system

Abstract

The system according to the embodiment aims to automatically generate an optimal tour itinerary based on the traveler's wishes. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of desired travel destinations, itineraries, budgets, and specific activities. The generation unit generates a tour itinerary based on the information received by the reception unit. The provision unit provides the tour itinerary generated by the generation unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to automatically generate optimal tour itineraries based on a traveler's desired destinations, schedule, budget, and activities.

[0005] The system according to the embodiment aims to automatically generate an optimal tour itinerary based on the traveler's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of desired travel destinations, itineraries, budgets, and specific activities. The generation unit generates a tour itinerary based on the information received by the reception unit. The provision unit provides the tour itinerary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate an optimal tour itinerary based on the traveler's wishes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A tour creation system according to an embodiment of the present invention creates a recommended tour itinerary when a user inputs their desired travel destination, itinerary, budget, and activities. This tour creation system uses a generation AI to analyze the information and generate an optimal tour itinerary. This tour itinerary is customized based on the user's preferences and includes tourist attractions, activities, and accommodations at the travel destination. The generated tour itinerary is provided to the user, who can review it and request modifications or additions as needed. For example, a user inputs their desired travel destination, itinerary, budget, and activities. To do this, the user simply inputs their departure point and destination. For example, they might input "I want to go from my home to the train station." This information is then input into the generation AI. The generation AI then analyzes the input information and creates a video showing how to get from their current location to the destination. The generation AI calculates the optimal route based on map data and generates a video that follows that route. For example, if a user inputs a route from their home to the train station, a video that follows that route is generated. The generated video begins navigation based on the orientation of the user's smartphone. For example, if a user points their smartphone north, the video will also begin navigating north. This allows the user to navigate in the direction they are facing. Furthermore, the video on the screen moves in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to navigate at their own pace. This mechanism creates a simple structure that is easy to use for both children and the elderly, making it popular with everyone. Users can intuitively navigate without complex operations. Furthermore, since the viewpoint of the smartphone is the axis for all directions, they will not get lost, and since the smartphone is pointed horizontally, walking safety is ensured. For example, if a user is walking with their smartphone pointed horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This allows the tour generation system to generate and provide optimal tour itineraries based on the user's preferences.

[0029] A tour creation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user of a desired travel destination, itinerary, budget, and activities. The information input by the user includes, but is not limited to, specific travel destinations (e.g., domestic travel, overseas travel, specific cities, tourist attractions, etc.), itinerary (e.g., departure and return dates, length of travel), total budget, daily budget, and currency unit, and specific activities (e.g., sightseeing, sports, and cultural experiences). The generation unit uses a generation AI to generate a tour itinerary based on the information received by the reception unit. The generation AI generates the tour itinerary using, for example, techniques such as neural networks and genetic algorithms. The generation unit generates an optimal tour itinerary based on the user's preferences. The generation unit can also generate a tour itinerary by using the generation AI to collect information on tourist spots, activities, accommodations, etc. at the travel destination. For example, the generation AI may collect historical buildings, natural landscapes, theme parks, etc. as tourist spots, outdoor activities, cultural experiences, shopping, etc. as activities, and hotels, guesthouses, resorts, etc. as accommodations. The providing unit provides the tour itinerary generated by the generating unit to the user. For example, the providing unit allows the user to check the tour itinerary and request modifications or additions as necessary. For example, the providing unit allows the user to request modifications or additions, such as changes to the schedule or addition of activities. In this way, the tour generation system according to the embodiment can generate and provide an optimal tour itinerary based on the user's wishes.

[0030] The generation unit can generate a tour itinerary using a generation AI. The generation AI generates a tour itinerary using technologies such as neural networks and genetic algorithms. The generation AI generates an optimal tour itinerary based on the user's preferences. The generation AI analyzes user input information, selects optimal tourist spots, activities, and accommodations, and generates a tour itinerary. The generation AI adjusts the order in which tourist spots are visited and the length of stay based on the user's preferences to generate an efficient tour itinerary. The generation AI adjusts the type and duration of activities based on the user's preferences to generate a fulfilling tour itinerary. This improves the accuracy of generating a tour itinerary by using the generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input user input information to the generation AI, which then generates a tour itinerary.

[0031] The generation unit can collect information on tourist spots, activities, and accommodations at travel destinations and generate a tour itinerary. The generation unit, for example, collects information on historical buildings, natural landscapes, theme parks, and other tourist spots. The generation unit, for example, collects information on outdoor activities, cultural experiences, shopping, and other activities. The generation unit, for example, collects information on hotels, guesthouses, resorts, and other accommodations. The generation unit, for example, adjusts the order of visits and the length of stay based on the information on tourist spots to generate an efficient tour itinerary. The generation unit, for example, adjusts the type and time based on the information on activities to generate a fulfilling tour itinerary. The generation unit, for example, adjusts the location of accommodations and the length of stay based on the information on accommodations to generate a comfortable tour itinerary. In this way, by collecting detailed information on travel destinations, a more fulfilling tour itinerary can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information on tourist spots, activities, and accommodations into the generation AI, which then generates the tour itinerary.

