system

Abstract

The system according to this embodiment aims to efficiently generate and modify travel plans, thereby reducing the effort required from the user. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a modification reception unit, and a modification unit. The reception unit receives user input. The generation unit generates a travel plan based on the information received by the reception unit. The provision unit provides the plan generated by the generation unit to the user. The modification reception unit receives user modifications. The modification unit reflects the modifications received by the modification reception unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that when making a travel plan, it is necessary to cross-check many services, which is time-consuming.

[0005] The system according to the embodiment aims to efficiently generate and modify a travel plan and reduce the burden on the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a modification reception unit, and a modification unit. The reception unit receives user input. The generation unit generates a travel plan based on the information received by the reception unit. The provision unit provides the plan generated by the generation unit to the user. The modification reception unit receives user modifications. The modification unit reflects the modifications received by the modification reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate and modify travel plans, reducing the effort required from the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The travel plan creation system according to an embodiment of the present invention is a system that efficiently creates travel plans using a generation AI and RAG, significantly reducing the effort required from the user. When a user communicates general details such as destination and budget priorities via chat, the generation AI generates a travel plan based on that information. The generated plan is provided to the user as a detailed screen, and the user communicates items to be corrected to the AI ​​via chat or by writing on a printed sheet. The AI ​​understands the user's intentions from the writing and chat and corrects the plan, repeating this process until an optimal travel plan is completed. From the completed plan screen, users can purchase tickets based on route information, make hotel reservations, purchase gift certificates, etc. This system allows users to adjust their itinerary without gathering a large amount of information even in the event of unforeseen circumstances, and convenience is improved by utilizing services within the linked group. Furthermore, an expansion of the user base for group services is expected. For example, a user communicates general details such as destination and budget priorities via chat. For example, they might input something like, "I want to go to Tokyo, and my budget is under 100,000 yen." This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a travel plan. The AI ​​generates plans by considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. For example, it can suggest a plan to visit tourist spots in Tokyo and hotels that fit within the budget. The generated plan is provided to the user as a detailed screen. The user can view this screen and tell the AI ​​what they want to change via chat or by writing on a printed sheet of paper. For example, they might instruct the AI ​​to make a change such as, "This hotel is over budget, so please find a cheaper hotel." The AI ​​understands the user's intentions from their writing or chat and revise the plan. For example, it might suggest another hotel that fits within the budget. By repeating this process, the optimal travel plan that meets the user's wishes is completed. From the completed plan screen, users can purchase tickets from route information, book hotels, and purchase gift certificates. For example, they can "purchase tickets from route information services," "book hotels on travel sites," and "purchase gift certificates through hometown tax donations." This system allows users to adjust their itinerary even in the event of unforeseen circumstances without having to gather a large amount of information.For example, even in the event of sudden weather changes or transportation delays, the AI ​​automatically suggests alternative plans. Furthermore, by utilizing services within the linked group, convenience is improved, and an expansion of the group's user base is expected. As a result, the travel plan creation system can streamline the process of creating travel plans for users, significantly reducing the effort required.

[0029] The travel plan creation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a modification reception unit, and a modification unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit accepts, for example, general information such as destination and budget priorities from the user via chat. The generation unit generates a travel plan based on the information received by the reception unit. The generation unit generates a travel plan considering, for example, map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc., using a generation AI. For example, the generation AI generates a plan to visit tourist destinations based on map information of tourist destinations. The generation unit can also have the generation AI suggest the most suitable means of transportation based on route information. The generation unit can also have the generation AI suggest hotels that can be booked within the budget based on hotel travel site information. The provision unit provides the plan generated by the generation unit to the user. For example, the provision unit provides the generated plan to the user as a detailed screen. The delivery unit displays plans, for example, through web pages or mobile apps. The delivery unit can also provide plans as printed materials. The revision reception unit accepts revisions from users. The revision reception unit accepts revisions from users, for example, through chat or by writing on printed paper. The revision unit reflects the revisions received by the revision reception unit. The revision unit uses AI to understand the user's intentions and revise the plan. For example, if a user instructs the revision unit to change an over-budget hotel to a cheaper one, the generating AI will suggest another hotel that fits within the budget. By repeating this process, an optimal travel plan that meets the user's wishes is completed. Thus, the travel plan creation system according to this embodiment can efficiently create travel plans by accepting user input, providing generated plans, and reflecting revisions.

