system

Abstract

The system according to the embodiment aims to propose an original tour plan based on the user's travel wishes and interests. [Solution] The system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit interactively inputs a user's travel wishes or interests. The generation unit analyzes the content of the dialogue input by the reception unit and generates a tour plan. The confirmation unit proposes the tour plan generated by the generation unit to the user, who then confirms and modifies it.

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 had the problem of making it difficult to propose original tour plans based on a user's travel wishes and interests.

[0005] The system according to the embodiment aims to propose an original tour plan based on the user's travel wishes and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit interactively inputs a user's travel wishes or interests. The generation unit analyzes the content of the dialogue input by the reception unit and generates a tour plan. The confirmation unit proposes the tour plan generated by the generation unit to the user, who then confirms and modifies it. [Effects of the Invention]

[0007] The system according to the embodiment can propose an original tour plan based on the user's travel wishes and interests. [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) The original tour proposal system according to an embodiment of the present invention proposes an original tour based on the content of a dialogue with the user, even if the specific time and destination are undecided. In this original tour proposal system, the user interactively inputs their travel preferences and interests. The AI ​​analyzes the dialogue, generates an optimal tour plan, and proposes it to the user. The user can then review and modify the proposed tour plan. For example, if the user inputs preferences such as "I love nature," "I want to visit historical sites," or "I want a relaxing place," this information is input to the AI. The AI ​​then understands the user's preferences and interests and generates an optimal tour plan based on them. For example, if the user selects "I love nature," the AI ​​generates a tour plan that includes tourist spots rich in nature. The generated tour plan is then proposed to the user, who can review and modify it as needed. For example, the user can select the tourist spots they want to visit from the proposed tourist spots and exclude the ones they do not want to visit. This system allows users to easily create an original tour that is optimal for them, even if the specific time and destination are undecided. The AI ​​then proposes an optimal tour plan based on the user's preferences and interests, simply by interactively inputting their preferences and interests. This facilitates smooth travel planning. Furthermore, the generated tour plan is based on the user's wishes, resulting in a highly satisfying trip. For example, if a user inputs "I like nature" and "I want to go to historical places," the generation AI will generate a tour plan that combines natural and historical tourist spots. The user can then review the plan, select the places they want to go to, and exclude the places they don't want to go to, to create an original tour that is best suited to them. This allows the original tour suggestion system to propose the optimal tour plan based on the user's wishes and interests, which the user can then review and modify.

[0029] The original tour proposal system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit interactively inputs a user's travel preferences and interests. The user's travel preferences and interests include, but are not limited to, destinations, budgets, and travel purposes. The reception unit can input the user's preferences and interests using, for example, text chat or voice dialogue. The generation unit uses a generation AI to analyze the dialogue input by the reception unit and generate an optimal tour plan. The generation AI analyzes the user's preferences and interests using, for example, natural language processing technology or a machine learning algorithm, and generates a tour plan based on the analysis. The generation unit generates a tour plan including, for example, an itinerary, accommodations, tourist attractions, etc. based on the user's preferences and interests. The confirmation unit proposes the tour plan generated by the generation unit to the user, who can confirm and modify it. The confirmation unit proposes the tour plan through, for example, a user interface, allowing the user to confirm the tour plan using a preview screen or a confirmation checklist. The user can confirm the proposed tour plan and modify it as necessary. For example, a user can select places they want to go to and exclude places they do not want to go to. As a result, the original tour proposal system according to the embodiment proposes an optimal tour plan based on the user's wishes and interests, and the user can confirm and modify the plan.

[0030] The generation unit can generate a tour plan based on the user's wishes and interests. The generation unit generates a tour plan including an itinerary, accommodations, tourist attractions, etc. based on the user's wishes and interests, for example. The generation unit uses a generation AI to analyze the user's wishes and interests and generate a tour plan based on the results. The generation AI uses, for example, natural language processing technology or a machine learning algorithm to analyze the user's wishes and interests and generate a tour plan based on the results. The generation unit generates a tour plan including an itinerary, accommodations, tourist attractions, etc. based on the user's wishes and interests, for example. This allows the generation unit to generate an optimal tour plan based on the user's wishes and interests.

