System

Abstract

An object of a system according to an embodiment is to propose an optimal travel plan on the basis of desired conditions of a travel and to efficiently perform a reservation procedure.SOLUTION: A system according to an embodiment includes a reception unit, a proposal unit, and a reservation unit. The reception part inputs desired conditions of travel. The proposal unit analyzes the condition input by the reception unit and proposes a travel plan. The reservation section performs a reservation procedure based on the travel plan proposed by the proposal section.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of proposing optimal travel plans based on desired travel conditions and carrying out reservation procedures, which are complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to propose an optimal travel plan based on desired travel conditions and to efficiently carry out reservation procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a reservation unit. The reception unit inputs desired travel conditions. The proposal unit analyzes the conditions input by the reception unit and proposes a travel plan. The reservation unit performs reservation procedures based on the travel plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal travel plan based on desired travel conditions and can efficiently carry out reservation procedures. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A travel suggestion system according to an embodiment of the present invention is a system in which a user inputs desired travel conditions, and a generation AI analyzes the data to propose an optimal travel plan and process the reservation. In the travel suggestion system, a user inputs desired travel conditions, and a generation AI analyzes the data to propose an optimal travel plan. This proposal includes travel destinations, accommodations, tourist attractions, and places to eat. Furthermore, the travel suggestion system can automatically process reservations based on the proposed travel plan. For example, a user inputs desired travel conditions. For example, detailed conditions such as the travel destination, budget, travel duration, and activities of interest are input. This information is input into the generation AI. The travel suggestion system then analyzes the input conditions using the generation AI. The generation AI then references a database of travel destinations to identify the travel destination that best meets the user's requirements. For example, resorts and tourist spots are suggested as places where users can relax at a beach resort. The system also suggests optimal accommodations and tourist attractions based on the budget and travel duration. The travel suggestion system can also automatically process reservations based on the proposed travel plan. For example, airline ticket reservations, hotel reservations, and sightseeing tour reservations can be made all at once. This allows the user to prepare for a trip without any hassle. As a result, the travel suggestion system allows the user to obtain the optimal travel plan without any hassle. For example, even busy businessmen or people who are not good at planning trips can easily prepare for a trip. Furthermore, the plan proposed by the generation AI is best suited to the user's conditions, making it possible to achieve a highly satisfying trip. As a result, the travel suggestion system allows the user to obtain the optimal travel plan without any hassle. For example, even busy businessmen or people who are not good at planning trips can easily prepare for a trip. Furthermore, the plan proposed by the generation AI is best suited to the user's conditions, making it possible to achieve a highly satisfying trip.

[0029] A travel suggestion system according to an embodiment includes a reception unit, a suggestion unit, and a reservation unit. The reception unit allows a user to input desired travel conditions. The desired travel conditions include, but are not limited to, budget, travel destination, travel period, and activities of interest. The reception unit provides an interface for the user to input conditions in text format. The reception unit can also input conditions using voice input. For example, the desired conditions are automatically set when the user simply inputs, "I want to relax at a beach resort." The reception unit can also suggest travel destinations based on images uploaded by the user. The suggestion unit uses a generation AI to analyze the conditions input by the reception unit and suggest an optimal travel plan. The suggestion unit, for example, references a database of travel destinations to identify travel destinations that best meet the user's conditions. The travel destination database includes information such as resorts, tourist spots, accommodations, tourist attractions, and dining options. The suggestion unit uses the generation AI to generate an optimal travel plan based on the user's conditions. For example, the generation AI suggests optimal accommodations and tourist attractions based on the user's budget and travel period. The suggestion unit can also customize the travel plan based on the user's activities of interest. For example, if the user is interested in snorkeling, the generation AI can suggest travel destinations where snorkeling is enjoyable. The reservation unit performs reservation procedures based on the travel plan proposed by the suggestion unit. The reservation unit can, for example, make airline ticket reservations, hotel reservations, and sightseeing tour reservations all at once. The reservation unit uses the generation AI to perform optimal reservation procedures based on the user's requirements. For example, the generation AI can suggest optimal reservation procedures based on the user's budget and travel period. This allows the travel suggestion system according to the embodiment to easily obtain the optimal travel plan for the user. Some or all of the above-described 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 perform reservation procedures using an AI model that inputs the travel plan proposed by the suggestion unit and outputs a reservation procedure.

[0030] The suggestion unit can refer to a database of travel destinations and identify a travel destination that best meets the user's requirements. The travel destination database includes, but is not limited to, information on resorts, tourist spots, accommodations, tourist attractions, and dining options. The suggestion unit can refer to the database of travel destinations and identify a travel destination that best meets the user's requirements. For example, the suggestion unit can suggest resorts and tourist spots as beach resorts where people can relax. The suggestion unit can also suggest optimal accommodations and tourist attractions based on the user's budget and travel period. For example, the suggestion unit can suggest optimal accommodations for a three-night, four-day trip with a budget of 100,000 yen or less. This allows the travel destination that best meets the user's requirements to be identified. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input the database of travel destinations into a generation AI and cause the generation AI to identify a travel destination that best meets the user's requirements.

[0031] The suggestion unit can suggest accommodations and tourist spots based on the budget and travel period. The suggestion unit, for example, suggests optimal accommodations and tourist spots based on the user's budget and travel period. For example, the suggestion unit suggests optimal accommodations for a three-night, four-day trip with a budget of 100,000 yen or less. Furthermore, if the travel period is one week, the suggestion unit can also suggest tourist spots that can be enjoyed in one week. Furthermore, if the budget is limited, the suggestion unit can suggest accommodations with high cost performance. For example, the suggestion unit suggests hotels where guests can stay with a budget of 50,000 yen or less. This makes it possible to suggest optimal accommodations and tourist spots based on the budget and travel period. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's budget and travel period into the generation AI and cause the generation AI to suggest optimal accommodations and tourist spots.