[0030] The reception desk accepts user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, users can input their travel destination, budget, travel duration, number of companions, and preferences for specific tourist attractions or activities. In the case of text input, users use a keyboard to enter detailed travel preferences. In the case of voice input, speech recognition technology is used to convert the user's speech into text, and the system analyzes the content. In the case of image input, users upload photos or screenshots of places they want to visit, and image recognition technology is used to identify the locations. By supporting these diverse input methods, the reception desk ensures that users can receive information in the most convenient way. Furthermore, the reception desk accepts users to input general information such as destination and budget priorities in a chat format. By using a chatbot, necessary information can be collected through dialogue with the user, and the user's intentions can be accurately understood. For example, if a user inputs "I want to go on a hot spring trip on the weekend next month," the chatbot will ask additional questions such as "What is your budget?" or "Which region's hot springs would you prefer?" to collect more detailed information. This allows the reception department to accurately understand user needs and smoothly provide that information to the next step, the generation department.

[0031] The generation unit generates travel plans based on information received by the reception unit. For example, using a generation AI, the generation unit generates travel plans considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. Specifically, the generation AI selects the most suitable tourist destinations and activities based on information such as the destination, budget, and travel period entered by the user. For example, if a user enters "I'm planning a 3-day trip to Kyoto," the generation AI collects information on major tourist attractions and events in Kyoto and creates a schedule for efficient sightseeing. The generation AI can also suggest the most suitable mode of transportation based on route information. For example, it calculates a route that minimizes travel time by considering train and bus timetables. Furthermore, the generation AI can suggest hotels that fit within the user's budget based on hotel travel site information. It selects the most suitable accommodation considering the user's budget and desired accommodation conditions (e.g., with hot springs, with breakfast, etc.). The generation AI integrates this information to generate the optimal travel plan for the user. For example, the system can generate a travel plan based on map information of tourist destinations, suggest the most suitable mode of transportation based on route information, and recommend hotels that fit within the user's budget based on hotel travel site information. This allows the generation unit to efficiently create detailed travel plans that meet the user's preferences.

[0032] The service provider provides users with plans generated by the generation unit. For example, the service provider provides users with the generated plans as detailed screens. Specifically, it displays plans through web pages and mobile apps. Users can review the generated plans and view detailed information on each tourist destination and accommodation, as well as transportation schedules. For example, web pages display photos, reviews, and map information of tourist destinations, allowing users to visualize their trip concretely. Mobile apps provide features that support schedule management and navigation during the trip, based on information updated in real time. Furthermore, the service provider can also provide plans in printed form. If desired, users can download the generated plans in PDF format, print them, and carry them with them. This allows them to review their travel plans even in places without internet access. The service provider ensures that users can easily review the generated plans and request modifications or additions as needed. For example, if a user wants to add a specific tourist destination or change accommodation, the service provider accepts the request and provides the information to the modification request unit, which is the next step. This allows the service provider to provide a user-friendly interface, enabling users to smoothly review and modify their travel plans.

[0033] The revision request department accepts user revisions. Specifically, it accepts revisions from users via chat or by writing on printed paper. For example, users can enter requests such as "I want to add this tourist spot" or "I want to change this hotel" through the chatbot. The revision request department receives these requests and provides the information to the revision department, which is the next step. Users can also make handwritten revisions to a printed plan and upload the image. Image recognition technology can be used to analyze the handwritten revisions and reflect them in the system. The revision request department quickly and accurately receives user revision requests and provides the information to the revision department, supporting revisions to the plan that meet the user's wishes.

[0034] The revision department incorporates revisions received by the revision reception department. Specifically, it uses AI to understand the user's intentions and revise the plan. For example, if a user requests a change from an over-budget hotel to a cheaper one, the generating AI will suggest an alternative hotel that fits within the budget. The generating AI analyzes the user's revision request and generates the optimal revision. For example, if a user requests to add a specific tourist destination, the generating AI will generate a new plan including that destination and adjust it to maintain consistency with other schedules and transportation. Also, if a user requests to change a hotel, the generating AI will suggest an alternative hotel that matches the budget and desired conditions, and reconstruct the entire plan. By repeating these processes, the revision department completes the optimal travel plan that meets the user's wishes. Furthermore, the revision department can continuously improve the accuracy and effectiveness of the revisions based on user feedback. For example, if a user makes an additional request regarding the revised plan, the generating AI will incorporate that request and generate an even more optimal plan. In this way, the revision department can flexibly revise plans according to the user's wishes and ultimately provide a travel plan that satisfies the user.