[0032] The providing unit provides the tour itinerary to the user, and the user can request modifications or additions. The providing unit, for example, allows the user to check the tour itinerary and make modifications or additions as necessary. The providing unit, for example, allows the user to request modifications or additions, such as changing the schedule or adding an activity. The providing unit, for example, allows the user to check the contents of the tour itinerary and make modifications or additions as necessary. The providing unit, for example, allows the user to check the contents of the tour itinerary and make modifications or additions as necessary. This allows the user to check the tour itinerary and make modifications or additions as necessary. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's modifications or additions to the generation AI, and the generation AI can regenerate the tour itinerary.

[0033] The generation unit can customize the tour itinerary taking into account the user's wishes. The generation unit customizes the tour itinerary based on, for example, the user's wishes. The generation unit customizes the tour itinerary based on, for example, the user's preferences and past travel history. The generation unit generates an efficient tour itinerary by, for example, adjusting the order of visiting tourist spots and the length of stay based on the user's wishes. The generation unit generates a fulfilling tour itinerary by, for example, adjusting the type and time of activities based on the user's wishes. The generation unit generates a comfortable tour itinerary by, for example, adjusting the selection of accommodation and the length of stay based on the user's wishes. This enables customization according to the user's wishes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's wishes into the generation AI, which then customizes the tour itinerary.

[0034] The providing unit can check the content of the tour itinerary provided to the user and request corrections or additions as necessary. The providing unit, for example, allows the user to check the content of the tour itinerary and request corrections or additions as necessary. The providing unit can, for example, allow the user to request corrections or additions, such as changing the schedule or adding an activity. The providing unit can, for example, allow the user to check the content of the tour itinerary and request corrections or additions as necessary. The providing unit can, for example, allow the user to check the content of the tour itinerary and request corrections or additions as necessary. This allows the user to check the content of the tour itinerary and request corrections or additions as necessary. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the providing unit can input the user's requests for corrections or additions to the generation AI, which can then regenerate the tour itinerary.

[0035] The reception unit can analyze the user's past travel history and select the optimal input method. The reception unit can suggest an input method based on, for example, the user's past travel destinations and itineraries. The reception unit can analyze the user's preferred travel style from the user's past travel history and select the optimal input method. The reception unit can customize the input method based on, for example, the user's past budget and desired activities. This improves input efficiency by selecting the optimal input method based on the user's past travel history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI, which can select the optimal input method.

[0036] The reception unit can perform filtering based on the user's current living situation and areas of interest at the time of input. The reception unit, for example, considers the user's current living situation (work, family, etc.) to suggest appropriate travel destinations and itineraries. The reception unit, for example, considers the user's current living situation (work, family, etc.) to suggest relevant travel destinations and activities based on the user's areas of interest (history, nature, art, etc.). The reception unit, for example, considers the user's current health condition to suggest a reasonable travel plan. This enables more appropriate suggestions by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI, which then performs filtering.

[0037] The reception unit can prioritize input of highly relevant information taking into account the user's geographical location information when inputting information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. For example, the reception unit can cause the user to prioritize input of travel destinations that are easily accessible from the user's current location. For example, the reception unit can prioritize input of related tourist spots and activities based on the user's current location. This allows more relevant information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI, which can then prioritize input of highly relevant information.

[0038] The reception unit can analyze the user's social media activity at the time of input and input relevant information. The reception unit can suggest an input method based on, for example, travel destinations and activities shared by the user on social media. The reception unit can analyze, for example, the user's areas of interest on social media and allow the user to input relevant information. The reception unit can customize the input method by, for example, referring to travel-related accounts the user follows on social media. This allows more relevant information to be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then input relevant information.

[0039] When generating a tour itinerary, the generation unit can adjust the level of detail of the itinerary based on the importance of the travel destination. For example, the generation unit generates an itinerary including detailed descriptions for major tourist spots. For example, the generation unit generates an itinerary including concise descriptions for minor tourist spots. For example, the generation unit adjusts the visiting time and the staying time according to the importance of the travel destination. In this way, by adjusting the level of detail of the itinerary based on the importance of the travel destination, a more appropriate tour itinerary can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance data of the travel destination into the generation AI, which can then adjust the level of detail of the itinerary.