[0032] The reservation unit can make reservations for airline tickets, hotels, and sightseeing tours all at once. The reservation unit can make reservations for airline tickets, hotels, and sightseeing tours all at once, for example. For example, the reservation unit makes airline ticket reservations based on a travel plan selected by the user. The reservation unit can also make reservations for accommodations selected by the user. The reservation unit can also make reservations for sightseeing tours selected by the user. For example, the reservation unit confirms the details of the sightseeing tour selected by the user and performs the reservation procedure. This allows the reservation procedure to be performed all at once. 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 data of the travel plan selected by the user into the generation AI and have the generation AI perform the reservation procedure for airline tickets, hotels, and sightseeing tours.

[0033] The suggestion unit can customize a travel plan based on activities of interest to the user. The suggestion unit customizes a travel plan based on activities of interest to the user, for example. For example, if the user is interested in snorkeling, the suggestion unit can suggest travel destinations where snorkeling is enjoyed. Furthermore, if the user is interested in historical sites, the suggestion unit can suggest travel plans that visit historical sites. Furthermore, if the user is interested in outdoor activities, the suggestion unit can suggest travel destinations where outdoor activities are enjoyed. For example, if the user is interested in hiking, the suggestion unit can suggest travel destinations where hiking is enjoyed. This makes it possible to provide a customized travel plan based on the user's interests. Some or all of the above-described 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 data on activities of interest to the user into the generation AI and cause the generation AI to propose a customized travel plan.

[0034] The suggestion unit can generate a travel plan by referencing a database of travel destinations based on the user's conditions. The suggestion unit can, for example, generate a travel plan by referencing a database of travel destinations based on the user's conditions. The travel plan can include, but is not limited to, details such as itineraries, destinations to visit, and activities. The suggestion unit can also customize the travel plan based on the user's activities of interest. For example, if the user is interested in snorkeling, the suggestion unit can generate a travel plan that allows the user to enjoy snorkeling. If the user is interested in historical sites, the suggestion unit can also generate a travel plan that visits historical sites. This allows the generation of an optimal travel plan based on the user's conditions. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data on the user's conditions into a generation AI and cause the generation AI to generate an optimal travel plan.

[0035] The reception unit can analyze the user's past travel history and suggest an input format. For example, the reception unit can display related travel destinations as candidates based on travel destinations the user has visited in the past. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. For example, if the user visited a beach resort in the past summer, the reception unit can suggest a beach resort for the next summer as well. This makes it possible to provide an optimal input format based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data of the user's past travel history into a generation AI and have the generation AI suggest an optimal input format.

[0036] When inputting desired travel conditions, the reception unit can customize input items based on the user's current living situation and areas of interest. For example, the reception unit can suggest related travel destinations based on places the user has shown interest in in recent social media activities. The reception unit can also suggest relaxing travel destinations taking into account the user's current work or academic situation. The reception unit can also suggest travel destinations where specific activities can be enjoyed based on the user's hobbies and areas of interest. For example, if the user is interested in hiking, the reception unit can suggest travel destinations where hiking is enjoyed. This allows customized input items to be provided based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's current living situation and areas of interest into the generation AI and cause the generation AI to suggest customized input items.

[0037] The reception unit can select an input means according to the user's input method when inputting desired travel conditions. For example, the reception unit can automatically set the desired conditions when the user simply inputs, for example, "I want to relax at a beach resort" by voice. The reception unit can also customize and display input fields when the user inputs detailed conditions in text. The reception unit can also suggest travel destinations based on images uploaded by the user. For example, if the user uploads an image of a beach, a beach resort can be suggested. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described 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 data on the user's input method into a generation AI and have the generation AI select the optimal input means.

[0038] When inputting desired travel conditions, the reception unit can prioritize input of conditions taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. Furthermore, if the user is interested in a specific area, the reception unit can also prioritize input of conditions related to that area. Furthermore, the reception unit can prioritize input of related conditions based on areas the user has visited in the past. For example, travel destinations related to areas the user has visited in the past can be suggested. This makes it possible to provide highly relevant conditions based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data of the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant conditions.

[0039] When the user inputs desired travel conditions, the reception unit can analyze the user's social media activity and suggest related conditions. For example, the reception unit can suggest related travel destinations based on the locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. The reception unit can also suggest related conditions by referring to the activities of the user's friends on social media. For example, related travel destinations can be suggested based on the locations where the user has checked in on social media. This makes it possible to provide related conditions based on the user's social media activity. Some or all of the above-described 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 data on the user's social media activity into a generation AI and have the generation AI suggest related conditions.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired travel conditions. The reception unit, for example, customizes the input interface based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal input means. For example, the input interface is customized based on feedback provided by the user in the past. This makes it possible to provide a customized input method based on the user's past feedback. Some 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 past feedback data into a generation AI and have the generation AI customize the input method.

[0041] When making a suggestion, the suggestion unit can adjust the content of the suggestion based on the importance of the travel destination. For example, the suggestion unit provides detailed information about major tourist destinations. The suggestion unit can also provide concise information about lesser tourist destinations. The suggestion unit can also prioritize suggesting tourist destinations with high importance according to the user's interests. For example, the suggestion unit provides detailed information about major tourist destinations. This makes it possible to provide an optimal level of detail in the suggestion based on the importance of the travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data about the importance of the travel destination into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0042] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the travel destination. For example, in the case of a beach resort, the suggestion unit can suggest relaxing activities. In the case of city tourism, the suggestion unit can also suggest major tourist spots. In the case of an adventure tour, the suggestion unit can also suggest active activities. For example, in the case of a beach resort, the suggestion unit can suggest relaxing activities. This makes it possible to provide an optimal suggestion algorithm depending on the category of the travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the category of the travel destination into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0043] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit can suggest a similar plan based on a travel plan selected by the user in the past. The suggestion unit can also prioritize proposals based on specific conditions from the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and make optimal suggestions. For example, the suggestion unit can suggest a similar plan based on a travel plan selected by the user in the past. This makes it possible to provide optimal suggestions based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposals.

