System

Abstract

An object of a system according to an embodiment is to enable even a user who finds it troublesome to make a travel plan to easily create a travel plan.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The receiving unit receives an input from a user. The generation unit generates a travel plan based on the information received by the reception unit. The providing section provides the travel plan generated by the generation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users who find it troublesome to make travel plans to efficiently create travel plans.

[0005] The system according to the embodiment aims to enable even users who find it troublesome to make travel plans to easily create them. [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 user input. The generation unit generates an itinerary based on the information received by the reception unit. The provision unit provides the itinerary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable even users who find it difficult to plan their trips to easily create a trip plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel plan generation system according to an embodiment of the present invention generates and provides travel plans based on user input. The travel plan generation system inputs information such as an image of places the user would like to visit, things they would like to do, travel duration, budget, and personal information. For example, the user inputs information such as "I want to relax somewhere with an ocean view," "A three-day trip," and "My budget is 50,000 yen." This information is then input into a generation AI. The generation AI then analyzes the input information and creates a travel plan. The generation AI generates a detailed travel plan including destinations, transportation, length of stay, and information about nearby shops. For example, information such as "Destination: City X, transportation: Train, length of stay: 2 nights and 3 days, nearby shop: Cafe X" is included. Furthermore, for users who enjoy making travel plans, the generation AI suggests additional destinations and optimal routes based on the already input travel plan. For example, suggestions such as "After City X, it's a good idea to go to City Y" or "The best route to City Y" are suggested. This system allows even users who find it difficult to plan travel plans to easily create travel plans, while providing additional suggestions to users who enjoy making travel plans, improving the satisfaction of everyone's travel experience. This allows the travel plan generation system to generate and provide travel plans based on user input. For example, even people who find it difficult to plan trips can easily create travel plans, and people who enjoy planning trips can receive additional suggestions, improving the satisfaction of everyone's travel experience.

[0029] An itinerary generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives user input. The user input may include, but is not limited to, an image of places the user would like to visit, things the user would like to do, travel duration, budget, and personal information. The reception unit may receive user input in the form of, for example, text input, voice input, or image input. The generation unit uses a generation AI to generate an itinerary based on the information received by the reception unit. The generated itinerary may include, but is not limited to, destinations, transportation options, length of stay, and nearby store information. The generation unit may also generate an itinerary taking into account, for example, the user's preferences and past travel history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the user's input information to generate an optimal itinerary. The provision unit provides the itinerary generated by the generation unit. The provision unit may also suggest, for example, additional destinations and optimal routes based on the generated itinerary. The provision unit uses the generation AI to provide the itinerary to the user and make further suggestions. As a result, the travel plan generation system according to the embodiment can generate and provide travel plans based on user input. For example, even people who find it troublesome to make travel plans can easily create travel plans, and people who enjoy making travel plans can receive additional suggestions, improving the satisfaction of everyone's travel experience.

[0030] The generation unit can generate a travel plan based on the user's preferences and past travel history. The generation unit, for example, generates a travel plan taking the user's preferences into consideration. For example, the generation unit proposes an optimal travel plan based on the user's previously visited places and preferred activities. The generation unit can also generate a travel plan taking the user's past travel history into consideration. For example, the generation unit proposes similar travel destinations and activities based on the user's past reservation information and records of visited places. This allows a more appropriate travel plan to be generated by taking the user's preferences and past travel history into consideration. 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 user's preferences and past travel history into the generation AI and cause the generation AI to generate an optimal travel plan.

[0031] The providing unit can suggest additional destinations and optimal routes based on the generated travel plan. The providing unit, for example, suggests additional destinations based on the generated travel plan. For example, the providing unit suggests nearby tourist attractions and activities in addition to the destinations in the generated travel plan. The providing unit can also suggest the best route. For example, the providing unit suggests optimal transportation means and routes taking into account shortening travel time and saving on transportation costs. This improves satisfaction with the travel experience by suggesting additional destinations and optimal routes based on the generated travel plan. 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 generated travel plan into the generation AI and cause the generation AI to suggest additional destinations and optimal routes.

[0032] The reception unit allows the user to input an image of a place they would like to go or something they would like to do, their travel period, their budget, their personal information, and the like. The reception unit, for example, inputs an image of a place the user would like to go. For example, the reception unit may accept a user input of an image such as "I want to relax in a place with an ocean view." The reception unit may also accept a user input of an activity such as "I want to go skydiving." The reception unit may also input a travel period. For example, the reception unit may accept a user input of a duration such as "a three-day trip." The reception unit may also input a budget. For example, the reception unit may accept a user input of an amount such as "My budget is 50,000 yen." The reception unit may also input personal information. For example, the reception unit may accept a user input of personal information such as their name, contact information, and age. By inputting an image of a place they would like to go or something they would like to do, their travel period, their budget, their personal information, and the like, the information necessary for generating a travel plan can be collected. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without a generation AI. For example, the reception unit can input user input information to the generation AI and have the generation AI analyze the information.