[0040] When generating a tour itinerary, the generation unit can apply different generation algorithms depending on the category of the travel destination. For example, in the case of a natural tourist destination, the generation unit applies an algorithm that generates an itinerary that emphasizes natural scenery. For example, in the case of an urban tourist destination, the generation unit applies an algorithm that generates an itinerary that emphasizes the city's major tourist attractions. For example, in the case of a historical tourist destination, the generation unit applies an algorithm that generates an itinerary that emphasizes the historical background. In this way, by applying different generation algorithms depending on the category of the travel destination, a more appropriate tour itinerary can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the travel destination into the generation AI, and the generation AI can apply different generation algorithms.

[0041] When generating a tour itinerary, the generation unit can determine the priority of the processes based on the submission time of the travel destinations. For example, if the submission time of the travel destinations is early, the generation unit generates the processes with priority. For example, if the submission time of the travel destinations is late, the generation unit generates the processes later. For example, the generation unit adjusts the level of detail and priority of the processes according to the submission time. In this way, by determining the priority of the processes based on the submission time of the travel destinations, a more efficient tour itinerary can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input data on the submission time of the travel destinations into the generation AI, and the generation AI can determine the priority of the processes.

[0042] When generating a tour itinerary, the generation unit can adjust the order of the itineraries based on the relevance of the travel destinations. For example, if the relevance of the travel destinations is high, the generation unit generates itineraries in which the travel destinations are visited consecutively. For example, if the relevance of the travel destinations is low, the generation unit generates itineraries in which the travel destinations are visited at intervals. For example, the generation unit adjusts the order of visits and the duration of stay according to the relevance of the travel destinations. In this way, by adjusting the order of the itineraries based on the relevance of the travel destinations, a more efficient tour itinerary can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the travel destinations into the generation AI, which can then adjust the order of the itineraries.

[0043] When providing a tour process, the providing unit can select the optimal providing method by referring to the user's past travel history. For example, the providing unit selects the optimal providing method based on the providing method used by the user in the past. For example, the providing unit selects the preferred providing method from the user's past travel history. For example, the providing unit customizes the providing method by referring to travel destinations and activities used by the user in the past. This makes it possible to select a more appropriate providing method by referring to the user's past travel history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's past travel history data into the generation AI, which can select the optimal providing method.

[0044] The providing unit can customize the means of provision based on the user's current living situation when providing a tour process. The providing unit selects an appropriate provision method, for example, taking into consideration the user's current living situation (work, family, etc.). The providing unit selects a reasonable provision method, for example, taking into consideration the user's current health condition. The providing unit provides relevant information, for example, based on the user's current areas of interest. This enables more appropriate provision by customizing the means of provision based on the user's current living situation. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's living situation data into the generation AI, which then customizes the means of provision.

[0045] When providing a tour process, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit prioritizes providing travel destinations close to the user's current location. For example, the providing unit prioritizes providing travel destinations that are easily accessible from the user's current location. For example, the providing unit prioritizes providing related tourist spots and activities based on the user's current location. This allows a more appropriate providing method to be selected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can select the optimal providing method.

[0046] When providing a tour process, the providing unit can analyze the user's social media activities and suggest a means of provision. The providing unit can suggest a means of provision based on, for example, travel destinations and activities shared by the user on social media. The providing unit can analyze, for example, the user's areas of interest on social media and provide related information. The providing unit can customize the means of provision based on, for example, travel-related accounts the user follows on social media. This makes it possible to suggest more appropriate means of provision by analyzing the user's social media activities. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI, which can then suggest a means of provision.

[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] The reception unit can analyze the information entered by the user in real time and provide instant feedback based on the input content. For example, when a user enters a travel destination, the latest tourist information and event information for that travel destination can be displayed instantly. Furthermore, when a user enters a budget, a list of activities and accommodations that can be enjoyed within that budget can be provided. Furthermore, when a user enters a travel date, the system can suggest the optimal travel plan to fit the date. This allows the user to receive instant feedback based on the input content, allowing for smoother travel planning.

[0049] The generation unit can generate a tour itinerary that takes into account weather information at the travel destination based on information input by the user. For example, if the weather at the travel destination is bad, indoor activities that can be enjoyed are prioritized. On the other hand, if the weather is good, a tour itinerary that includes many outdoor sightseeing and activities can be generated. Furthermore, the generation unit can provide the user with an optimal travel plan that takes into account special seasonal events and festivals. This allows the user to enjoy their trip regardless of the weather.

[0050] The generation unit can generate a tour itinerary that includes information about the culture and customs of the travel destination based on the user's input information. For example, the generation unit can suggest a tour itinerary that includes cultural events and traditional festivals of the travel destination. The generation unit can also introduce restaurants and markets where users can enjoy the travel destination's food culture and local specialties. Furthermore, the generation unit can provide information about the historical background and important tourist spots of the travel destination, allowing the user to gain a deeper understanding of the place. This allows the user to experience the travel destination's culture and customs more deeply.