[0031] The confirmation unit allows the user to check the proposed tour plan and modify it as necessary. The confirmation unit proposes the tour plan generated by the generation unit to the user, and the user modifies it. The confirmation unit, for example, proposes the tour plan through a user interface, and the user can confirm the tour plan using a preview screen or a confirmation checklist. The user can check the proposed tour plan and modify it as necessary. For example, the user can select places they want to go and exclude places they do not want to go. This allows the confirmation unit to allow the user to check the proposed tour plan and modify it as necessary.

[0032] The reception unit allows the user to interactively input travel wishes or interests. The reception unit interactively inputs the user's travel wishes and interests. The user's travel wishes and interests include, but are not limited to, destination, budget, and travel purpose, for example. The reception unit can input the user's wishes and interests using, for example, text chat or voice dialogue. This allows the reception unit to interactively input the user's travel wishes and interests.

[0033] The generation unit can analyze the dialogue content using the generation AI and generate a tour plan. The generation unit uses the generation AI to analyze the dialogue content input by the reception unit and generate an optimal tour plan. The generation AI uses, for example, natural language processing technology or a machine learning algorithm to analyze the user's wishes and interests and generate a tour plan based on the analysis. The generation unit generates a tour plan including, for example, an itinerary, accommodations, tourist spots, etc. based on the user's wishes and interests. This allows the generation unit to analyze the dialogue content using the generation AI and generate a tour plan.

[0034] The confirmation unit allows the user to select places they want to go and exclude places they do not want to go. The confirmation unit proposes the tour plan generated by the generation unit to the user, and the user confirms and modifies it. For example, the confirmation unit proposes a tour plan through a user interface, and the user can confirm the tour plan using a preview screen or a confirmation checklist. The user can confirm the proposed tour plan and modify it as necessary. For example, the user can select places they want to go and exclude places they do not want to go. This allows the confirmation unit to select places they want to go and exclude places they do not want to go.

[0035] The reception unit can analyze the user's past travel history and select the optimal input method. The reception unit can automatically suggest related travel preferences and interests based on, for example, places the user has visited in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest preferences and interests related to specific seasons or events from the user's past travel history. This allows the reception unit to select the optimal input method based on the user's past travel history. Past travel history includes, for example, past reservation information and travel records, but is not limited to these examples.

[0036] The reception unit can filter travel desires and interests based on the user's current living situation or areas of interest when the user inputs the travel desires or interests. The reception unit can, for example, suggest related travel desires and interests based on the user's current living situation (work, family, etc.). The reception unit can also filter related travel desires and interests based on the user's areas of interest (sports, culture, etc.). Furthermore, the reception unit can suggest appropriate travel desires and interests based on the user's current health condition. In this way, the reception unit can suggest more appropriate travel desires and interests by filtering based on the user's current living situation and areas of interest. Living situations include, but are not limited to, occupation, family environment, health condition, etc.

[0037] When inputting travel wishes and interests, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, the reception unit can prioritize suggesting tourist destinations close to the user's current location. The reception unit can also prioritize suggesting tourist destinations close to places the user has visited in the past. Furthermore, the reception unit can prioritize suggesting tourist destinations that are easily accessible from the user's current location. This allows the reception unit to prioritize inputting highly relevant information in consideration of the user's geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to these examples.

[0038] When inputting travel preferences and interests, the reception unit can analyze the user's social media activity and input related information. The reception unit can, for example, suggest tourist destinations that the user has "liked" on social media. The reception unit can also suggest related information from travel-related accounts that the user follows. Furthermore, the reception unit can also suggest related information based on travel preferences and interests that the user has shared on social media. This allows the reception unit to analyze the user's social media activity and input related information. Social media activity includes, for example, the content of posts and accounts followed, but is not limited to these examples.

[0039] When generating a tour plan, the generation unit can generate the plan by referring to the user's past travel history. For example, the generation unit generates a tour plan that includes related tourist attractions based on places the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour plan. This allows the generation unit to generate an optimal plan by referring to the user's past travel history. Past travel history includes, for example, past reservation information and travel records, but is not limited to these examples.

[0040] When generating a tour plan, the generation unit can customize the plan based on the user's current living situation. For example, if the user is busy with work, the generation unit can generate a tour plan that can be enjoyed in a short period of time. Furthermore, if the user is unable to travel for a long period of time due to family circumstances, the generation unit can also generate a tour plan that can be enjoyed in a nearby area. Furthermore, the generation unit can also generate a reasonable plan based on the user's health condition. This allows the generation unit to customize the plan based on the user's current living situation. Examples of the current living situation include, but are not limited to, occupation, family environment, and health condition.