[0035] The generation unit can generate travel plans by considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. For example, the generation unit can generate a travel plan for visiting tourist destinations based on map information of tourist destinations. For example, the generation unit can also suggest the most suitable mode of transportation based on route information. For example, the generation unit can suggest hotels that can be booked within a budget based on hotel travel site information. In this way, by generating travel plans by considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc., the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data such as map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, and entrance fees into a generation AI and have the generation AI generate a travel plan.

[0036] The service provider can provide the generated plan to the user as a detail screen. For example, the service provider can display the generated plan as a detail screen through a web page or mobile app. The service provider can also provide the plan as a printed document. By providing the generated plan to the user as a detail screen, it makes it easier for the user to check the contents of the plan. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the data of the generated plan into a generation AI and have the generation AI perform the generation of the detail screen.

[0037] The revision reception unit can accept user comments via chat or on printed paper. For example, the revision reception unit can accept items to be revised entered by the user via chat. The revision reception unit can also accept revision items written by the user on printed paper by scanning them. This allows users to easily revise their plans by accepting comments via chat or on printed paper. Some or all of the above processing in the revision reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the revision reception unit can input revision items written by the user via chat or on printed paper into a generative AI and have the generative AI analyze the revision content.

[0038] The modification unit can understand the user's intentions and modify the plan. For example, the modification unit uses AI to understand the user's intentions and modify the plan. For example, if the user instructs the modification unit to change an over-budget hotel to a cheaper one, the generating AI will suggest another hotel that fits within the budget. For example, if the user instructs the modification unit to change the order of tourist attractions, the generating AI will change them to the optimal order. In this way, by understanding the user's intentions and modifying the plan, the modification unit can provide the optimal plan that meets the user's wishes. Some or all of the above processing in the modification unit may be performed using, for example, the generating AI, or without the generating AI. For example, the modification unit can input data to understand the user's intentions into the generating AI and have the generating AI perform the plan modification.

[0039] The service provider can enable users to purchase tickets from a route information service, book hotels on travel websites, and purchase gift certificates through the Furusato Nozei (hometown tax donation) program from the completed plan screen. For example, the service provider can provide a link to a route information service from the completed plan screen, allowing users to purchase tickets directly. For example, the service provider can provide a link to a travel website, allowing users to book hotels directly. For example, the service provider can provide a link to the Furusato Nozei program, allowing users to purchase gift certificates directly. This improves user convenience by enabling direct purchase of tickets, hotel reservations, and gift certificates from the completed plan screen. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the data from the completed plan screen into a generation AI and have the generation AI generate the links.

[0040] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display destination candidates based on places the user has visited in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest destinations related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past travel history data into a generative AI and have the generative AI suggest the optimal input method.

[0041] The reception desk can filter input content based on the user's current interests and preferences. For example, the reception desk can display destination suggestions based on tourist destinations or events the user has recently searched for. For example, the reception desk can analyze the user's social media activity and suggest relevant destinations and budgets. For example, the reception desk can filter destination suggestions based on themes the user is interested in (history, nature, food, etc.). By filtering input content based on the user's current interests and preferences, the reception desk can provide the user with highly relevant information. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's current interests and preferences data into a generative AI and have the generative AI perform the filtering of the input content.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during input. For example, the reception desk can prioritize displaying tourist attractions and hotels close to the user's current location. For example, the reception desk can suggest the most suitable means of transportation and routes based on the user's current location. For example, the reception desk can suggest destinations that are within the user's budget, taking into account the distance from the user's current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized for input. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI prioritize inputting highly relevant information.

[0043] The reception desk can analyze the user's social media activity during input and automatically input relevant information. For example, the reception desk can display destination suggestions based on tourist spots and events that the user has "liked" on social media. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant destinations and budgets. For example, the reception desk can filter destination suggestions based on posts from influencers that the user follows. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI perform the automatic input of relevant information.