[0051] The providing unit can display reviews and ratings from other users when the user checks the tour itinerary. For example, it can display ratings and comments from other users about specific tourist spots and activities for the user to refer to. It can also display reviews of accommodations and restaurants to help the user make their selections. Furthermore, it provides a function for users to post their own travel experiences as reviews, allowing them to share information with other users. This allows users to make better travel plans while taking into account the opinions of other users.

[0052] The generation unit can generate a tour itinerary that takes into account safety information for the travel destination based on the information input by the user. For example, it can provide a tour itinerary that includes security information and emergency contact information for the travel destination. It can also provide information on points to be aware of and dangerous areas at the travel destination, and give advice to the user to enjoy the trip safely. It can also provide information on medical facilities and pharmacies at the travel destination, allowing for quick response in the event of an emergency. This allows the user to enjoy their trip with peace of mind.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives the user's desired travel destination, itinerary, budget, and activities. The information input by the user includes, for example, specific travel destinations such as domestic travel, international travel, and specific cities or tourist spots, itinerary such as departure and return dates and length of the trip, total budget, daily budget, and currency unit, and specific activities such as sightseeing, sports, and cultural experiences. Step 2: The generation unit uses the generation AI to generate a tour itinerary based on the information received by the reception unit. The generation AI generates the tour itinerary using technologies such as neural networks and genetic algorithms. The generation unit generates the optimal tour itinerary based on the user's wishes, collects information on tourist spots, activities, accommodations, etc. at the travel destination, and generates the tour itinerary. Step 3: The providing unit provides the tour itinerary generated by the generating unit to the user. The user can check the tour itinerary and request corrections or additions as necessary. For example, the user can request corrections or additions such as changing the schedule or adding activities.

[0055] (Example 2) A tour creation system according to an embodiment of the present invention creates a recommended tour itinerary when a user inputs their desired travel destination, itinerary, budget, and activities. This tour creation system uses a generation AI to analyze the information and generate an optimal tour itinerary. This tour itinerary is customized based on the user's preferences and includes tourist attractions, activities, and accommodations at the travel destination. The generated tour itinerary is provided to the user, who can review it and request modifications or additions as needed. For example, a user inputs their desired travel destination, itinerary, budget, and activities. To do this, the user simply inputs their departure point and destination. For example, they might input "I want to go from my home to the train station." This information is then input into the generation AI. The generation AI then analyzes the input information and creates a video showing how to get from their current location to the destination. The generation AI calculates the optimal route based on map data and generates a video that follows that route. For example, if a user inputs a route from their home to the train station, a video that follows that route is generated. The generated video begins navigation based on the orientation of the user's smartphone. For example, if a user points their smartphone north, the video will also begin navigating north. This allows the user to navigate in the direction they are facing. Furthermore, the video on the screen moves in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to navigate at their own pace. This mechanism creates a simple structure that is easy to use for both children and the elderly, making it popular with everyone. Users can intuitively navigate without complex operations. Furthermore, since the viewpoint of the smartphone is the axis for all directions, they will not get lost, and since the smartphone is pointed horizontally, walking safety is ensured. For example, if a user is walking with their smartphone pointed horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This allows the tour generation system to generate and provide optimal tour itineraries based on the user's preferences.

[0056] A tour creation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user of a desired travel destination, itinerary, budget, and activities. The information input by the user includes, but is not limited to, specific travel destinations (e.g., domestic travel, overseas travel, specific cities, tourist attractions, etc.), itinerary (e.g., departure and return dates, length of travel), total budget, daily budget, and currency unit, and specific activities (e.g., sightseeing, sports, and cultural experiences). The generation unit uses a generation AI to generate a tour itinerary based on the information received by the reception unit. The generation AI generates the tour itinerary using, for example, techniques such as neural networks and genetic algorithms. The generation unit generates an optimal tour itinerary based on the user's preferences. The generation unit can also generate a tour itinerary by using the generation AI to collect information on tourist spots, activities, accommodations, etc. at the travel destination. For example, the generation AI may collect historical buildings, natural landscapes, theme parks, etc. as tourist spots, outdoor activities, cultural experiences, shopping, etc. as activities, and hotels, guesthouses, resorts, etc. as accommodations. The providing unit provides the tour itinerary generated by the generating unit to the user. For example, the providing unit allows the user to check the tour itinerary and request modifications or additions as necessary. For example, the providing unit allows the user to request modifications or additions, such as changes to the schedule or addition of activities. In this way, the tour generation system according to the embodiment can generate and provide an optimal tour itinerary based on the user's wishes.