[0041] When generating a tour plan, the generation unit can generate the plan taking into consideration the user's geographical location information. For example, the generation unit generates a tour plan that prioritizes tourist spots close to the user's current location. The generation unit can also generate a tour plan that includes tourist spots close to places the user has visited in the past. Furthermore, the generation unit can generate a tour plan that includes tourist spots that are easily accessible from the user's current location. This allows the generation unit to generate an optimal plan taking into consideration the user's geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to these examples.

[0042] When generating a tour plan, the generation unit can analyze the user's social media activity to generate the plan. For example, the generation unit generates a tour plan that includes tourist attractions that the user has "liked" on social media. The generation unit can also generate a tour plan that includes related tourist attractions from travel-related accounts that the user follows. Furthermore, the generation unit can also generate a tour plan that includes related tourist attractions based on travel wishes and interests that the user has shared on social media. This allows the generation unit to generate a plan by analyzing the user's social media activity. Social media activity includes, for example, posted content and followed accounts, but is not limited to these examples.

[0043] When confirming a tour plan, the confirmation unit can select a confirmation method by referring to the user's past travel history. For example, the confirmation unit can confirm a tour plan including related tourist attractions based on places the user has visited in the past. The confirmation unit can also suggest a route that avoids crowds based on the user's past travel history. Furthermore, the confirmation unit can analyze the user's past travel history and confirm the most efficient tour plan. This allows the confirmation unit to select the optimal confirmation method by referring to the user's past travel history. Past travel history includes, for example, past reservation information and travel records, but is not limited to these examples.

[0044] The confirmation unit can customize the confirmation means based on the user's current living situation when confirming the tour plan. For example, if the user is busy with work, the confirmation unit can confirm a tour plan that can be enjoyed in a short period of time. Furthermore, if the user has difficulty traveling for a long period of time due to family circumstances, the confirmation unit can also confirm a tour plan that can be enjoyed in a nearby area. Furthermore, the confirmation unit can also confirm a reasonable plan based on the user's health condition. This allows the confirmation unit to customize the confirmation means based on the user's current living situation. The current living situation includes, but is not limited to, for example, occupation, family environment, health condition, etc.

[0045] When confirming a tour plan, the confirmation unit can select a confirmation method taking into consideration the user's geographical location information. For example, the confirmation unit prioritizes checking tourist attractions close to the user's current location. The confirmation unit can also check tourist attractions close to places the user has visited in the past. Furthermore, the confirmation unit can check tourist attractions that are easily accessible from the user's current location. This allows the confirmation unit to select an optimal confirmation method taking into consideration the user's geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to these examples.

[0046] The confirmation unit can analyze the user's social media activity and suggest a confirmation method when confirming the tour plan. For example, the confirmation unit can confirm tourist destinations that the user has "liked" on social media. The confirmation unit can also confirm related tourist destinations from travel-related accounts that the user follows. Furthermore, the confirmation unit can also confirm related tourist destinations based on travel wishes and interests that the user has shared on social media. This allows the confirmation unit to analyze the user's social media activity and suggest a confirmation method. Social media activity includes, for example, posted content and followed accounts, but is not limited to these examples.

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

[0048] When the user inputs his / her travel preferences and interests, the reception unit can automatically suggest related travel preferences and interests by referring to the user's past travel history. For example, the reception unit can suggest related tourist spots based on places 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. Furthermore, the reception unit can predict and suggest preferences and interests related to specific seasons or events from the user's past travel history. This allows the reception unit to select the optimal input method based on the user's past travel history.

[0049] The generation unit can customize a tour plan based on the user's current living situation. For example, if the user is busy with work, the generation unit can generate a tour plan that can be enjoyed in a short period of time. Also, if the user has difficulty traveling for a long period of time due to family circumstances, the generation unit can generate a tour plan that can be enjoyed in a nearby area. Furthermore, the generation unit can generate a reasonable plan based on the user's health condition. This allows the generation unit to customize a plan based on the user's current living situation.

[0050] The reception unit can analyze the user's social media activity and input related information. For example, it can suggest tourist spots that the user has "liked" on social media. The reception unit can also suggest related information from travel-related accounts that the user follows. Furthermore, the reception unit can also suggest related information based on travel wishes and interests that the user has shared on social media. This allows the reception unit to analyze the user's social media activity and input related information.