[0044] When making a proposal, the suggestion unit can determine the order of proposals based on the submission date of the travel destination. The suggestion unit, for example, determines the priority of proposals based on the best season for the travel destination. The suggestion unit can also determine the priority of proposals based on the user's planned travel date. The suggestion unit can also determine the priority of proposals based on the timing of an event at the travel destination. For example, the suggestion unit determines the priority of proposals based on the best season for the travel destination. This makes it possible to provide an optimal priority of proposals based on the submission date of the travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the submission date of the travel destination into the generation AI and have the generation AI determine the order of proposals.

[0045] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the travel destinations. For example, the suggestion unit prioritizes suggesting highly relevant travel destinations based on the user's interests. The suggestion unit can also prioritize suggesting highly relevant travel destinations based on the user's past travel history. The suggestion unit can also prioritize suggesting highly relevant travel destinations based on the user's current living situation. For example, the suggestion unit prioritizes suggesting highly relevant travel destinations based on the user's interests. This makes it possible to provide an optimal suggestion order based on the relevance of the travel destinations. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the relevance of the travel destinations to the generation AI and cause the generation AI to adjust the suggestion order.

[0046] When making a suggestion, the suggestion unit can adjust the proposed terminology according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can make the suggestion in simple language. Furthermore, if the user is an experienced traveler, the suggestion unit can make a detailed suggestion using technical terms. Furthermore, the suggestion unit can customize the content of the suggestion according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can make the suggestion in simple language. This makes it possible to provide the optimal proposed technical terms according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the proposed terminology.

[0047] During the reservation procedure, the reservation unit can select a reservation method by analyzing the user's past reservation history. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. The reservation unit can also prioritize and suggest specific conditions based on the user's past reservation history. The reservation unit can also analyze the user's past reservation history and suggest the optimal reservation method. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. This makes it possible to provide the optimal reservation method based on the user's past reservation history. 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 data on the user's past reservation history into a generation AI and have the generation AI select the optimal reservation method.

[0048] The reservation unit can customize the reservation procedure based on the user's current lifestyle during the reservation process. For example, if the user is busy, the reservation unit can provide a simple reservation procedure. Furthermore, if the user is relaxed, the reservation unit can also provide a detailed reservation procedure. Furthermore, the reservation unit can suggest an optimal reservation procedure based on the user's current lifestyle. For example, if the user is busy, a simple reservation procedure can be provided. This makes it possible to provide an optimal reservation procedure based on the user's current lifestyle. Some or all of the above-described 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 data on the user's current lifestyle into the generation AI and have the generation AI customize the reservation procedure.

[0049] The reservation unit can improve the reservation procedure by reflecting user feedback during the reservation procedure. The reservation unit improves the reservation procedure, for example, based on feedback previously provided by the user. The reservation unit can also preferentially suggest specific reservation methods based on the user's past feedback. The reservation unit can also analyze the user's past feedback and suggest the optimal reservation procedure. For example, the reservation unit improves the reservation procedure based on feedback previously provided by the user. This makes it possible to provide the optimal reservation procedure based on the user's feedback. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input user feedback data into a generation AI and have the generation AI improve the reservation procedure.

[0050] The reservation unit can select the optimal reservation method during the reservation procedure, taking into account the user's geographical location information. For example, the reservation unit prioritizes suggesting accommodations close to the user's current location. Furthermore, if the user is interested in a particular area, the reservation unit can also suggest accommodations related to that area. Furthermore, the reservation unit can also suggest related accommodations based on areas the user has previously visited. For example, the reservation unit prioritizes suggesting accommodations close to the user's current location. This makes it possible to provide the optimal reservation method based on the user's geographical location information. Some or all of the above-described 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 geographical location information data into a generation AI and have the generation AI select the optimal reservation method.

[0051] During the reservation process, the reservation unit can analyze the user's social media activity and suggest a means for the reservation process. For example, the reservation unit can suggest relevant accommodations based on the location where the user checked in on social media. The reservation unit can also analyze the content of the user's social media posts and suggest relevant accommodations. The reservation unit can also suggest relevant accommodations based on the activity of the user's friends on social media. For example, relevant accommodations can be suggested based on the location where the user checked in on social media. This makes it possible to provide the 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, or without, AI. For example, the reservation unit can input data on the user's social media activity into a generation AI and have the generation AI suggest reservation means.

[0052] The reservation unit can customize the reservation procedure method by reflecting the user's past feedback during the reservation procedure. The reservation unit customizes the reservation procedure, for example, based on feedback provided by the user in the past. The reservation unit can also preferentially suggest specific reservation methods based on the user's past feedback. The reservation unit can also analyze the user's past feedback and suggest the optimal reservation procedure. For example, the reservation unit customizes the reservation procedure based on feedback provided by the user in the past. This makes it possible to provide the optimal reservation procedure based on the user's past feedback. 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 data of the user's past feedback into a generation AI and have the generation AI customize the reservation procedure.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The suggestion unit can analyze the user's past travel history and suggest new travel plans based on past travel destinations and activities. For example, it can suggest again travel destinations that the user has visited in the past and that the user was particularly satisfied with. It can also suggest travel plans that include activities that the user has particularly enjoyed in the past. Furthermore, it can analyze the user's preferences from the past travel history and suggest similar travel destinations and activities. This makes it possible to provide an optimal travel plan based on the user's past travel history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's past travel history into a generation AI and have the generation AI suggest an optimal travel plan.