[0057] The generation unit can generate a tour itinerary using a generation AI. The generation AI generates a tour itinerary using technologies such as neural networks and genetic algorithms. The generation AI generates an optimal tour itinerary based on the user's preferences. The generation AI analyzes user input information, selects optimal tourist spots, activities, and accommodations, and generates a tour itinerary. The generation AI adjusts the order in which tourist spots are visited and the length of stay based on the user's preferences to generate an efficient tour itinerary. The generation AI adjusts the type and duration of activities based on the user's preferences to generate a fulfilling tour itinerary. This improves the accuracy of generating a tour itinerary by using the generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit can input user input information to the generation AI, which then generates a tour itinerary.

[0058] The generation unit can collect information on tourist spots, activities, and accommodations at travel destinations and generate a tour itinerary. The generation unit, for example, collects information on historical buildings, natural landscapes, theme parks, and other tourist spots. The generation unit, for example, collects information on outdoor activities, cultural experiences, shopping, and other activities. The generation unit, for example, collects information on hotels, guesthouses, resorts, and other accommodations. The generation unit, for example, adjusts the order of visits and the length of stay based on the information on tourist spots to generate an efficient tour itinerary. The generation unit, for example, adjusts the type and time based on the information on activities to generate a fulfilling tour itinerary. The generation unit, for example, adjusts the location of accommodations and the length of stay based on the information on accommodations to generate a comfortable tour itinerary. In this way, by collecting detailed information on travel destinations, a more fulfilling tour itinerary can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information on tourist spots, activities, and accommodations into the generation AI, which then generates the tour itinerary.

[0059] The providing unit provides the tour itinerary to the user, and the user can request modifications or additions. The providing unit, for example, allows the user to check the tour itinerary and make modifications or additions as necessary. The providing unit, for example, allows the user to request modifications or additions, such as changing the schedule or adding an activity. The providing unit, for example, allows the user to check the contents of the tour itinerary and make modifications or additions as necessary. The providing unit, for example, allows the user to check the contents of the tour itinerary and make modifications or additions as necessary. This allows the user to check the tour itinerary and make modifications or additions as necessary. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's modifications or additions to the generation AI, and the generation AI can regenerate the tour itinerary.

[0060] The generation unit can customize the tour itinerary taking into account the user's wishes. The generation unit customizes the tour itinerary based on, for example, the user's wishes. The generation unit customizes the tour itinerary based on, for example, the user's preferences and past travel history. The generation unit generates an efficient tour itinerary by, for example, adjusting the order of visiting tourist spots and the length of stay based on the user's wishes. The generation unit generates a fulfilling tour itinerary by, for example, adjusting the type and time of activities based on the user's wishes. The generation unit generates a comfortable tour itinerary by, for example, adjusting the selection of accommodation and the length of stay based on the user's wishes. This enables customization according to the user's wishes. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's wishes into the generation AI, which then customizes the tour itinerary.

[0061] The providing unit can check the content of the tour itinerary provided to the user and request corrections or additions as necessary. The providing unit, for example, allows the user to check the content of the tour itinerary and request corrections or additions as necessary. The providing unit can, for example, allow the user to request corrections or additions, such as changing the schedule or adding an activity. The providing unit can, for example, allow the user to check the content of the tour itinerary and request corrections or additions as necessary. The providing unit can, for example, allow the user to check the content of the tour itinerary and request corrections or additions as necessary. This allows the user to check the content of the tour itinerary and request corrections or additions as necessary. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the providing unit can input the user's requests for corrections or additions to the generation AI, which can then regenerate the tour itinerary.

[0062] The reception unit can estimate the user's emotions and adjust the timing of input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of input to allow the user to relax. For example, if the user is excited, the reception unit can advance the timing of input to quickly collect information. For example, if the user is tired, the reception unit can adjust the timing of input to allow the user to take a break. This allows for more appropriate input by adjusting the timing of input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the timing of input based on the result.

[0063] The reception unit can analyze the user's past travel history and select the optimal input method. The reception unit can suggest an input method based on, for example, the user's past travel destinations and itineraries. The reception unit can analyze the user's preferred travel style from the user's past travel history and select the optimal input method. The reception unit can customize the input method based on, for example, the user's past budget and desired activities. This improves input efficiency by selecting the optimal input method based on the user's past travel history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI, which can select the optimal input method.

