 of the smart device 14. The navigation unit navigates to the site using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the above example, and various changes are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements including the reception unit, analysis unit, proposal unit, reservation unit, contact unit, and navigation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts input of the user's natural language. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's intention using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on accommodations, restaurants, and tourist spots and proposes them to the user. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes reservations for the proposed facilities. The contact unit communicates the reservation information to a friend via the communication I / F 44 of the smart glasses 214. The navigation unit navigates to the local location using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, reservation unit, contact unit, and navigation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and accepts input of the user's natural language. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's intention using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on accommodations, restaurants, and tourist spots and proposes them to the user. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes reservations for the proposed facilities. The contact unit communicates reservation information to friends via the communication I / F 44 of the headset type terminal 314. The navigation unit navigates to the local area using the display 343 of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements including the reception unit, analysis unit, proposal unit, reservation unit, contact unit, and navigation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts input of the user's natural language. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's intention using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and collects information on accommodations, restaurants, and tourist spots and proposes them to the user. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes reservations for the proposed facilities. The contact unit communicates reservation information to friends via the communication I / F 44 of the robot 414. The navigation unit navigates to the site using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0187] 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.

[0188] 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.

[0189] These emotions are distributed in the 3 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Appendix 1) a reception unit for receiving an input of a natural language of a user; an analysis unit that analyzes the natural language input received by the reception unit and understands the user's intention; A suggestion unit that suggests or compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit; a reservation unit for making reservations for accommodations, restaurants, and tourist spots proposed by the suggestion unit; a communication unit for communicating information reserved by the reservation unit to a friend; a navigation unit that performs navigation to the location based on the information communicated by the communication unit. A system characterized by: (Appendix 2) The suggestion unit, Collect information on accommodations, restaurants, and tourist spots and make suggestions to users 2. The system of claim 1, (Appendix 3) The analysis unit is Generative AI analyzes the user's natural language input and understands their intent 2. The system of claim 1, (Appendix 4) The reservation unit: Make reservations for accommodation, restaurants, and sightseeing spots selected by the user 2. The system of claim 1, (Appendix 5) The communication unit includes: Inform a friend of your travel dates or accommodation information 2. The system of claim 1, (Appendix 6) The navigation unit includes: Provide directions to help users reach their travel destination 2. The system of claim 1, (Appendix 7) The navigation unit includes: Support sharing local information or photos 2. The system of claim 1, (Appendix 8) The reception unit is Estimating user emotions and adjusting timing for accepting natural language input based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past input history and select the reception method 2. The system of claim 1, (Appendix 10) The reception unit is Accepting natural language input and filtering it based on the user's current context and interests 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting natural language input, the system prioritizes relevant input by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 13) The reception unit is When accepting natural language input, analyze the user's social media activity and accept related input. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail in the analysis based on the importance of the input 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, apply different parsing algorithms depending on the category of the input 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when the inputs were submitted 2. The system of claim 1, (Appendix 19) The analysis unit is At parse time, adjust the order of parsing based on the relevance of the inputs 2. The system of claim 1, (Appendix 20) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, adjust the level of detail of the suggestions based on the importance of the accommodation, dining, or tourist destination 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of accommodation, restaurant, or tourist attraction. 2. The system of claim 1, (Appendix 23) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 24) The suggestion unit, When submitting proposals, we will prioritize the proposals based on when they were submitted for accommodation, dining, and attractions. 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, the order of suggestions is adjusted based on the relevance of places to stay, eat, and visit 2. The system of claim 1, (Appendix 26) The reservation unit: To estimate a user's emotion and adjust a reservation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The reservation unit: When making a reservation, analyze the user's past reservation history to select the optimal reservation method 2. The system of claim 1, (Appendix 28) The reservation unit: When booking, customize the booking experience based on the user's current situation 2. The system of claim 1, (Appendix 29) The reservation unit: To estimate a user's emotion and prioritize reservations based on the estimated user's emotion. 2. The system of claim 1, (Appendix 30) The reservation unit: When making a reservation, consider the user's geographic location to determine the best way to make the reservation 2. The system of claim 1, (Appendix 31) The reservation unit: When booking, analyze users' social media activity to suggest options for booking 2. The system of claim 1, (Appendix 32) The communication unit includes: To estimate a user's emotions and adjust a contact method based on the estimated user's emotions 2. The system of claim 1, (Appendix 33) The communication unit includes: When contacting a user, analyze the user's past contact history to determine the most appropriate contact method. 2. The system of claim 1, (Appendix 34) The communication unit includes: To estimate a user's emotion and determine a priority of contacts based on the estimated user's emotion. 2. The system of claim 1, (Appendix 35) The communication unit includes: When contacting someone, consider their geographic location to find the best way to contact them 2. The system of claim 1, (Appendix 36) The navigation unit includes: Inferring a user's emotions and adjusting a navigation method based on the estimated user's emotions 2. The system of claim 1, (Appendix 37) The navigation unit includes: When navigating, the system analyzes the user's past navigation history to select the optimal navigation method. 2. The system of claim 1, (Appendix 38) The navigation unit includes: When navigating, the user's current location is updated in real time to guide the user. 2. The system of claim 1, (Appendix 39) The navigation unit includes: To estimate a user's emotion and adjust voice guidance of navigation based on the estimated user's emotion. 2. The system of claim 1, (Appendix 40) The navigation unit includes: When navigating, consider the user's geographic location to choose the best navigation method 2. The system of claim 1, (Appendix 41) The navigation unit includes: When navigating, analyze the user's social media activity to suggest navigation options 2. The system of claim 1, [Explanation of symbols]

[0206] 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 for receiving an input of a natural language of a user; an analysis unit that analyzes the natural language input received by the reception unit and understands the user's intention; A suggestion unit that suggests or compares accommodations, restaurants, and tourist spots based on the user's intention understood by the analysis unit; a reservation unit for making reservations for accommodations, restaurants, and tourist spots proposed by the suggestion unit; a communication unit for communicating information reserved by the reservation unit to a friend; a navigation unit that performs navigation to the location based on the information communicated by the communication unit. A system characterized by:

2. The suggestion unit, Collect information on accommodations, restaurants, and tourist spots and make suggestions to users 2. The system of claim 1 .

3. The analysis unit is Generative AI analyzes the user's natural language input and understands their intent 2. The system of claim 1 .

4. The reservation unit: Make reservations for accommodation, restaurants, and sightseeing spots selected by the user 2. The system of claim 1 .

5. The communication unit includes: Inform a friend of your travel dates or accommodation information 2. The system of claim 1 .

6. The navigation unit includes: Provide directions to help users reach their travel destination 2. The system of claim 1 .

7. The navigation unit includes: Support sharing local information or photos 2. The system of claim 1 .

8. The reception unit is A system that estimates a user's emotions and adjusts the timing of accepting natural language input based on the estimated user's emotions 2. The system of claim 1 .

9. The reception unit is Analyze the user's past input history and select the reception method 2. The system of claim 1 .

10. The reception unit is Accepting natural language input and filtering it based on the user's current context and interests 2. The system of claim 1 .

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