[0051] The reception unit can preferentially input highly relevant information in consideration of the user's geographical location information. For example, the reception unit can preferentially suggest tourist spots that are close to the user's current location. The reception unit can also suggest tourist spots that are close to places the user has visited in the past. Furthermore, the reception unit can also preferentially suggest tourist spots that are easily accessible from the user's current location. This allows the reception unit to preferentially input highly relevant information in consideration of the user's geographical location information.

[0052] When generating a tour plan, the generation unit can generate the plan by referring to the user's past travel history. For example, a tour plan including related tourist spots is generated based on places the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour plan. This allows the generation unit to generate an optimal plan by referring to the user's past travel history.

[0053] When confirming a tour plan, the confirmation unit can select a confirmation method by referring to the user's past travel history. For example, the confirmation unit can confirm a tour plan that includes related tourist attractions based on places the user has visited in the past. The confirmation unit can also suggest a route that avoids crowds based on the user's past travel history. Furthermore, the confirmation unit can analyze the user's past travel history and confirm the most efficient tour plan. This allows the confirmation unit to select the optimal confirmation method by referring to the user's past travel history.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit interactively inputs the user's travel wishes and interests. The user's travel wishes and interests include, for example, the destination, budget, and purpose of the trip. The reception unit can input the user's wishes and interests using text chat or voice dialogue. Step 2: The generation unit uses the generation AI to analyze the dialogue content input by the reception unit and generate an optimal tour plan. The generation AI uses natural language processing technology and machine learning algorithms to analyze the user's wishes and interests and generate a tour plan based on them. The generation unit generates a tour plan including itinerary, accommodations, tourist spots, etc. based on the user's wishes and interests. Step 3: The confirmation unit proposes the tour plan generated by the generation unit to the user, who then confirms and modifies it. The confirmation unit proposes the tour plan through a user interface, and the user can confirm the tour plan using a preview screen or a confirmation checklist. The user can confirm the proposed tour plan and modify it as necessary. For example, the user can select the places they want to go to and exclude the places they do not want to go to.

[0056] (Example 2) The original tour proposal system according to an embodiment of the present invention proposes an original tour based on the content of a dialogue with the user, even if the specific time and destination are undecided. In this original tour proposal system, the user interactively inputs their travel preferences and interests. The AI ​​analyzes the dialogue, generates an optimal tour plan, and proposes it to the user. The user can then review and modify the proposed tour plan. For example, if the user inputs preferences such as "I love nature," "I want to visit historical sites," or "I want a relaxing place," this information is input to the AI. The AI ​​then understands the user's preferences and interests and generates an optimal tour plan based on them. For example, if the user selects "I love nature," the AI ​​generates a tour plan that includes tourist spots rich in nature. The generated tour plan is then proposed to the user, who can review and modify it as needed. For example, the user can select the tourist spots they want to visit from the proposed tourist spots and exclude the ones they do not want to visit. This system allows users to easily create an original tour that is optimal for them, even if the specific time and destination are undecided. The AI ​​then proposes an optimal tour plan based on the user's preferences and interests, simply by interactively inputting their preferences and interests. This facilitates smooth travel planning. Furthermore, the generated tour plan is based on the user's wishes, resulting in a highly satisfying trip. For example, if a user inputs "I like nature" and "I want to go to historical places," the generation AI will generate a tour plan that combines natural and historical tourist spots. The user can then review the plan, select the places they want to go to, and exclude the places they don't want to go to, to create an original tour that is best suited to them. This allows the original tour suggestion system to propose the optimal tour plan based on the user's wishes and interests, which the user can then review and modify.