[0064] The reception unit can perform filtering based on the user's current living situation and areas of interest at the time of input. The reception unit, for example, considers the user's current living situation (work, family, etc.) to suggest appropriate travel destinations and itineraries. The reception unit, for example, considers the user's current living situation (work, family, etc.) to suggest relevant travel destinations and activities based on the user's areas of interest (history, nature, art, etc.). The reception unit, for example, considers the user's current health condition to suggest a reasonable travel plan. This enables more appropriate suggestions by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's living situation and areas of interest into the generation AI, which then performs filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit allows the user to input important information with priority. For example, when the user is relaxed, the reception unit allows the user to input detailed information. For example, when the user is in a hurry, the reception unit allows the user to input the minimum necessary information with priority. This enables more appropriate information input by determining the priority of information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which then estimates the emotion and determines the priority of information to be input based on the result.

[0066] The reception unit can prioritize input of highly relevant information taking into account the user's geographical location information when inputting information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. For example, the reception unit can cause the user to prioritize input of travel destinations that are easily accessible from the user's current location. For example, the reception unit can prioritize input of related tourist spots and activities based on the user's current location. This allows more relevant information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI, which can then prioritize input of highly relevant information.

[0067] The reception unit can analyze the user's social media activity at the time of input and input relevant information. The reception unit can suggest an input method based on, for example, travel destinations and activities shared by the user on social media. The reception unit can analyze, for example, the user's areas of interest on social media and allow the user to input relevant information. The reception unit can customize the input method by, for example, referring to travel-related accounts the user follows on social media. This allows more relevant information to be input by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then input relevant information.

[0068] The generation unit can estimate the user's emotions and adjust the presentation method of the tour itinerary based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a tour itinerary that proceeds at a leisurely pace. For example, if the user is in a hurry, the generation unit generates an efficient and short tour itinerary. For example, if the user is excited, the generation unit generates an active and stimulating tour itinerary. This allows for providing a more appropriate tour itinerary by adjusting the presentation method of the tour itinerary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the presentation method of the tour itinerary based on the result.

[0069] When generating a tour itinerary, the generation unit can adjust the level of detail of the itinerary based on the importance of the travel destination. For example, the generation unit generates an itinerary including detailed descriptions for major tourist spots. For example, the generation unit generates an itinerary including concise descriptions for minor tourist spots. For example, the generation unit adjusts the visiting time and the staying time according to the importance of the travel destination. In this way, by adjusting the level of detail of the itinerary based on the importance of the travel destination, a more appropriate tour itinerary can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance data of the travel destination into the generation AI, which can then adjust the level of detail of the itinerary.

[0070] When generating a tour itinerary, the generation unit can apply different generation algorithms depending on the category of the travel destination. For example, in the case of a natural tourist destination, the generation unit applies an algorithm that generates an itinerary that emphasizes natural scenery. For example, in the case of an urban tourist destination, the generation unit applies an algorithm that generates an itinerary that emphasizes the city's major tourist attractions. For example, in the case of a historical tourist destination, the generation unit applies an algorithm that generates an itinerary that emphasizes the historical background. In this way, by applying different generation algorithms depending on the category of the travel destination, a more appropriate tour itinerary can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the travel destination into the generation AI, and the generation AI can apply different generation algorithms.

[0071] The generation unit can estimate the user's emotion and adjust the length of the tour itinerary based on the estimated user's emotion. For example, if the user is relaxed, the generation unit generates a longer tour itinerary. For example, if the user is in a hurry, the generation unit generates a shorter tour itinerary. For example, if the user is excited, the generation unit generates a tour itinerary that includes more active activities. This allows the length of the tour itinerary to be adjusted according to the user's emotion, thereby providing a more appropriate tour itinerary. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the length of the tour itinerary based on the result.

[0072] When generating a tour itinerary, the generation unit can determine the priority of the processes based on the submission time of the travel destinations. For example, if the submission time of the travel destinations is early, the generation unit generates the processes with priority. For example, if the submission time of the travel destinations is late, the generation unit generates the processes later. For example, the generation unit adjusts the level of detail and priority of the processes according to the submission time. In this way, by determining the priority of the processes based on the submission time of the travel destinations, a more efficient tour itinerary can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input data on the submission time of the travel destinations into the generation AI, and the generation AI can determine the priority of the processes.

[0073] When generating a tour itinerary, the generation unit can adjust the order of the itineraries based on the relevance of the travel destinations. For example, if the relevance of the travel destinations is high, the generation unit generates itineraries in which the travel destinations are visited consecutively. For example, if the relevance of the travel destinations is low, the generation unit generates itineraries in which the travel destinations are visited at intervals. For example, the generation unit adjusts the order of visits and the duration of stay according to the relevance of the travel destinations. In this way, by adjusting the order of the itineraries based on the relevance of the travel destinations, a more efficient tour itinerary can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the travel destinations into the generation AI, which can then adjust the order of the itineraries.