[0033] The generation unit can generate a detailed travel plan including destinations, transportation methods, duration of stay, and information on nearby shops. The generation unit, for example, generates a travel plan including destinations. For example, the generation unit suggests optimal destinations based on user input information. The generation unit can also generate a travel plan including transportation methods. For example, the generation unit suggests optimal transportation methods taking into account the user's budget and travel time. The generation unit can also generate a travel plan including duration of stay. For example, the generation unit suggests optimal duration of stay based on the user's travel period. The generation unit can also generate a travel plan including information on nearby shops. For example, the generation unit suggests information on restaurants, cafes, shopping spots, etc. near the destination. By generating a detailed travel plan, a specific travel plan can be provided to the user. 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 can input user input information to the generation AI and cause the generation AI to generate a detailed travel plan.

[0034] The reception unit can analyze the user's past input history and suggest the best input method. The reception unit, for example, analyzes the user's past input history. For example, the reception unit automatically displays travel destinations and activities previously input by the user as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel destinations related to specific seasons or events from the user's past input history. For example, the reception unit suggests the optimal input method based on information previously input by the user. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI suggest the optimal input method.

[0035] The reception unit can acquire the user's current location information and present input candidates based on the location. The reception unit, for example, acquires the user's current location information. For example, the reception unit can acquire the user's current location using GPS data. The reception unit can also acquire the user's current location using Wi-Fi location information or an IP address. The reception unit then presents input candidates based on the acquired location information. For example, when the user inputs their current location, the reception unit can automatically acquire the current location and set it as a starting point. Furthermore, when the user inputs a destination, the reception unit can suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the starting point. In this way, by acquiring the user's current location information, input candidates based on the location can be presented. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to present input candidates based on the location.

[0036] The reception unit can analyze the user's input content in real time and automatically complete related input candidates. The reception unit, for example, analyzes the user's input content in real time. For example, if the user inputs "sea," the reception unit can automatically complete related travel destinations and activities. Furthermore, if the user inputs "relaxation," the reception unit can automatically complete related travel destinations and accommodations. Furthermore, if the user inputs "3 days," the reception unit can automatically complete appropriate travel destinations and plans. This allows related input candidates to be automatically completed by analyzing the user's input content in real time. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input content to the generation AI and have the generation AI perform automatic completion of related input candidates.

[0037] The reception unit can analyze the user's social media activity and present related travel destinations and activities as input candidates. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related travel destinations and activities. Furthermore, the reception unit can also suggest related travel destinations and activities by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related travel destinations and activities can be presented as input candidates. 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 the user's social media activity into the generation AI and cause the generation AI to suggest related travel destinations and activities.

[0038] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit, for example, reflects the user's past feedback. For example, the reception unit adjusts the interface design based on feedback provided by the user in the past. The reception unit can also resolve problems previously pointed out by the user and provide an easy-to-use interface. Furthermore, the reception unit can optimize the input procedure and provide an efficient input method based on the user's past feedback. In this way, the input interface can be customized by reflecting the user's past feedback. 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 the user's past feedback into the generation AI and cause the generation AI to customize the interface.

[0039] The reception unit can select the best input method based on the user's device information. The reception unit, for example, considers the user's device information. For example, if the user is using a smartphone, the reception unit can provide an input method optimized for touch operation. Furthermore, if the user is using a tablet, the reception unit can provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can provide an input method optimized for keyboard input. This allows the optimal input method to be selected by considering the user's device information. 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 the user's device information to the generation AI and cause the generation AI to select the optimal input method.

[0040] During generation, the generation unit can generate the best travel plan by referring to the user's past travel history. The generation unit, for example, refers to the user's past travel history. For example, the generation unit may suggest similar travel destinations based on places the user has visited in the past. The generation unit can also generate a travel plan that includes preferred activities from the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the travel plan that will provide the highest level of satisfaction. In this way, the optimal travel plan can be generated by referring to the user's past travel history. 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 may input the user's past travel history into the generation AI and cause the generation AI to generate the best travel plan.

[0041] The generation unit can customize the travel plan based on the user's current living situation and areas of interest during generation. The generation unit, for example, takes into account the user's current living situation. For example, the generation unit generates a travel plan that allows the user to relax based on their current living situation. The generation unit can also generate a plan that includes related travel destinations and activities based on the user's areas of interest (history, nature, art, etc.). Furthermore, the generation unit can generate an optimal travel plan by taking into account the user's current living situation and areas of interest. This makes it possible to provide a more appropriate travel plan by taking into account the user's current living situation and areas of interest. 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 user's current living situation and areas of interest into the generation AI and have the generation AI customize the travel plan.

[0042] The generation unit can improve the accuracy of the travel plan by reflecting user feedback during generation. The generation unit, for example, reflects user feedback. For example, the generation unit adjusts the content of the travel plan based on feedback previously provided by the user. The generation unit can also generate a more satisfying travel plan by reflecting user feedback. Furthermore, the generation unit can improve the accuracy of the travel plan based on user feedback. In this way, the accuracy of the travel plan can be improved by reflecting user feedback. Some or all of the above-mentioned 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 user feedback into the generation AI and cause the generation AI to improve the accuracy of the travel plan.