[0055] The suggestion unit can customize a travel plan based on the user's current living situation and areas of interest. For example, the suggestion unit can suggest related travel destinations based on places the user has shown interest in in their recent social media activities. It can also suggest relaxing travel destinations taking into account the user's current work or academic situation. It can also suggest travel destinations where specific activities can be enjoyed based on the user's hobbies and areas of interest. This allows the system to provide an optimal travel plan based on the user's current living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's current living situation and areas of interest into the generation AI and have the generation AI suggest a customized travel plan.

[0056] The suggestion unit can adjust the content of the suggestion according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can focus on basic information. On the other hand, if the user is an experienced traveler, the suggestion unit can provide suggestions including detailed information and expert advice. Furthermore, the way the suggestion is presented can be customized according to the user's level of expertise. This makes it possible to provide optimal suggestions according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the content of the suggestion.

[0057] The suggestion unit can improve the content of the suggestion by reflecting the user's past feedback. For example, the suggestion unit can customize the content of the suggestion based on feedback provided by the user in the past. Also, the suggestion unit can prioritize suggestions for specific conditions based on the user's past feedback. Furthermore, the suggestion unit can analyze the user's past feedback and make optimal suggestions. This makes it possible to provide optimal suggestions based on the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data of the user's past feedback into the generation AI and cause the generation AI to improve the content of the suggestion.

[0058] The suggestion unit can suggest a travel plan taking into account the user's geographical location information. For example, it can prioritize suggesting travel destinations close to the user's current location. Also, if the user is interested in a particular area, it can suggest travel destinations related to that area. Furthermore, it can suggest related travel destinations based on areas the user has visited in the past. This makes it possible to provide an optimal travel plan based on the user's geographical location information. 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 user's geographical location information data into the generation AI and cause the generation AI to suggest an optimal travel plan.

[0059] The suggestion unit can analyze the user's social media activity and suggest related travel plans. For example, it can suggest related travel destinations based on the locations where the user has checked in on social media. It can also suggest related travel destinations by analyzing the content of the user's social media posts. It can also suggest related travel destinations based on the activities of the user's friends on social media. This makes it possible to provide an optimal travel plan based on the user's social media activity. 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 data on the user's social media activity into a generation AI and have the generation AI suggest related travel plans.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit allows the user to input desired travel conditions. Desired travel conditions include, for example, budget, travel destination, travel period, and activities of interest. The reception unit provides an interface for the user to input conditions in text format, and voice input and image upload are also possible. Step 2: The proposal unit analyzes the conditions entered by the reception unit and proposes the optimal travel plan. The proposal unit uses generation AI to refer to a database of travel destinations and identify the travel destinations, accommodations, and tourist spots that best suit the user's conditions. It also customizes the travel plan based on the user's activities of interest. Step 3: The reservation department completes the reservation process based on the travel plan proposed by the proposal department. The reservation department can use the generation AI to make reservations for airline tickets, hotels, and sightseeing tours all at once. It proposes the optimal reservation process based on the user's budget and travel period.

[0062] (Example 2) A travel suggestion system according to an embodiment of the present invention is a system in which a user inputs desired travel conditions, and a generation AI analyzes the data to propose an optimal travel plan and process the reservation. In the travel suggestion system, a user inputs desired travel conditions, and a generation AI analyzes the data to propose an optimal travel plan. This proposal includes travel destinations, accommodations, tourist attractions, and places to eat. Furthermore, the travel suggestion system can automatically process reservations based on the proposed travel plan. For example, a user inputs desired travel conditions. For example, detailed conditions such as the travel destination, budget, travel duration, and activities of interest are input. This information is input into the generation AI. The travel suggestion system then analyzes the input conditions using the generation AI. The generation AI then references a database of travel destinations to identify the travel destination that best meets the user's requirements. For example, resorts and tourist spots are suggested as places where users can relax at a beach resort. The system also suggests optimal accommodations and tourist attractions based on the budget and travel duration. The travel suggestion system can also automatically process reservations based on the proposed travel plan. For example, airline ticket reservations, hotel reservations, and sightseeing tour reservations can be made all at once. This allows the user to prepare for a trip without any hassle. As a result, the travel suggestion system allows the user to obtain the optimal travel plan without any hassle. For example, even busy businessmen or people who are not good at planning trips can easily prepare for a trip. Furthermore, the plan proposed by the generation AI is best suited to the user's conditions, making it possible to achieve a highly satisfying trip. As a result, the travel suggestion system allows the user to obtain the optimal travel plan without any hassle. For example, even busy businessmen or people who are not good at planning trips can easily prepare for a trip. Furthermore, the plan proposed by the generation AI is best suited to the user's conditions, making it possible to achieve a highly satisfying trip.

[0063] A travel suggestion system according to an embodiment includes a reception unit, a suggestion unit, and a reservation unit. The reception unit allows a user to input desired travel conditions. The desired travel conditions include, but are not limited to, budget, travel destination, travel period, and activities of interest. The reception unit provides an interface for the user to input conditions in text format. The reception unit can also input conditions using voice input. For example, the desired conditions are automatically set when the user simply inputs, "I want to relax at a beach resort." The reception unit can also suggest travel destinations based on images uploaded by the user. The suggestion unit uses a generation AI to analyze the conditions input by the reception unit and suggest an optimal travel plan. The suggestion unit, for example, references a database of travel destinations to identify travel destinations that best meet the user's conditions. The travel destination database includes information such as resorts, tourist spots, accommodations, tourist attractions, and dining options. The suggestion unit uses the generation AI to generate an optimal travel plan based on the user's conditions. For example, the generation AI suggests optimal accommodations and tourist attractions based on the user's budget and travel period. The suggestion unit can also customize the travel plan based on the user's activities of interest. For example, if the user is interested in snorkeling, the generation AI can suggest travel destinations where snorkeling is enjoyable. The reservation unit performs reservation procedures based on the travel plan proposed by the suggestion unit. The reservation unit can, for example, make airline ticket reservations, hotel reservations, and sightseeing tour reservations all at once. The reservation unit uses the generation AI to perform optimal reservation procedures based on the user's requirements. For example, the generation AI can suggest optimal reservation procedures based on the user's budget and travel period. This allows the travel suggestion system according to the embodiment to easily obtain the optimal travel plan for the user. Some or all of the above-described 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 perform reservation procedures using an AI model that inputs the travel plan proposed by the suggestion unit and outputs a reservation procedure.