[0074] The providing unit can estimate the user's emotions and adjust the tour itinerary presentation method based on the estimated user emotions. For example, if the user is relaxed, the providing unit selects a presentation method that includes detailed explanations. For example, if the user is in a hurry, the providing unit selects a concise and to-the-point presentation method. For example, if the user is excited, the providing unit selects a visually appealing presentation method. This allows the tour itinerary presentation method to be adjusted according to the user's emotions, thereby providing a more appropriate tour itinerary. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the tour itinerary presentation method based on the result.

[0075] When providing a tour process, the providing unit can select the optimal providing method by referring to the user's past travel history. For example, the providing unit selects the optimal providing method based on the providing method used by the user in the past. For example, the providing unit selects the preferred providing method from the user's past travel history. For example, the providing unit customizes the providing method by referring to travel destinations and activities used by the user in the past. This makes it possible to select a more appropriate providing method by referring to the user's past travel history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's past travel history data into the generation AI, which can select the optimal providing method.

[0076] The providing unit can customize the means of provision based on the user's current living situation when providing a tour process. The providing unit selects an appropriate provision method, for example, taking into consideration the user's current living situation (work, family, etc.). The providing unit selects a reasonable provision method, for example, taking into consideration the user's current health condition. The providing unit provides relevant information, for example, based on the user's current areas of interest. This enables more appropriate provision by customizing the means of provision based on the user's current living situation. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's living situation data into the generation AI, which then customizes the means of provision.

[0077] The providing unit can estimate the user's emotions and determine the priority of tour steps based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize steps that include detailed explanations. For example, if the user is in a hurry, the providing unit can prioritize steps that are concise and to the point. For example, if the user is excited, the providing unit can prioritize steps that are visually appealing. This allows the prioritization of tour steps according to the user's emotions, thereby providing a more appropriate tour step. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then estimate the emotion and determine the priority of the tour steps based on the estimation result.

[0078] When providing a tour process, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, the providing unit prioritizes providing travel destinations close to the user's current location. For example, the providing unit prioritizes providing travel destinations that are easily accessible from the user's current location. For example, the providing unit prioritizes providing related tourist spots and activities based on the user's current location. This allows a more appropriate providing method to be selected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI, which can select the optimal providing method.

[0079] When providing a tour process, the providing unit can analyze the user's social media activities and suggest a means of provision. The providing unit can suggest a means of provision based on, for example, travel destinations and activities shared by the user on social media. The providing unit can analyze, for example, the user's areas of interest on social media and provide related information. The providing unit can customize the means of provision based on, for example, travel-related accounts the user follows on social media. This makes it possible to suggest more appropriate means of provision by analyzing the user's social media activities. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI, which can then suggest a means of provision. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 reception device 38 of the smart device 14, and the user inputs their desired travel destination, itinerary, budget, and what they want to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour itinerary using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated tour itinerary to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 microphone 238 of the smart glasses 214, and the user inputs their desired travel destination, itinerary, budget, and what they want to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour itinerary using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated tour itinerary to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and the user inputs their desired travel destination, itinerary, budget, and what they want to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour itinerary using a generation AI. The provision unit is realized, for example, by the display 343 of the headset terminal 314, and provides the generated tour itinerary to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision 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 microphone 238 of the robot 414, and the user inputs their desired travel destination, itinerary, budget, and what they want to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a tour itinerary using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated tour itinerary to the user.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the information entered by the user in real time and provide instant feedback based on the input content. For example, when a user enters a travel destination, the latest tourist information and event information for that travel destination can be displayed instantly. Furthermore, when a user enters a budget, a list of activities and accommodations that can be enjoyed within that budget can be provided. Furthermore, when a user enters a travel date, the system can suggest the optimal travel plan to fit the date. This allows the user to receive instant feedback based on the input content, allowing for smoother travel planning.

[0082] The generation unit can generate a tour itinerary that takes into account weather information at the travel destination based on information input by the user. For example, if the weather at the travel destination is bad, indoor activities that can be enjoyed are prioritized. On the other hand, if the weather is good, a tour itinerary that includes many outdoor sightseeing and activities can be generated. Furthermore, the generation unit can provide the user with an optimal travel plan that takes into account special seasonal events and festivals. This allows the user to enjoy their trip regardless of the weather.

[0083] The generation unit can generate a tour itinerary that includes information about the culture and customs of the travel destination based on the user's input information. For example, the generation unit can suggest a tour itinerary that includes cultural events and traditional festivals of the travel destination. The generation unit can also introduce restaurants and markets where users can enjoy the travel destination's food culture and local specialties. Furthermore, the generation unit can provide information about the historical background and important tourist spots of the travel destination, allowing the user to gain a deeper understanding of the place. This allows the user to experience the travel destination's culture and customs more deeply.