[0043] The generation unit can generate the best travel plan based on the user's geographical location information during generation. The generation unit, for example, takes into account the user's geographical location information. For example, the generation unit suggests travel destinations close to the user's current location. The generation unit can also suggest the best means of transportation based on the user's geographical location information. Furthermore, the generation unit can generate the best travel plan by taking into account the user's geographical location information. In this way, the best travel plan can be generated by taking into account the user's geographical location information. 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 user's geographical location information into the generation AI and cause the generation AI to generate the best travel plan.

[0044] During generation, the generation unit can analyze the user's social media activity and incorporate related travel destinations and activities into the plan. The generation unit, for example, analyzes the user's social media activity. For example, the generation unit incorporates places where the user has checked in on social media into the travel plan. The generation unit can also analyze the user's social media posts and incorporate related travel destinations and activities into the plan. Furthermore, the generation unit can incorporate related travel destinations and activities into the plan by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related travel destinations and activities can be incorporated into the plan. 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 can input the user's social media activity into the generation AI and cause the generation AI to incorporate related travel destinations and activities.

[0045] The generation unit can customize the travel plan by reflecting the user's past feedback when generating the travel plan. The generation unit, for example, reflects the user's past feedback. For example, the generation unit adjusts the content of the travel plan based on feedback provided by the user in the past. The generation unit can also generate a more satisfying travel plan by reflecting the user's feedback. Furthermore, the generation unit can improve the accuracy of the travel plan based on the user's feedback. In this way, the travel plan can be customized by reflecting the user's past feedback. Some or all of the above-mentioned 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 the user's past feedback into the generation AI and have the generation AI customize the travel plan.

[0046] When providing the display information, the providing unit can select the best display method by referring to the user's past travel history. The providing unit, for example, refers to the user's past travel history. For example, the providing unit provides the best display method based on display methods used by the user in the past. The providing unit can also suggest a preferred display method from the user's past travel history. Furthermore, the providing unit can analyze the user's past travel history and provide the display method that provides the highest satisfaction. In this way, the best display method can be selected by referring to the user's past travel history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past travel history into the generation AI and cause the generation AI to select the best display method.

[0047] The providing unit can customize the display content by taking into account the user's current living situation and areas of interest when providing the display content. The providing unit, for example, takes into account the user's current living situation. For example, the providing unit displays a relaxing travel plan based on the user's current living situation. The providing unit can also display related travel destinations and activities based on the user's areas of interest (history, nature, art, etc.). Furthermore, the providing unit can display an optimal travel plan by taking into account the user's current living situation and areas of interest. This makes it possible to provide more appropriate display content by taking into account the user's current living situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's current living situation and areas of interest into the generation AI and cause the generation AI to customize the display content.

[0048] The providing unit can improve the display method by reflecting user feedback when providing the display. The providing unit, for example, reflects user feedback. For example, the providing unit adjusts the display method based on feedback previously provided by the user. The providing unit can also provide a display method with higher visibility by reflecting user feedback. Furthermore, the providing unit can also optimize the display content based on user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, or without using, a generation AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the display method.

[0049] The providing unit can select the best display method based on the user's geographical location information when providing the information. The providing unit, for example, takes into account the user's geographical location information. For example, the providing unit prioritizes displaying travel destinations close to the user's current location. The providing unit can also display the best means of transportation based on the user's geographical location information. Furthermore, the providing unit can display the best travel plan taking into account the user's geographical location information. In this way, the best display method can be selected by taking into account the user's geographical location information. 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 to the generation AI and cause the generation AI to select the best display method.

[0050] At the time of providing, the providing unit can analyze the user's social media activity and display related travel destinations and activities. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit displays places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and display related travel destinations and activities. Furthermore, the providing unit can display related travel destinations and activities by referring to the activities of the user's friends on social media. In this way, related travel destinations and activities can be displayed by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to display related travel destinations and activities.

[0051] The providing unit can customize the display method by reflecting the user's past feedback when providing the content. The providing unit, for example, reflects the user's past feedback. For example, the providing unit adjusts the display method based on feedback provided by the user in the past. The providing unit can also provide a display method with higher visibility by reflecting the user's feedback. Furthermore, the providing unit can also optimize the display content based on the user's feedback. In this way, the display method can be customized by reflecting the user's past feedback. 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 past feedback into the generation AI and cause the generation AI to customize the display method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can also take the user's current health condition into consideration when receiving user input. For example, if the user inputs information about their health condition, the reception unit can adjust the travel plan based on that information. Specifically, if the user is tired, it can suggest travel destinations and activities that will help them relax. Also, if the user is active, it can suggest a travel plan that includes many active activities. Furthermore, if the user has a specific health problem, it can provide a travel plan that addresses that problem. This makes it possible to provide the optimal travel plan based on the user's health condition.