[0057] The original tour proposal system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit interactively inputs a user's travel preferences and interests. The user's travel preferences and interests include, but are not limited to, destinations, budgets, and travel purposes. The reception unit can input the user's preferences and interests using, for example, text chat or voice dialogue. The generation unit uses a generation AI to analyze the dialogue input by the reception unit and generate an optimal tour plan. The generation AI analyzes the user's preferences and interests using, for example, natural language processing technology or a machine learning algorithm, and generates a tour plan based on the analysis. The generation unit generates a tour plan including, for example, an itinerary, accommodations, tourist attractions, etc. based on the user's preferences and interests. The confirmation unit proposes the tour plan generated by the generation unit to the user, who can confirm and modify it. The confirmation unit proposes the tour plan through, for example, a user interface, allowing the user to confirm the tour plan using a preview screen or a confirmation checklist. The user can confirm the proposed tour plan and modify it as necessary. For example, a user can select places they want to go to and exclude places they do not want to go to. As a result, the original tour proposal system according to the embodiment proposes an optimal tour plan based on the user's wishes and interests, and the user can confirm and modify the plan.

[0058] The generation unit can generate a tour plan based on the user's wishes and interests. The generation unit generates a tour plan including an itinerary, accommodations, tourist attractions, etc. based on the user's wishes and interests, for example. The generation unit uses a generation AI to analyze the user's wishes and interests and generate a tour plan based on the results. The generation AI uses, for example, natural language processing technology or a machine learning algorithm to analyze the user's wishes and interests and generate a tour plan based on the results. The generation unit generates a tour plan including an itinerary, accommodations, tourist attractions, etc. based on the user's wishes and interests, for example. This allows the generation unit to generate an optimal tour plan based on the user's wishes and interests.

[0059] The confirmation unit allows the user to check the proposed tour plan and modify it as necessary. The confirmation unit proposes the tour plan generated by the generation unit to the user, and the user modifies it. The confirmation unit, for example, proposes the tour plan through a user interface, and the user can confirm the tour plan using a preview screen or a confirmation checklist. The user can check the proposed tour plan and modify it as necessary. For example, the user can select places they want to go and exclude places they do not want to go. This allows the confirmation unit to allow the user to check the proposed tour plan and modify it as necessary.

[0060] The reception unit allows the user to interactively input travel wishes or interests. The reception unit interactively inputs the user's travel wishes and interests. The user's travel wishes and interests include, but are not limited to, destination, budget, and travel purpose, for example. The reception unit can input the user's wishes and interests using, for example, text chat or voice dialogue. This allows the reception unit to interactively input the user's travel wishes and interests.

[0061] The generation unit can analyze the dialogue content using the generation AI and generate a tour plan. The generation unit uses the generation AI to analyze the dialogue content input by the reception unit and generate an optimal tour plan. The generation AI uses, for example, natural language processing technology or a machine learning algorithm to analyze the user's wishes and interests and generate a tour plan based on the analysis. The generation unit generates a tour plan including, for example, an itinerary, accommodations, tourist spots, etc. based on the user's wishes and interests. This allows the generation unit to analyze the dialogue content using the generation AI and generate a tour plan.

[0062] The confirmation unit allows the user to select places they want to go and exclude places they do not want to go. The confirmation unit proposes the tour plan generated by the generation unit to the user, and the user confirms and modifies it. For example, the confirmation unit proposes a tour plan through a user interface, and the user can confirm the tour plan using a preview screen or a confirmation checklist. The user can confirm the proposed tour plan and modify it as necessary. For example, the user can select places they want to go and exclude places they do not want to go. This allows the confirmation unit to select places they want to go and exclude places they do not want to go.

[0063] The reception unit can estimate the user's emotions and adjust the input method for travel wishes and interests based on the estimated user emotions. 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 to enable the user to quickly input their travel wishes and interests. This allows the reception unit to adjust the input method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The reception unit can analyze the user's past travel history and select the optimal input method. The reception unit can automatically suggest related travel preferences and interests based on, for example, places the user has visited in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest preferences and interests related to specific seasons or events from the user's past travel history. This allows the reception unit to select the optimal input method based on the user's past travel history. Past travel history includes, for example, past reservation information and travel records, but is not limited to these examples.

[0065] The reception unit can filter travel desires and interests based on the user's current living situation or areas of interest when the user inputs the travel desires or interests. The reception unit can, for example, suggest related travel desires and interests based on the user's current living situation (work, family, etc.). The reception unit can also filter related travel desires and interests based on the user's areas of interest (sports, culture, etc.). Furthermore, the reception unit can suggest appropriate travel desires and interests based on the user's current health condition. In this way, the reception unit can suggest more appropriate travel desires and interests by filtering based on the user's current living situation and areas of interest. Living situations include, but are not limited to, occupation, family environment, health condition, etc.