[0064] The suggestion unit can refer to a database of travel destinations and identify a travel destination that best meets the user's requirements. The travel destination database includes, but is not limited to, information on resorts, tourist spots, accommodations, tourist attractions, and dining options. The suggestion unit can refer to the database of travel destinations and identify a travel destination that best meets the user's requirements. For example, the suggestion unit can suggest resorts and tourist spots as beach resorts where people can relax. The suggestion unit can also suggest optimal accommodations and tourist attractions based on the user's budget and travel period. For example, the suggestion unit can suggest optimal accommodations for a three-night, four-day trip with a budget of 100,000 yen or less. This allows the travel destination that best meets the user's requirements to be identified. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input the database of travel destinations into a generation AI and cause the generation AI to identify a travel destination that best meets the user's requirements.

[0065] The suggestion unit can suggest accommodations and tourist spots based on the budget and travel period. The suggestion unit, for example, suggests optimal accommodations and tourist spots based on the user's budget and travel period. For example, the suggestion unit suggests optimal accommodations for a three-night, four-day trip with a budget of 100,000 yen or less. Furthermore, if the travel period is one week, the suggestion unit can also suggest tourist spots that can be enjoyed in one week. Furthermore, if the budget is limited, the suggestion unit can suggest accommodations with high cost performance. For example, the suggestion unit suggests hotels where guests can stay with a budget of 50,000 yen or less. This makes it possible to suggest optimal accommodations and tourist spots based on the budget and travel period. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's budget and travel period into the generation AI and cause the generation AI to suggest optimal accommodations and tourist spots.

[0066] The reservation unit can make reservations for airline tickets, hotels, and sightseeing tours all at once. The reservation unit can make reservations for airline tickets, hotels, and sightseeing tours all at once, for example. For example, the reservation unit makes airline ticket reservations based on a travel plan selected by the user. The reservation unit can also make reservations for accommodations selected by the user. The reservation unit can also make reservations for sightseeing tours selected by the user. For example, the reservation unit confirms the details of the sightseeing tour selected by the user and performs the reservation procedure. This allows the reservation procedure to be performed all at once. 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 data of the travel plan selected by the user into the generation AI and have the generation AI perform the reservation procedure for airline tickets, hotels, and sightseeing tours.

[0067] The suggestion unit can customize a travel plan based on activities of interest to the user. The suggestion unit customizes a travel plan based on activities of interest to the user, for example. For example, if the user is interested in snorkeling, the suggestion unit can suggest travel destinations where snorkeling is enjoyed. Furthermore, if the user is interested in historical sites, the suggestion unit can suggest travel plans that visit historical sites. Furthermore, if the user is interested in outdoor activities, the suggestion unit can suggest travel destinations where outdoor activities are enjoyed. For example, if the user is interested in hiking, the suggestion unit can suggest travel destinations where hiking is enjoyed. This makes it possible to provide a customized travel plan based on the user's interests. Some or all of the above-described 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 data on activities of interest to the user into the generation AI and cause the generation AI to propose a customized travel plan.

[0068] The suggestion unit can generate a travel plan by referencing a database of travel destinations based on the user's conditions. The suggestion unit can, for example, generate a travel plan by referencing a database of travel destinations based on the user's conditions. The travel plan can include, but is not limited to, details such as itineraries, destinations to visit, and activities. The suggestion unit can also customize the travel plan based on the user's activities of interest. For example, if the user is interested in snorkeling, the suggestion unit can generate a travel plan that allows the user to enjoy snorkeling. If the user is interested in historical sites, the suggestion unit can also generate a travel plan that visits historical sites. This allows the generation of an optimal travel plan based on the user's conditions. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data on the user's conditions into a generation AI and cause the generation AI to generate an optimal travel plan.

[0069] The reception unit can estimate the user's emotions and adjust the input method for desired travel conditions based on the emotion data. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input desired travel conditions. For example, the user can simply input "I want to relax at a beach resort" by voice, and the desired conditions are automatically set. This allows the optimal input method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI adjust the input method based on the emotion.

[0070] The reception unit can analyze the user's past travel history and suggest an input format. For example, the reception unit can display related travel destinations as candidates based on travel destinations the user has visited in the past. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. For example, if the user visited a beach resort in the past summer, the reception unit can suggest a beach resort for the next summer as well. This makes it possible to provide an optimal input format based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data of the user's past travel history into a generation AI and have the generation AI suggest an optimal input format.

[0071] When inputting desired travel conditions, the reception unit can customize input items based on the user's current living situation and areas of interest. For example, the reception unit can suggest related travel destinations based on places the user has shown interest in in recent social media activities. The reception unit can also suggest relaxing travel destinations taking into account the user's current work or academic situation. The reception unit can also suggest travel destinations where specific activities can be enjoyed based on the user's hobbies and areas of interest. For example, if the user is interested in hiking, the reception unit can suggest travel destinations where hiking is enjoyed. This allows customized input items to be provided based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's current living situation and areas of interest into the generation AI and cause the generation AI to suggest customized input items.