[0054] The generation unit can minimize the environmental impact of the travel plan based on the information input by the user. For example, the generation unit can preferentially suggest eco-friendly means of transportation and accommodations. The generation unit can also suggest activities that respect the local culture and natural environment. Furthermore, if the user desires an environmentally friendly trip, the generation unit can generate a travel plan that meets the user's desire. This makes it possible to provide an environmentally friendly travel plan.

[0055] The providing unit can provide safety information for the user during the trip based on the generated travel plan. For example, the providing unit can provide security information and emergency contact information for the travel destination. The providing unit can also provide health risks and vaccination information for the travel destination. Furthermore, the providing unit can also provide weather information and natural disaster risks during the trip. This makes it possible to provide information for the user to enjoy the trip safely.

[0056] The reception unit can provide customization options for the travel plan based on the information input by the user. For example, if the user requests a travel plan based on a specific theme, the reception unit can provide options according to that theme. In addition, if the user has specific dietary restrictions or allergies, the reception unit can provide a travel plan that takes that information into consideration. Furthermore, if the user has specific interests or hobbies, the reception unit can suggest activities that match those interests or hobbies. This makes it possible to provide a travel plan that meets the user's individual needs.

[0057] The generation unit can optimize the cost of the travel plan based on the information input by the user. For example, the generation unit can suggest the most suitable accommodations and means of transportation according to the user's budget. Furthermore, if the user wants to keep costs down, the generation unit can also provide a travel plan that utilizes discount information and special offers. Furthermore, if the user desires a luxury trip, the generation unit can also suggest luxury accommodations and activities that meet the user's desires. This makes it possible to provide the most suitable travel plan according to the user's budget.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives user input. The user input includes, for example, an image of a place they would like to go, things they would like to do, travel duration, budget, personal information, etc. The reception unit can receive user input in the form of text input, voice input, image input, etc. Step 2: The generation unit uses a generation AI to generate a travel plan based on the information received by the reception unit. The generated travel plan includes destinations, transportation methods, length of stay, and information about nearby shops. The generation unit can also generate a travel plan taking into account the user's preferences and past travel history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the information input by the user to generate an optimal travel plan. Step 3: The providing unit provides the travel plan generated by the generating unit. The providing unit can also suggest additional destinations and optimal routes based on the generated travel plan. The providing unit uses the generating AI to provide the travel plan to the user and make further suggestions.

[0060] (Example 2) A travel plan generation system according to an embodiment of the present invention generates and provides travel plans based on user input. The travel plan generation system inputs information such as an image of places the user would like to visit, things they would like to do, travel duration, budget, and personal information. For example, the user inputs information such as "I want to relax somewhere with an ocean view," "A three-day trip," and "My budget is 50,000 yen." This information is then input into a generation AI. The generation AI then analyzes the input information and creates a travel plan. The generation AI generates a detailed travel plan including destinations, transportation, length of stay, and information about nearby shops. For example, information such as "Destination: City X, transportation: Train, length of stay: 2 nights and 3 days, nearby shop: Cafe X" is included. Furthermore, for users who enjoy making travel plans, the generation AI suggests additional destinations and optimal routes based on the already input travel plan. For example, suggestions such as "After City X, it's a good idea to go to City Y" or "The best route to City Y" are suggested. This system allows even users who find it difficult to plan travel plans to easily create travel plans, while providing additional suggestions to users who enjoy making travel plans, improving the satisfaction of everyone's travel experience. This allows the travel plan generation system to generate and provide travel plans based on user input. For example, even people who find it difficult to plan trips can easily create travel plans, and people who enjoy planning trips can receive additional suggestions, improving the satisfaction of everyone's travel experience.

[0061] An itinerary generation system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives user input. The user input may include, but is not limited to, an image of places the user would like to visit, things the user would like to do, travel duration, budget, and personal information. The reception unit may receive user input in the form of, for example, text input, voice input, or image input. The generation unit uses a generation AI to generate an itinerary based on the information received by the reception unit. The generated itinerary may include, but is not limited to, destinations, transportation options, length of stay, and nearby store information. The generation unit may also generate an itinerary taking into account, for example, the user's preferences and past travel history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the user's input information to generate an optimal itinerary. The provision unit provides the itinerary generated by the generation unit. The provision unit may also suggest, for example, additional destinations and optimal routes based on the generated itinerary. The provision unit uses the generation AI to provide the itinerary to the user and make further suggestions. As a result, the travel plan generation system according to the embodiment can generate and provide travel plans based on user input. For example, even people who find it troublesome to make travel plans can easily create travel plans, and people who enjoy making travel plans can receive additional suggestions, improving the satisfaction of everyone's travel experience.