[0044] The generation unit can adjust the level of detail in the plan based on the purpose of the trip during generation. For example, if the purpose is sightseeing, the generation unit will generate a plan that includes detailed information about tourist destinations. If the purpose is business, the generation unit will generate a plan that is tailored to the schedule of meetings and business negotiations. If the purpose is relaxation, the generation unit will generate a plan that includes information about resorts and spas. In this way, by adjusting the level of detail in the plan based on the purpose of the trip, the user can be provided with the most suitable plan. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the purpose of the trip data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0045] The generation unit can apply different generation algorithms depending on the travel category during generation. For example, in the case of a family trip, the generation unit applies an algorithm that generates a plan that includes activities for children. For example, in the case of a couple's trip, the generation unit applies an algorithm that generates a plan that includes romantic spots. For example, in the case of a solo trip, the generation unit applies an algorithm that generates a plan with a high degree of flexibility. In this way, by applying different generation algorithms depending on the travel category, the user can be provided with the most suitable plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0046] The generation unit can determine the priority of plans based on the time of year of the trip during the generation process. For example, for a summer trip, the generation unit will prioritize plans that include beaches and swimming pools. For a winter trip, the generation unit will prioritize plans that include skiing and hot springs. For a spring trip, the generation unit will prioritize plans that include cherry blossom viewing and hiking. By prioritizing plans based on the time of year of the trip, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input travel time data into a generation AI and have the generation AI perform the determination of plan priorities.

[0047] The generation unit can adjust the plan content based on the relevance of the trip during generation. For example, if the user is interested in history, the generation unit will generate a plan that includes historical tourist attractions. For example, if the user is interested in nature, the generation unit will generate a plan that includes nature parks and hiking trails. For example, if the user is interested in gourmet food, the generation unit will generate a plan that includes local specialty dishes. In this way, by adjusting the plan content based on the relevance of the trip, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input travel relevance data into a generation AI and have the generation AI perform the adjustment of the plan content.

[0048] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may propose the optimal display method based on the display method the user has preferred to use in the past. For example, the service provider may propose a display method with high visibility based on the user's past operation history. For example, the service provider may analyze the user's past operation history and propose the most efficient display method. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider may input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0049] The service provider can customize the displayed plan content based on the user's current situation at the time of delivery. For example, if the user is on the move, the service provider can provide a concise and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by customizing the displayed plan content based on the user's current situation, the service provider can provide the user with the most suitable display content. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's current situation data into a generative AI and have the generative AI perform the customization of the displayed plan content.

[0050] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input user device information data into a generation AI and have the generation AI select the optimal display method.

[0051] The service provider can analyze the user's social media activity and display relevant information at the time of delivery. For example, the service provider can display destination suggestions based on tourist destinations and events that the user has "liked" on social media. For example, the service provider can analyze the content of the user's social media posts and suggest relevant destinations and budgets. For example, the service provider can filter destination suggestions based on posts from influencers that the user follows. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI and have the generative AI display relevant information.

[0052] The correction reception unit can analyze the user's past correction history and propose the optimal correction method when a correction is submitted. For example, the correction reception unit proposes the optimal correction method based on the user's past corrections. For example, the correction reception unit prioritizes proposing frequently performed corrections from the user's past correction history. For example, the correction reception unit analyzes the user's past correction history and proposes an efficient correction method. In this way, the optimal correction method can be proposed by analyzing the user's past correction history. Some or all of the above processing in the correction reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the correction reception unit can input the user's past correction history data into a generation AI and have the generation AI propose the optimal correction method.

[0053] The correction reception unit can filter corrections based on the user's current situation when a correction is received. For example, if the user is on the move, the correction reception unit will prioritize suggesting concise and easily visible corrections. For example, if the user is relaxed, the correction reception unit will suggest detailed corrections. For example, if the user is in a hurry, the correction reception unit will quickly implement the corrections. In this way, by filtering corrections based on the user's current situation, the system can provide the user with the most suitable corrections. Some or all of the above processing in the correction reception unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the correction reception unit can input the user's current situation data into a generation AI and have the generation AI perform the filtering of corrections.