[0072] The reception unit can select an input means according to the user's input method when inputting desired travel conditions. For example, the reception unit can automatically set the desired conditions when the user simply inputs, for example, "I want to relax at a beach resort" by voice. The reception unit can also customize and display input fields when the user inputs detailed conditions in text. The reception unit can also suggest travel destinations based on images uploaded by the user. For example, if the user uploads an image of a beach, a beach resort can be suggested. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described 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 data on the user's input method into a generation AI and have the generation AI select the optimal input means.

[0073] The reception unit can estimate the user's emotions and determine the priority of the desired conditions to be input based on the emotion data. For example, when the user is relaxed, the reception unit can prioritize input of detailed conditions. Furthermore, when the user is in a hurry, the reception unit can prioritize input of only basic conditions. Furthermore, when the user is excited, the reception unit can prioritize input of activities of interest. For example, when the user is excited, input items related to activities are displayed preferentially. This allows the priority of the desired conditions to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the desired conditions based on the emotion.

[0074] When inputting desired travel conditions, the reception unit can prioritize input of conditions taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. Furthermore, if the user is interested in a specific area, the reception unit can also prioritize input of conditions related to that area. Furthermore, the reception unit can prioritize input of related conditions based on areas the user has visited in the past. For example, travel destinations related to areas the user has visited in the past can be suggested. This makes it possible to provide highly relevant conditions based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input data of the user's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant conditions.

[0075] When the user inputs desired travel conditions, the reception unit can analyze the user's social media activity and suggest related conditions. For example, the reception unit can suggest related travel destinations based on the locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. The reception unit can also suggest related conditions by referring to the activities of the user's friends on social media. For example, related travel destinations can be suggested based on the locations where the user has checked in on social media. This makes it possible to provide related conditions based on the user's social media activity. Some or all of the above-described 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 data on the user's social media activity into a generation AI and have the generation AI suggest related conditions.

[0076] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired travel conditions. The reception unit, for example, customizes the input interface based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal input means. For example, the input interface is customized based on feedback provided by the user in the past. This makes it possible to provide a customized input method based on the user's past feedback. Some 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 past feedback data into a generation AI and have the generation AI customize the input method.

[0077] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the emotion data. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions. If the user is excited, the suggestion unit can also provide visually appealing suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. This makes it possible to provide an optimal way to express the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed based on the emotion.

[0078] When making a suggestion, the suggestion unit can adjust the content of the suggestion based on the importance of the travel destination. For example, the suggestion unit provides detailed information about major tourist destinations. The suggestion unit can also provide concise information about lesser tourist destinations. The suggestion unit can also prioritize suggesting tourist destinations with high importance according to the user's interests. For example, the suggestion unit provides detailed information about major tourist destinations. This makes it possible to provide an optimal level of detail in the suggestion based on the importance of the travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data about the importance of the travel destination into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0079] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the travel destination. For example, in the case of a beach resort, the suggestion unit can suggest relaxing activities. In the case of city tourism, the suggestion unit can also suggest major tourist spots. In the case of an adventure tour, the suggestion unit can also suggest active activities. For example, in the case of a beach resort, the suggestion unit can suggest relaxing activities. This makes it possible to provide an optimal suggestion algorithm depending on the category of the travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the category of the travel destination into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0080] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit can suggest a similar plan based on a travel plan selected by the user in the past. The suggestion unit can also prioritize proposals based on specific conditions from the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and make optimal suggestions. For example, the suggestion unit can suggest a similar plan based on a travel plan selected by the user in the past. This makes it possible to provide optimal suggestions based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposals.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the emotion data. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions. If the user is excited, the suggestion unit can also provide visually appealing suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. This allows the optimal length of suggestions to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions based on the emotion.

[0082] When making a proposal, the suggestion unit can determine the order of proposals based on the submission date of the travel destination. The suggestion unit, for example, determines the priority of proposals based on the best season for the travel destination. The suggestion unit can also determine the priority of proposals based on the user's planned travel date. The suggestion unit can also determine the priority of proposals based on the timing of an event at the travel destination. For example, the suggestion unit determines the priority of proposals based on the best season for the travel destination. This makes it possible to provide an optimal priority of proposals based on the submission date of the travel destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the submission date of the travel destination into the generation AI and have the generation AI determine the order of proposals.

[0083] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the travel destinations. For example, the suggestion unit prioritizes suggesting highly relevant travel destinations based on the user's interests. The suggestion unit can also prioritize suggesting highly relevant travel destinations based on the user's past travel history. The suggestion unit can also prioritize suggesting highly relevant travel destinations based on the user's current living situation. For example, the suggestion unit prioritizes suggesting highly relevant travel destinations based on the user's interests. This makes it possible to provide an optimal suggestion order based on the relevance of the travel destinations. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the relevance of the travel destinations to the generation AI and cause the generation AI to adjust the suggestion order.

[0084] When making a suggestion, the suggestion unit can adjust the proposed terminology according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can make the suggestion in simple language. Furthermore, if the user is an experienced traveler, the suggestion unit can make a detailed suggestion using technical terms. Furthermore, the suggestion unit can customize the content of the suggestion according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can make the suggestion in simple language. This makes it possible to provide the optimal proposed technical terms according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the proposed terminology.

[0085] The reservation unit can estimate the user's emotions and adjust the reservation procedure method based on the emotion data. For example, if the user is relaxed, the reservation unit can provide a detailed reservation procedure. If the user is in a hurry, the reservation unit can also provide a concise reservation procedure. If the user is excited, the reservation unit can also provide a visually appealing reservation procedure. For example, if the user is relaxed, the reservation unit can provide a detailed reservation procedure. This makes it possible to provide an optimal reservation procedure method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit can be performed using AI, for example, or without AI. For example, the reservation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the reservation procedure method based on the emotion.