[0062] The generation unit can generate a travel plan based on the user's preferences and past travel history. The generation unit, for example, generates a travel plan taking the user's preferences into consideration. For example, the generation unit proposes an optimal travel plan based on the user's previously visited places and preferred activities. The generation unit can also generate a travel plan taking the user's past travel history into consideration. For example, the generation unit proposes similar travel destinations and activities based on the user's past reservation information and records of visited places. This allows a more appropriate travel plan to be generated by taking the user's preferences and past travel history into consideration. 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 user's preferences and past travel history into the generation AI and cause the generation AI to generate an optimal travel plan.

[0063] The providing unit can suggest additional destinations and optimal routes based on the generated travel plan. The providing unit, for example, suggests additional destinations based on the generated travel plan. For example, the providing unit suggests nearby tourist attractions and activities in addition to the destinations in the generated travel plan. The providing unit can also suggest the best route. For example, the providing unit suggests optimal transportation means and routes taking into account shortening travel time and saving on transportation costs. This improves satisfaction with the travel experience by suggesting additional destinations and optimal routes based on the generated travel plan. 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 generated travel plan into the generation AI and cause the generation AI to suggest additional destinations and optimal routes.

[0064] The reception unit allows the user to input an image of a place they would like to go or something they would like to do, their travel period, their budget, their personal information, and the like. The reception unit, for example, inputs an image of a place the user would like to go. For example, the reception unit may accept a user input of an image such as "I want to relax in a place with an ocean view." The reception unit may also accept a user input of an activity such as "I want to go skydiving." The reception unit may also input a travel period. For example, the reception unit may accept a user input of a duration such as "a three-day trip." The reception unit may also input a budget. For example, the reception unit may accept a user input of an amount such as "My budget is 50,000 yen." The reception unit may also input personal information. For example, the reception unit may accept a user input of personal information such as their name, contact information, and age. By inputting an image of a place they would like to go or something they would like to do, their travel period, their budget, their personal information, and the like, the information necessary for generating a travel plan can be collected. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without a generation AI. For example, the reception unit can input user input information to the generation AI and have the generation AI analyze the information.

[0065] The generation unit can generate a detailed travel plan including destinations, transportation methods, duration of stay, and information on nearby shops. The generation unit, for example, generates a travel plan including destinations. For example, the generation unit suggests optimal destinations based on user input information. The generation unit can also generate a travel plan including transportation methods. For example, the generation unit suggests optimal transportation methods taking into account the user's budget and travel time. The generation unit can also generate a travel plan including duration of stay. For example, the generation unit suggests optimal duration of stay based on the user's travel period. The generation unit can also generate a travel plan including information on nearby shops. For example, the generation unit suggests information on restaurants, cafes, shopping spots, etc. near the destination. By generating a detailed travel plan, a specific travel plan can be provided to the user. 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 can input user input information to the generation AI and cause the generation AI to generate a detailed travel plan.

[0066] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. The reception unit can also analyze the user's text input and estimate the emotion. For example, the reception unit can analyze the user's input content and input speed to estimate the emotion. Next, the reception unit adjusts the display method of the input interface based on the estimated user emotion. For example, if the user is stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This improves the user's input experience by adjusting the display method of the input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0067] The reception unit can analyze the user's past input history and suggest the best input method. The reception unit, for example, analyzes the user's past input history. For example, the reception unit automatically displays travel destinations and activities previously input by the user as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest travel destinations related to specific seasons or events from the user's past input history. For example, the reception unit suggests the optimal input method based on information previously input by the user. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI suggest the optimal input method.

[0068] The reception unit can acquire the user's current location information and present input candidates based on the location. The reception unit, for example, acquires the user's current location information. For example, the reception unit can acquire the user's current location using GPS data. The reception unit can also acquire the user's current location using Wi-Fi location information or an IP address. The reception unit then presents input candidates based on the acquired location information. For example, when the user inputs their current location, the reception unit can automatically acquire the current location and set it as a starting point. Furthermore, when the user inputs a destination, the reception unit can suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the starting point. In this way, by acquiring the user's current location information, input candidates based on the location can be presented. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to present input candidates based on the location.

[0069] The reception unit can analyze the user's input content in real time and automatically complete related input candidates. The reception unit, for example, analyzes the user's input content in real time. For example, if the user inputs "sea," the reception unit can automatically complete related travel destinations and activities. Furthermore, if the user inputs "relaxation," the reception unit can automatically complete related travel destinations and accommodations. Furthermore, if the user inputs "3 days," the reception unit can automatically complete appropriate travel destinations and plans. This allows related input candidates to be automatically completed by analyzing the user's input content in real time. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input content to the generation AI and have the generation AI perform automatic completion of related input candidates.

[0070] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. The reception unit can also analyze the user's text input and estimate the emotions. For example, the reception unit analyzes the user's input content and input speed to estimate the emotions. Next, the reception unit prioritizes the input content based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize input of important information and postpone detailed information. Also, if the user is relaxed, the reception unit can prioritize input of detailed information and propose a customizable plan. Furthermore, if the user is in a hurry, the reception unit can prioritize input of the minimum necessary information and quickly generate a plan. This enables efficient input by prioritizing input content based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0071] The reception unit can analyze the user's social media activity and present related travel destinations and activities as input candidates. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit suggests places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related travel destinations and activities. Furthermore, the reception unit can also suggest related travel destinations and activities by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related travel destinations and activities can be presented as input candidates. 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 the user's social media activity into the generation AI and cause the generation AI to suggest related travel destinations and activities.