[0066] The reception unit can estimate the user's emotions and prioritize the travel preferences and interests to be input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize relaxing travel preferences and interests. Furthermore, if the user is excited, the reception unit can prioritize active travel preferences and interests. Furthermore, if the user is tired, the reception unit can prioritize refreshing travel preferences and interests. This allows the reception unit to prioritize the travel preferences and interests based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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.

[0067] When inputting travel wishes and interests, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, the reception unit can prioritize suggesting tourist destinations close to the user's current location. The reception unit can also prioritize suggesting tourist destinations close to places the user has visited in the past. Furthermore, the reception unit can prioritize suggesting tourist destinations that are easily accessible from the user's current location. This allows the reception unit to prioritize inputting highly relevant information in consideration of the user's geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to these examples.

[0068] When inputting travel preferences and interests, the reception unit can analyze the user's social media activity and input related information. The reception unit can, for example, suggest tourist destinations that the user has "liked" on social media. The reception unit can also suggest related information from travel-related accounts that the user follows. Furthermore, the reception unit can also suggest related information based on travel preferences and interests that the user has shared on social media. This allows the reception unit to analyze the user's social media activity and input related information. Social media activity includes, for example, the content of posts and accounts followed, but is not limited to these examples.

[0069] The generation unit can estimate the user's emotions and adjust the tour plan generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a tour plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a tour plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate a tour plan that adds visually stimulating effects. This allows the generation unit to adjust the tour plan generation method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] When generating a tour plan, the generation unit can generate the plan by referring to the user's past travel history. For example, the generation unit generates a tour plan that includes related tourist attractions based on places the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour plan. This allows the generation unit to generate an optimal plan by referring to the user's past travel history. Past travel history includes, for example, past reservation information and travel records, but is not limited to these examples.

[0071] When generating a tour plan, the generation unit can customize the plan based on the user's current living situation. For example, if the user is busy with work, the generation unit can generate a tour plan that can be enjoyed in a short period of time. Furthermore, if the user is unable to travel for a long period of time due to family circumstances, the generation unit can also generate a tour plan that can be enjoyed in a nearby area. Furthermore, the generation unit can also generate a reasonable plan based on the user's health condition. This allows the generation unit to customize the plan based on the user's current living situation. Examples of the current living situation include, but are not limited to, occupation, family environment, and health condition.

[0072] The generation unit can estimate the user's emotions and determine the priority of tour plans to be generated based on the estimated user emotions. For example, when the user is relaxed, the generation unit generates a tour plan that prioritizes relaxing tourist spots. Furthermore, when the user is excited, the generation unit can generate a tour plan that prioritizes active tourist spots. Furthermore, when the user is tired, the generation unit can generate a tour plan that prioritizes refreshing tourist spots. This allows the generation unit to determine the priority of tour plans based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When generating a tour plan, the generation unit can generate the plan taking into consideration the user's geographical location information. For example, the generation unit generates a tour plan that prioritizes tourist spots close to the user's current location. The generation unit can also generate a tour plan that includes tourist spots close to places the user has visited in the past. Furthermore, the generation unit can generate a tour plan that includes tourist spots that are easily accessible from the user's current location. This allows the generation unit to generate an optimal plan taking into consideration the user's geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to these examples.

[0074] When generating a tour plan, the generation unit can analyze the user's social media activity to generate the plan. For example, the generation unit generates a tour plan that includes tourist attractions that the user has "liked" on social media. The generation unit can also generate a tour plan that includes related tourist attractions from travel-related accounts that the user follows. Furthermore, the generation unit can also generate a tour plan that includes related tourist attractions based on travel wishes and interests that the user has shared on social media. This allows the generation unit to generate a plan by analyzing the user's social media activity. Social media activity includes, for example, posted content and followed accounts, but is not limited to these examples.

[0075] The confirmation unit can estimate the user's emotions and adjust the tour plan confirmation method based on the estimated user emotions. For example, if the user is nervous, the confirmation unit can provide a simple and highly visible confirmation method. If the user is relaxed, the confirmation unit can also provide a confirmation method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation unit can also provide a confirmation method that focuses on the main points. This allows the confirmation unit to adjust the tour plan confirmation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] When confirming a tour plan, the confirmation unit can select a confirmation method by referring to the user's past travel history. For example, the confirmation unit can confirm a tour plan including related tourist attractions based on places the user has visited in the past. The confirmation unit can also suggest a route that avoids crowds based on the user's past travel history. Furthermore, the confirmation unit can analyze the user's past travel history and confirm the most efficient tour plan. This allows the confirmation unit to select the optimal confirmation method by referring to the user's past travel history. Past travel history includes, for example, past reservation information and travel records, but is not limited to these examples.