[0084] The providing unit can display reviews and ratings from other users when the user checks the tour itinerary. For example, it can display ratings and comments from other users about specific tourist spots and activities for the user to refer to. It can also display reviews of accommodations and restaurants to help the user make their selections. Furthermore, it provides a function for users to post their own travel experiences as reviews, allowing them to share information with other users. This allows users to make better travel plans while taking into account the opinions of other users.

[0085] The generation unit can generate a tour itinerary that takes into account safety information for the travel destination based on the information input by the user. For example, it can provide a tour itinerary that includes security information and emergency contact information for the travel destination. It can also provide information on points to be aware of and dangerous areas at the travel destination, and give advice to the user to enjoy the trip safely. It can also provide information on medical facilities and pharmacies at the travel destination, allowing for quick response in the event of an emergency. This allows the user to enjoy their trip with peace of mind.

[0086] The reception unit can estimate the user's emotions and customize the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface is provided to reduce the burden of input. If the user is relaxed, an interface is provided that allows the user to input detailed information, allowing the user to make more detailed travel plans. If the user is excited, a visually appealing interface is provided to allow the user to enjoyably proceed with travel planning. In this way, it is possible to provide an optimal input interface according to the user's emotions.

[0087] The generation unit can estimate the user's emotions and adjust the flexibility of the tour itinerary based on the estimated user's emotions. For example, if the user is relaxed, a flexible tour itinerary that includes a lot of free time can be generated. If the user is in a hurry, an efficient tour itinerary that makes effective use of time can be generated. Furthermore, if the user is excited, a tour itinerary that includes a lot of active activities can be generated. This makes it possible to provide a flexible tour itinerary that corresponds to the user's emotions.

[0088] The providing unit can estimate the user's emotions and adjust the tour plan feedback method based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided to allow the user to carefully consider the travel plan. If the user is in a hurry, concise and to the point feedback can be provided to allow the user to quickly proceed with the travel plan. Furthermore, if the user is excited, visually appealing feedback can be provided to allow the user to enjoy the travel plan. In this way, it is possible to provide the optimal feedback method according to the user's emotions.

[0089] The reception unit can estimate the user's emotions and adjust the confirmation process for the input contents based on the estimated user emotions. For example, if the user is feeling stressed, the input contents can be automatically confirmed to reduce the user's burden. If the user is relaxed, a detailed confirmation process can be provided to allow the user to carefully confirm the input contents. Furthermore, if the user is excited, a visually appealing confirmation process can be provided to allow the user to enjoy planning their trip. In this way, it is possible to provide an optimal confirmation process according to the user's emotions.

[0090] The providing unit can estimate the user's emotions and adjust the sharing method of the tour steps based on the estimated user emotions. For example, if the user is relaxed, the providing unit selects a sharing method that includes detailed explanations. For example, if the user is in a hurry, the providing unit selects a sharing method that is concise and to the point. For example, if the user is excited, the providing unit selects a sharing method that is visually appealing. This allows the sharing method of the tour steps to be adjusted according to the user's emotions, thereby providing a more appropriate tour step. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which then estimates the emotion and adjusts the sharing method of the tour steps based on the result.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives the user's desired travel destination, itinerary, budget, and activities. The information input by the user includes, for example, specific travel destinations such as domestic travel, international travel, and specific cities or tourist spots, itinerary such as departure and return dates and length of the trip, total budget, daily budget, and currency unit, and specific activities such as sightseeing, sports, and cultural experiences. Step 2: The generation unit uses the generation AI to generate a tour itinerary based on the information received by the reception unit. The generation AI generates the tour itinerary using technologies such as neural networks and genetic algorithms. The generation unit generates the optimal tour itinerary based on the user's wishes, collects information on tourist spots, activities, accommodations, etc. at the travel destination, and generates the tour itinerary. Step 3: The providing unit provides the tour itinerary generated by the generating unit to the user. The user can check the tour itinerary and request corrections or additions as necessary. For example, the user can request corrections or additions such as changing the schedule or adding activities.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 desk that accepts input of desired travel destinations, dates, budgets, and specific activities; a generation unit that generates a tour itinerary based on the information received by the reception unit; a providing unit that provides the tour process generated by the generating unit. A system characterized by:

2. The generation unit Generate tour itineraries using AI The system of claim 1 .

3. The generation unit Collect information on tourist attractions, activities, and accommodations at the travel destination and create a tour itinerary The system of claim 1 .

4. The providing unit Provides users with a tour of the process and allows them to make corrections or request additions The system of claim 1 .

5. The generation unit Customize the tour process based on user preferences The system of claim 1 .

6. The providing unit Review the tour steps provided to the user and request corrections or additions as necessary The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the timing of input 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 optimal input method The system of claim 1 .

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