[0086] During the reservation procedure, the reservation unit can select a reservation method by analyzing the user's past reservation history. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. The reservation unit can also prioritize and suggest specific conditions based on the user's past reservation history. The reservation unit can also analyze the user's past reservation history and suggest the optimal reservation method. For example, the reservation unit can suggest the optimal reservation method based on the reservation methods used by the user in the past. This makes it possible to provide the optimal reservation method based on the user's past reservation history. 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 data on the user's past reservation history into a generation AI and have the generation AI select the optimal reservation method.

[0087] The reservation unit can customize the reservation procedure based on the user's current lifestyle during the reservation process. For example, if the user is busy, the reservation unit can provide a simple reservation procedure. Furthermore, if the user is relaxed, the reservation unit can also provide a detailed reservation procedure. Furthermore, the reservation unit can suggest an optimal reservation procedure based on the user's current lifestyle. For example, if the user is busy, a simple reservation procedure can be provided. This makes it possible to provide an optimal reservation procedure based on the user's current lifestyle. Some or all of the above-described 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 data on the user's current lifestyle into the generation AI and have the generation AI customize the reservation procedure.

[0088] The reservation unit can improve the reservation procedure by reflecting user feedback during the reservation procedure. The reservation unit improves the reservation procedure, for example, based on feedback previously provided by the user. The reservation unit can also preferentially suggest specific reservation methods based on the user's past feedback. The reservation unit can also analyze the user's past feedback and suggest the optimal reservation procedure. For example, the reservation unit improves the reservation procedure based on feedback previously provided by the user. This makes it possible to provide the optimal reservation procedure based on the user's feedback. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input user feedback data into a generation AI and have the generation AI improve the reservation procedure.

[0089] The reservation unit can estimate the user's emotions and prioritize the reservation procedures based on the emotion data. For example, if the user is relaxed, the reservation unit can prioritize detailed reservation procedures. Furthermore, if the user is in a hurry, the reservation unit can prioritize basic reservation procedures. Furthermore, if the user is excited, the reservation unit can prioritize visually appealing reservation procedures. For example, if the user is relaxed, the reservation unit can prioritize detailed reservation procedures. This allows optimal reservation procedure prioritization according to the user's emotions to be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reservation unit can be performed using, for example, an AI, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the reservation procedures based on the emotion.

[0090] The reservation unit can select the optimal reservation method during the reservation procedure, taking into account the user's geographical location information. For example, the reservation unit prioritizes suggesting accommodations close to the user's current location. Furthermore, if the user is interested in a particular area, the reservation unit can also suggest accommodations related to that area. Furthermore, the reservation unit can also suggest related accommodations based on areas the user has previously visited. For example, the reservation unit prioritizes suggesting accommodations close to the user's current location. This makes it possible to provide the optimal reservation method based on the user's geographical location information. Some or all of the above-described 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 geographical location information data into a generation AI and have the generation AI select the optimal reservation method.

[0091] During the reservation process, the reservation unit can analyze the user's social media activity and suggest a means for the reservation process. For example, the reservation unit can suggest relevant accommodations based on the location where the user checked in on social media. The reservation unit can also analyze the content of the user's social media posts and suggest relevant accommodations. The reservation unit can also suggest relevant accommodations based on the activity of the user's friends on social media. For example, relevant accommodations can be suggested based on the location where the user checked in on social media. This makes it possible to provide the 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, or without, AI. For example, the reservation unit can input data on the user's social media activity into a generation AI and have the generation AI suggest reservation means.

[0092] The reservation unit can customize the reservation procedure method by reflecting the user's past feedback during the reservation procedure. The reservation unit customizes the reservation procedure, for example, based on feedback provided by the user in the past. The reservation unit can also preferentially suggest specific reservation methods based on the user's past feedback. The reservation unit can also analyze the user's past feedback and suggest the optimal reservation procedure. For example, the reservation unit customizes the reservation procedure based on feedback provided by the user in the past. This makes it possible to provide the optimal reservation procedure based on the user's past feedback. 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 data of the user's past feedback into a generation AI and have the generation AI customize the reservation procedure. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, proposal unit, and reservation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows a user to input desired travel conditions using the reception device 38 of the smart device 14. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's conditions using a generation AI and proposes an optimal travel plan. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and performs a reservation procedure based on the proposed travel plan. For example, some or all of the reception unit, proposal unit, and reservation unit may be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, proposal unit, and reservation 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 allows the user to vocally input desired travel conditions using the microphone 238 of the smart glasses 214. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's conditions using a generation AI and proposes an optimal travel plan. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and performs a reservation procedure based on the proposed travel plan. For example, some or all of the reception unit, proposal unit, and reservation unit may be realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, and reservation 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 allows the user to vocally input desired travel conditions using the microphone 238 of the headset-type terminal 314. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's conditions using a generation AI and proposes an optimal travel plan. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and performs a reservation procedure based on the proposed travel plan. For example, some or all of the reception unit, proposal unit, and reservation unit may be realized by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, and reservation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows a user to vocally input desired travel conditions using the microphone 238 of the robot 414. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's conditions using a generation AI and proposes an optimal travel plan. For example, the reservation unit is realized by the specific processing unit 290 of the data processing device 12, and performs a reservation procedure based on the proposed travel plan. For example, some or all of the reception unit, proposal unit, and reservation unit may be realized by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The suggestion unit can estimate the user's emotions and adjust the proposed travel plan based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize suggested travel destinations and activities that are relaxing. If the user is excited, it can suggest travel plans that include adventurous or exciting activities. If the user is sad, it can suggest travel destinations aimed at healing and refreshment. This allows the optimal travel plan to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI adjust the proposed content based on the emotion.