[0072] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit, for example, reflects the user's past feedback. For example, the reception unit adjusts the interface design based on feedback provided by the user in the past. The reception unit can also resolve problems previously pointed out by the user and provide an easy-to-use interface. Furthermore, the reception unit can optimize the input procedure and provide an efficient input method based on the user's past feedback. In this way, the input interface can be customized by reflecting the user's past feedback. 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 the user's past feedback into the generation AI and cause the generation AI to customize the interface.

[0073] The reception unit can select the best input method based on the user's device information. The reception unit, for example, considers the user's device information. For example, if the user is using a smartphone, the reception unit can provide an input method optimized for touch operation. Furthermore, if the user is using a tablet, the reception unit can provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can provide an input method optimized for keyboard input. This allows the optimal input method to be selected by considering the user's device information. 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 the user's device information to the generation AI and cause the generation AI to select the optimal input method.

[0074] The generation unit can estimate the user's emotions and adjust the contents of the travel plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. The generation unit can also analyze the user's text input and estimate the emotions. For example, the generation unit can analyze the user's input content and input speed to estimate the emotions. Next, the generation unit adjusts the contents of the travel plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a travel plan with a leisurely schedule. If the user is excited, the generation unit can generate a travel plan that includes many active activities. Furthermore, if the user is tired, the generation unit can generate a travel plan that includes many relaxing places and activities. This allows the generation of a more appropriate travel plan by adjusting the contents of the travel plan based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0075] During generation, the generation unit can generate the best travel plan by referring to the user's past travel history. The generation unit, for example, refers to the user's past travel history. For example, the generation unit may suggest similar travel destinations based on places the user has visited in the past. The generation unit can also generate a travel plan that includes preferred activities from the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the travel plan that will provide the highest level of satisfaction. In this way, the optimal travel plan can be generated by referring to the user's past travel history. 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 may input the user's past travel history into the generation AI and cause the generation AI to generate the best travel plan.

[0076] The generation unit can customize the travel plan based on the user's current living situation and areas of interest during generation. The generation unit, for example, takes into account the user's current living situation. For example, the generation unit generates a travel plan that allows the user to relax based on their current living situation. The generation unit can also generate a plan that includes related travel destinations and activities based on the user's areas of interest (history, nature, art, etc.). Furthermore, the generation unit can generate an optimal travel plan by taking into account the user's current living situation and areas of interest. This makes it possible to provide a more appropriate travel plan by taking into account the user's current living situation and areas of interest. 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 user's current living situation and areas of interest into the generation AI and have the generation AI customize the travel plan.

[0077] The generation unit can improve the accuracy of the travel plan by reflecting user feedback during generation. The generation unit, for example, reflects user feedback. For example, the generation unit adjusts the content of the travel plan based on feedback previously provided by the user. The generation unit can also generate a more satisfying travel plan by reflecting user feedback. Furthermore, the generation unit can improve the accuracy of the travel plan based on user feedback. In this way, the accuracy of the travel plan can be improved by reflecting user feedback. Some or all of the above-mentioned 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 user feedback into the generation AI and cause the generation AI to improve the accuracy of the travel plan.

[0078] The generation unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. The generation unit can also analyze the user's text input and estimate the emotions. For example, the generation unit can analyze the user's input content and input speed to estimate the emotions. Next, the generation unit prioritizes travel plans based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that prioritizes relaxing activities. If the user is excited, the generation unit can generate a travel plan that prioritizes active activities. Furthermore, if the user is tired, the generation unit can generate a travel plan that prioritizes relaxing places and activities. This allows the system to provide a more appropriate travel plan by prioritizing travel plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0079] The generation unit can generate the best travel plan based on the user's geographical location information during generation. The generation unit, for example, takes into account the user's geographical location information. For example, the generation unit suggests travel destinations close to the user's current location. The generation unit can also suggest the best means of transportation based on the user's geographical location information. Furthermore, the generation unit can generate the best travel plan by taking into account the user's geographical location information. In this way, the best travel plan can be generated by taking into account the user's geographical location information. 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 user's geographical location information into the generation AI and cause the generation AI to generate the best travel plan.

[0080] During generation, the generation unit can analyze the user's social media activity and incorporate related travel destinations and activities into the plan. The generation unit, for example, analyzes the user's social media activity. For example, the generation unit incorporates places where the user has checked in on social media into the travel plan. The generation unit can also analyze the user's social media posts and incorporate related travel destinations and activities into the plan. Furthermore, the generation unit can incorporate related travel destinations and activities into the plan by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related travel destinations and activities can be incorporated into the plan. 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 can input the user's social media activity into the generation AI and cause the generation AI to incorporate related travel destinations and activities.