[0077] The confirmation unit can customize the confirmation means based on the user's current living situation when confirming the tour plan. For example, if the user is busy with work, the confirmation unit can confirm a tour plan that can be enjoyed in a short period of time. Furthermore, if the user has difficulty traveling for a long period of time due to family circumstances, the confirmation unit can also confirm a tour plan that can be enjoyed in a nearby area. Furthermore, the confirmation unit can also confirm a reasonable plan based on the user's health condition. This allows the confirmation unit to customize the confirmation means based on the user's current living situation. The current living situation includes, but is not limited to, for example, occupation, family environment, health condition, etc.

[0078] The confirmation unit can estimate the user's emotions and adjust the tour plan modification method based on the estimated user's emotions. For example, if the user is nervous, the confirmation unit can provide a simple and highly visible modification method. If the user is relaxed, the confirmation unit can also provide a modification method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation unit can also provide a modification method that focuses on the main points. This allows the confirmation unit to adjust the tour plan modification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] When confirming a tour plan, the confirmation unit can select a confirmation method taking into consideration the user's geographical location information. For example, the confirmation unit prioritizes checking tourist attractions close to the user's current location. The confirmation unit can also check tourist attractions close to places the user has visited in the past. Furthermore, the confirmation unit can check tourist attractions that are easily accessible from the user's current location. This allows the confirmation unit to select an optimal confirmation method taking into consideration the user's geographical location information. Geographical location information includes, for example, GPS data and location information services, but is not limited to these examples.

[0080] The confirmation unit can analyze the user's social media activity and suggest a confirmation method when confirming the tour plan. For example, the confirmation unit can confirm tourist destinations that the user has "liked" on social media. The confirmation unit can also confirm related tourist destinations from travel-related accounts that the user follows. Furthermore, the confirmation unit can also confirm related tourist destinations based on travel wishes and interests that the user has shared on social media. This allows the confirmation unit to analyze the user's social media activity and suggest a confirmation method. Social media activity includes, for example, posted content and followed accounts, but is not limited to these examples. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation 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 is realized by the control unit 46A of the smart device 14, and interactively inputs the user's travel wishes and interests. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal tour plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart device 14, and proposes the generated tour plan to the user, who then confirms and modifies it. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and interactively inputs the user's travel wishes and interests. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal tour plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart glasses 214, and proposes the generated tour plan to the user, who then confirms and modifies it. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and inputs the user's travel wishes and interests in an interactive format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal tour plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and proposes the generated tour plan to the user, who then confirms and modifies it. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and confirmation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and inputs the user's travel wishes and interests in an interactive format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal tour plan using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the robot 414, and proposes the generated tour plan to the user, who then confirms and modifies it.

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

[0082] When the user inputs his / her travel preferences and interests, the reception unit can automatically suggest related travel preferences and interests by referring to the user's past travel history. For example, the reception unit can suggest related tourist spots based on places 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. Furthermore, the reception unit can predict and suggest preferences and interests related to specific seasons or events from the user's past travel history. This allows the reception unit to select the optimal input method based on the user's past travel history.

[0083] The generation unit can customize a tour plan based on the user's current living situation. For example, if the user is busy with work, the generation unit can generate a tour plan that can be enjoyed in a short period of time. Also, if the user has difficulty traveling for a long period of time due to family circumstances, the generation unit can generate a tour plan that can be enjoyed in a nearby area. Furthermore, the generation unit can generate a reasonable plan based on the user's health condition. This allows the generation unit to customize a plan based on the user's current living situation.

[0084] The confirmation unit can estimate the user's emotions and adjust the tour plan confirmation method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible confirmation method can be provided. If the user is relaxed, a confirmation method including detailed information can be provided. Furthermore, if the user is in a hurry, a confirmation method that focuses on the main points can be provided. In this way, the confirmation unit can adjust the tour plan confirmation method based on the user's emotions.