[0095] The suggestion unit can analyze the user's past travel history and suggest new travel plans based on past travel destinations and activities. For example, it can suggest again travel destinations that the user has visited in the past and that the user was particularly satisfied with. It can also suggest travel plans that include activities that the user has particularly enjoyed in the past. Furthermore, it can analyze the user's preferences from the past travel history and suggest similar travel destinations and activities. This makes it possible to provide an optimal travel plan based on the user's past travel history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the user's past travel history into a generation AI and have the generation AI suggest an optimal travel plan.

[0096] The suggestion unit can customize a travel plan based on the user's current living situation and areas of interest. For example, the suggestion unit can suggest related travel destinations based on places the user has shown interest in in their recent social media activities. It can also suggest relaxing travel destinations taking into account the user's current work or academic situation. It can also suggest travel destinations where specific activities can be enjoyed based on the user's hobbies and areas of interest. This allows the system to provide an optimal travel plan based on the user's current living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the user's current living situation and areas of interest into the generation AI and have the generation AI suggest a customized travel plan.

[0097] The suggestion unit can estimate the user's emotions and adjust the order of travel plan suggestions based on the estimated emotions. For example, if the user is relaxed, detailed suggestions can be provided preferentially. If the user is in a hurry, concise suggestions can be provided preferentially. Furthermore, if the user is excited, visually appealing suggestions can be provided preferentially. This allows for an optimal suggestion order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the suggestion order based on the emotion.

[0098] The suggestion unit can adjust the content of the suggestion according to the user's level of expertise. For example, if the user is a novice traveler, the suggestion unit can focus on basic information. On the other hand, if the user is an experienced traveler, the suggestion unit can provide suggestions including detailed information and expert advice. Furthermore, the way the suggestion is presented can be customized according to the user's level of expertise. This makes it possible to provide optimal suggestions according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the content of the suggestion.

[0099] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is in a hurry, concise suggestions can be provided. Furthermore, if the user is excited, visually appealing suggestions can be provided. This makes it possible to provide an optimal way to express suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed based on the emotion.

[0100] The suggestion unit can improve the content of the suggestion by reflecting the user's past feedback. For example, the suggestion unit can customize the content of the suggestion based on feedback provided by the user in the past. Also, the suggestion unit can prioritize suggestions for specific conditions based on the user's past feedback. Furthermore, the suggestion unit can analyze the user's past feedback and make optimal suggestions. This makes it possible to provide optimal suggestions based on the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data of the user's past feedback into the generation AI and cause the generation AI to improve the content of the suggestion.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is in a hurry, brief suggestions can be provided. Furthermore, if the user is excited, visually appealing suggestions can be provided. This allows the optimal length of suggestions to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions based on the emotion.

[0102] The suggestion unit can suggest a travel plan taking into account the user's geographical location information. For example, it can prioritize suggesting travel destinations close to the user's current location. Also, if the user is interested in a particular area, it can suggest travel destinations related to that area. Furthermore, it can suggest related travel destinations based on areas the user has visited in the past. This makes it possible to provide an optimal travel plan based on the user's geographical location information. 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 user's geographical location information data into the generation AI and cause the generation AI to suggest an optimal travel plan.

[0103] The suggestion unit can analyze the user's social media activity and suggest related travel plans. For example, it can suggest related travel destinations based on the locations where the user has checked in on social media. It can also suggest related travel destinations by analyzing the content of the user's social media posts. It can also suggest related travel destinations based on the activities of the user's friends on social media. This makes it possible to provide an optimal travel plan based on the user's social media activity. 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 data on the user's social media activity into a generation AI and have the generation AI suggest related travel plans.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit allows the user to input desired travel conditions. Desired travel conditions include, for example, budget, travel destination, travel period, and activities of interest. The reception unit provides an interface for the user to input conditions in text format, and voice input and image upload are also possible. Step 2: The proposal unit analyzes the conditions entered by the reception unit and proposes the optimal travel plan. The proposal unit uses generation AI to refer to a database of travel destinations and identify the travel destinations, accommodations, and tourist spots that best suit the user's conditions. It also customizes the travel plan based on the user's activities of interest. Step 3: The reservation department completes the reservation process based on the travel plan proposed by the proposal department. The reservation department can use the generation AI to make reservations for airline tickets, hotels, and sightseeing tours all at once. It proposes the optimal reservation process based on the user's budget and travel period.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0107] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. 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.

[0113] 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.

[0114] 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 audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] 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 carried out in a secure state.

[0117] Fig. 4 shows an example of the 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.

[0118] 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.

[0119] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0123] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. 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.

[0129] 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.

[0130] 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 audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] 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 carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0137] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0139] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. 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.

[0145] 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.

[0146] 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 audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] 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 carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0150] 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.

[0151] 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.

[0152] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0154] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] 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 voice indicating a user input regarding 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.

[0156] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] 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 USB (Universal Serial Bus) 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.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0172] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 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 section where travel requirements are entered; a proposal unit that analyzes the conditions input by the reception unit and proposes a travel plan; a reservation unit that performs reservation procedures based on the travel plan proposed by the proposal unit. A system characterized by:

2. The proposal unit Consult a database of travel destinations to identify the destinations that best fit the user's criteria 2. The system of claim 1.

3. The proposal unit Suggest accommodation and sightseeing spots based on your budget and travel period 2. The system of claim 1.

4. The reservation unit Book flights, hotels, and sightseeing tours all in one place 2. The system of claim 1.

5. The proposal unit Customize your travel plans based on your activity interests 2. The system of claim 1.

6. The proposal unit Generate a travel plan by referencing a database of travel destinations based on the user's criteria 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input method for desired travel conditions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past travel history and suggests input formats 2. The system of claim 1.

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