[0081] The generation unit can customize the travel plan by reflecting the user's past feedback when generating the travel plan. The generation unit, for example, reflects the user's past feedback. For example, the generation unit adjusts the content of the travel plan based on feedback provided by the user in the past. The generation unit can also generate a more satisfying travel plan by reflecting the user's feedback. Furthermore, the generation unit can improve the accuracy of the travel plan based on the user's feedback. In this way, the travel plan can be customized by reflecting the user's past feedback. Some or all of the above-mentioned 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 the user's past feedback into the generation AI and have the generation AI customize the travel plan.

[0082] The providing unit can estimate the user's emotions and adjust the display method of the travel plan based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. The providing unit can also analyze the user's text input and estimate the emotions. For example, the providing unit can analyze the user's input content and input speed to estimate the emotions. Next, the providing unit adjusts the display method of the travel plan based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the travel plan to be adjusted based on the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 the generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0083] When providing the display information, the providing unit can select the best display method by referring to the user's past travel history. The providing unit, for example, refers to the user's past travel history. For example, the providing unit provides the best display method based on display methods used by the user in the past. The providing unit can also suggest a preferred display method from the user's past travel history. Furthermore, the providing unit can analyze the user's past travel history and provide the display method that provides the highest satisfaction. In this way, the best display method can be selected by referring to the user's past travel history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past travel history into the generation AI and cause the generation AI to select the best display method.

[0084] The providing unit can customize the display content by taking into account the user's current living situation and areas of interest when providing the display content. The providing unit, for example, takes into account the user's current living situation. For example, the providing unit displays a relaxing travel plan based on the user's current living situation. The providing unit can also display related travel destinations and activities based on the user's areas of interest (history, nature, art, etc.). Furthermore, the providing unit can display an optimal travel plan by taking into account the user's current living situation and areas of interest. This makes it possible to provide more appropriate display content by taking into account the user's current living situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's current living situation and areas of interest into the generation AI and cause the generation AI to customize the display content.

[0085] The providing unit can improve the display method by reflecting user feedback when providing the display. The providing unit, for example, reflects user feedback. For example, the providing unit adjusts the display method based on feedback previously provided by the user. The providing unit can also provide a display method with higher visibility by reflecting user feedback. Furthermore, the providing unit can also optimize the display content based on user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, or without using, a generation AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the display method.

[0086] The providing unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. The providing unit can also analyze the user's text input and estimate the emotions. For example, the providing unit can analyze the user's input content and input speed to estimate the emotions. Next, the providing unit prioritizes travel plans based on the estimated user emotions. For example, if the user is relaxed, relaxing activities can be prioritized. Also, if the user is excited, active activities can be prioritized. Furthermore, if the user is tired, relaxing places and activities can be prioritized. This allows the system to provide more appropriate travel plans by prioritizing travel plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 the generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0087] The providing unit can select the best display method based on the user's geographical location information when providing the information. The providing unit, for example, takes into account the user's geographical location information. For example, the providing unit prioritizes displaying travel destinations close to the user's current location. The providing unit can also display the best means of transportation based on the user's geographical location information. Furthermore, the providing unit can display the best travel plan taking into account the user's geographical location information. In this way, the best display method can be selected by taking into account the user's geographical location information. 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 to the generation AI and cause the generation AI to select the best display method.

[0088] At the time of providing, the providing unit can analyze the user's social media activity and display related travel destinations and activities. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit displays places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and display related travel destinations and activities. Furthermore, the providing unit can display related travel destinations and activities by referring to the activities of the user's friends on social media. In this way, related travel destinations and activities can be displayed by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to display related travel destinations and activities.

[0089] The providing unit can customize the display method by reflecting the user's past feedback when providing the content. The providing unit, for example, reflects the user's past feedback. For example, the providing unit adjusts the display method based on feedback provided by the user in the past. The providing unit can also provide a display method with higher visibility by reflecting the user's feedback. Furthermore, the providing unit can also optimize the display content based on the user's feedback. In this way, the display method can be customized by reflecting the user's past feedback. 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 past feedback into the generation AI and cause the generation AI to customize the display method. === 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 receives user input. For example, text input can be received using a touch panel 38A, and voice input can be received using a microphone 38B. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a travel plan 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 travel plan to the user. For example, text and images can be displayed using the display 40A, and voice guidance can be provided using the speaker 40B. === 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 receives voice input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated travel plan to the user by voice. For example, the camera 42 can be used to detect the user's facial expression and make further suggestions based on the generated plan. === 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-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and displays the generated travel plan to the user. For example, audio guidance can be provided using the speaker 240, and the user's reaction can be detected using the camera 42. === 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 receives voice input from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a travel plan using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated travel plan to the user by voice. For example, the camera 42 can be used to detect the user's facial expression and make further suggestions based on the generated plan.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can also take the user's current health condition into consideration when receiving user input. For example, if the user inputs information about their health condition, the reception unit can adjust the travel plan based on that information. Specifically, if the user is tired, it can suggest travel destinations and activities that will help them relax. Also, if the user is active, it can suggest a travel plan that includes many active activities. Furthermore, if the user has a specific health problem, it can provide a travel plan that addresses that problem. This makes it possible to provide the optimal travel plan based on the user's health condition.