[0085] The reception unit can analyze the user's social media activity and input related information. For example, it can suggest tourist spots that the user has "liked" on social media. The reception unit can also suggest related information from travel-related accounts that the user follows. Furthermore, the reception unit can also suggest related information based on travel wishes and interests that the user has shared on social media. This allows the reception unit to analyze the user's social media activity and input related information.

[0086] The generation unit can estimate the user's emotions and adjust the tour plan generation method based on the estimated user's emotions. For example, if the user is relaxed, a tour plan that proceeds at a leisurely pace can be generated. If the user is in a hurry, a tour plan that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a tour plan that adds visually stimulating effects can be generated. This allows the generation unit to adjust the tour plan generation method based on the user's emotions.

[0087] The reception unit can preferentially input highly relevant information in consideration of the user's geographical location information. For example, the reception unit can preferentially suggest tourist spots that are close to the user's current location. The reception unit can also suggest tourist spots that are close to places the user has visited in the past. Furthermore, the reception unit can also preferentially suggest tourist spots that are easily accessible from the user's current location. This allows the reception unit to preferentially input highly relevant information in consideration of the user's geographical location information.

[0088] When generating a tour plan, the generation unit can generate the plan by referring to the user's past travel history. For example, a tour plan including related tourist spots is generated based on places the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient tour plan. This allows the generation unit to generate an optimal plan by referring to the user's past travel history.

[0089] The generation unit can estimate the user's emotions and determine the priority of tour plans to be generated based on the estimated user's emotions. For example, if the user is relaxed, a tour plan that prioritizes relaxing tourist spots can be generated. Also, if the user is excited, a tour plan that prioritizes active tourist spots can be generated. Furthermore, if the user is tired, a tour plan that prioritizes refreshing tourist spots can be generated. In this way, the generation unit can determine the priority of tour plans based on the user's emotions.

[0090] When confirming a tour plan, the confirmation unit can select a confirmation method by referring to the user's past travel history. For example, the confirmation unit can confirm a tour plan that includes related tourist attractions based on places the user has visited in the past. The confirmation unit can also suggest a route that avoids crowds based on the user's past travel history. Furthermore, the confirmation unit can analyze the user's past travel history and confirm the most efficient tour plan. This allows the confirmation unit to select the optimal confirmation method by referring to the user's past travel history.

[0091] The confirmation unit can estimate the user's emotions and adjust the method of modifying the tour plan based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible modification method can be provided. If the user is relaxed, a modification method including detailed information can be provided. Furthermore, if the user is in a hurry, a modification method that focuses on the main points can be provided. In this way, the confirmation unit can adjust the method of modifying the tour plan based on the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit interactively inputs the user's travel wishes and interests. The user's travel wishes and interests include, for example, the destination, budget, and purpose of the trip. The reception unit can input the user's wishes and interests using text chat or voice dialogue. Step 2: The generation unit uses the generation AI to analyze the dialogue content input by the reception unit and generate an optimal tour plan. The generation AI uses natural language processing technology and machine learning algorithms to analyze the user's wishes and interests and generate a tour plan based on them. The generation unit generates a tour plan including itinerary, accommodations, tourist spots, etc. based on the user's wishes and interests. Step 3: The confirmation unit proposes the tour plan generated by the generation unit to the user, who then confirms and modifies it. The confirmation unit proposes the tour plan through a user interface, and the user can confirm the tour plan using a preview screen or a confirmation checklist. The user can confirm the proposed tour plan and modify it as necessary. For example, the user can select the places they want to go to and exclude the places they do not want to go to.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0095] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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, to avoid confusion and 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for interactively inputting the user's travel wishes or interests; a generation unit that analyzes the dialogue content input by the reception unit and generates a tour plan; a confirmation unit that proposes the tour plan generated by the generation unit to a user and allows the user to confirm and modify the plan. A system characterized by:

2. The generation unit Generate tour plans based on user preferences and interests The system of claim 1 .

3. The confirmation unit The user reviews the proposed tour plan and makes any necessary modifications. The system of claim 1 .

4. The reception unit The user interactively inputs their travel preferences or interests The system of claim 1 .

5. The generation unit Analyze the conversation using AI and generate a tour plan The system of claim 1 .

6. The confirmation unit Users choose where they want to go and exclude places they don't want to go The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the input method of travel wishes and interests based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past travel history and select the input method The system of claim 1 .

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