[0092] The generation unit can minimize the environmental impact of the travel plan based on the information input by the user. For example, the generation unit can preferentially suggest eco-friendly means of transportation and accommodations. The generation unit can also suggest activities that respect the local culture and natural environment. Furthermore, if the user desires an environmentally friendly trip, the generation unit can generate a travel plan that meets the user's desire. This makes it possible to provide an environmentally friendly travel plan.

[0093] The providing unit can provide safety information for the user during the trip based on the generated travel plan. For example, the providing unit can provide security information and emergency contact information for the travel destination. The providing unit can also provide health risks and vaccination information for the travel destination. Furthermore, the providing unit can also provide weather information and natural disaster risks during the trip. This makes it possible to provide information for the user to enjoy the trip safely.

[0094] The reception unit can provide customization options for the travel plan based on the information input by the user. For example, if the user requests a travel plan based on a specific theme, the reception unit can provide options according to that theme. In addition, if the user has specific dietary restrictions or allergies, the reception unit can provide a travel plan that takes that information into consideration. Furthermore, if the user has specific interests or hobbies, the reception unit can suggest activities that match those interests or hobbies. This makes it possible to provide a travel plan that meets the user's individual needs.

[0095] The generation unit can optimize the cost of the travel plan based on the information input by the user. For example, the generation unit can suggest the most suitable accommodations and means of transportation according to the user's budget. Furthermore, if the user wants to keep costs down, the generation unit can also provide a travel plan that utilizes discount information and special offers. Furthermore, if the user desires a luxury trip, the generation unit can also suggest luxury accommodations and activities that meet the user's desires. This makes it possible to provide the most suitable travel plan according to the user's budget.

[0096] The reception unit can estimate the user's emotions and suggest travel plan themes based on the estimated user emotions. For example, if the user is feeling stressed, a travel plan that allows the user to relax can be suggested. If the user is excited, a travel plan that includes many active activities can be suggested. Furthermore, if the user is sad, a travel plan that will brighten the user's mood can be suggested. This makes it possible to provide the optimal travel plan according to the user's emotions.

[0097] The generation unit can estimate the user's emotions and adjust the activities in the travel plan based on the estimated user emotions. For example, if the user is relaxed, a travel plan including many leisurely activities can be generated. Alternatively, if the user is excited, a travel plan including many active activities can be generated. Furthermore, if the user is tired, a travel plan including many places and activities where the user can relax can be generated. This makes it possible to provide an optimal travel plan based on the user's emotions.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the travel plan based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the optimal display method can be provided based on the user's emotions.

[0099] The providing unit can estimate the user's emotions and prioritize the travel plan based on the estimated user's emotions. For example, if the user is relaxed, relaxing activities can be displayed with priority. Also, if the user is excited, active activities can be displayed with priority. Furthermore, if the user is tired, relaxing places and activities can be displayed with priority. In this way, an optimal travel plan can be provided based on the user's emotions.

[0100] The generation unit can estimate the user's emotions and adjust the contents of the travel plan based on the estimated user emotions. For example, if the user is relaxed, a travel plan with a leisurely schedule can be generated. If the user is excited, a travel plan including many active activities can be generated. Furthermore, if the user is tired, a travel plan including many places and activities where the user can relax can be generated. In this way, an optimal travel plan can be provided based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives user input. The user input includes, for example, an image of a place they would like to go, things they would like to do, travel duration, budget, personal information, etc. The reception unit can receive user input in the form of text input, voice input, image input, etc. Step 2: The generation unit uses a generation AI to generate a travel plan based on the information received by the reception unit. The generated travel plan includes destinations, transportation methods, length of stay, and information about nearby shops. The generation unit can also generate a travel plan taking into account the user's preferences and past travel history. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and analyzes the information input by the user to generate an optimal travel plan. Step 3: The providing unit provides the travel plan generated by the generating unit. The providing unit can also suggest additional destinations and optimal routes based on the generated travel plan. The providing unit uses the generating AI to provide the travel plan to the user and make further suggestions.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a generation unit that generates a travel plan based on the information received by the reception unit; a provider unit that provides the travel plan generated by the generator unit. A system characterized by:

2. The generation unit Generate travel plans based on user preferences and past travel history 2. The system of claim 1.

3. The providing unit Suggesting additional destinations and best routes based on the generated itinerary 2. The system of claim 1.

4. The reception unit The user inputs an image of the place they want to go or the things they want to do, travel period, budget, personal information, etc.

2. The system of claim 1.

5. The generation unit Generate detailed itineraries including destinations, transportation options, duration, and nearby shops 2. The system of claim 1.

6. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests the best input method 2. The system of claim 1.

8. The reception unit Get the user's current location and provide location-based suggestions 2. The system of claim 1.

Images