[0054] The correction reception unit can prioritize accepting corrections that are highly relevant, taking into account the user's geographical location information. For example, the correction reception unit can prioritize accepting corrections for tourist destinations or hotels close to the user's current location. For example, the correction reception unit can suggest corrections for the most suitable means of transportation or route based on the user's current location. For example, the correction reception unit can suggest corrections for destinations that are within the user's budget, taking into account the distance from the user's current location. In this way, by considering the user's geographical location information, the correction reception unit can prioritize accepting corrections that are highly relevant. Some or all of the above processing in the correction reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the correction reception unit can input the user's geographical location information data into a generation AI and have the generation AI prioritize accepting corrections that are highly relevant.

[0055] The correction reception unit can analyze the user's social media activity and automatically accept relevant corrections when a correction is requested. For example, the correction reception unit prioritizes accepting corrections for tourist destinations or events that the user has "liked" on social media. For example, the correction reception unit analyzes the content of the user's social media posts and suggests relevant corrections. For example, the correction reception unit filters corrections based on posts from influencers that the user follows. In this way, by analyzing the user's social media activity, it is possible to automatically accept relevant corrections. Some or all of the above processes in the correction reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the correction reception unit can input the user's social media activity data into a generative AI and have the generative AI perform the automatic acceptance of relevant corrections.

[0056] The correction unit can select the optimal correction method by referring to the user's past correction history when making corrections. For example, the correction unit proposes the optimal correction method based on the user's past corrections. For example, the correction unit prioritizes and proposes frequently made corrections from the user's past correction history. For example, the correction unit analyzes the user's past correction history and proposes an efficient correction method. In this way, the optimal correction method can be provided by referring to the user's past correction history. Some or all of the above processes in the correction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the correction unit can input the user's past correction history data into a generation AI and have the generation AI select the optimal correction method.

[0057] The editing unit can customize the edits based on the user's current situation. For example, if the user is on the move, the editing unit will prioritize suggesting concise and easily visible edits. If the user is relaxed, for example, the editing unit will suggest detailed edits. If the user is in a hurry, for example, the editing unit will quickly reflect the edits. This allows the editing unit to provide the user with the most suitable edits by customizing them based on the user's current situation. Some or all of the above processing in the editing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the editing unit can input the user's current situation data into a generative AI and have the generative AI perform the customization of the edits.

[0058] The editing unit can select the optimal editing method while considering the user's geographical location information. For example, the editing unit may prioritize editing changes for tourist destinations or hotels close to the user's current location. For example, the editing unit may suggest the optimal mode of transport and route based on the user's current location. For example, the editing unit may suggest editing changes for destinations that are within the user's budget, taking into account the distance from the user's current location. In this way, the optimal editing method can be provided by considering the user's geographical location information. Some or all of the above processing in the editing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the editing unit can input the user's geographical location information data into a generative AI and have the generative AI select the optimal editing method.

[0059] The editing unit can analyze the user's social media activity and suggest relevant revisions during the revision process. For example, the editing unit may prioritize revisions to tourist destinations or events that the user has "liked" on social media. For example, the editing unit may analyze the user's social media posts and suggest relevant revisions. For example, the editing unit may filter revisions based on posts from influencers that the user follows. In this way, relevant revisions can be provided by analyzing the user's social media activity. Some or all of the above processes in the editing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the editing unit may input the user's social media activity data into a generative AI and have the generative AI suggest relevant revisions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] A travel plan creation system can analyze a user's past travel history and suggest plans based on their preferences and tendencies from past trips. For example, it can suggest similar tourist destinations and accommodations based on the user's ratings of previously visited tourist spots and accommodations. It can also suggest plans that include similar activities and events based on information about activities and events the user has participated in in the past. Furthermore, it can suggest the most suitable modes of transportation and routes based on information about the modes of transport and routes the user has used in the past. In this way, it can provide the optimal travel plan based on the user's past travel history.

[0062] A travel planning system can analyze a user's social media activity and suggest travel plans based on their interests and preferences on social media. For example, it can display potential destinations based on tourist spots and events that the user has "liked" on social media. It can also analyze the content of the user's social media posts and suggest relevant destinations and activities. Furthermore, it can filter potential destinations based on posts from influencers the user follows. This allows the system to provide the most suitable travel plan based on the user's social media activity.

[0063] A travel planning system can propose the most suitable travel plan from a user's current location, taking into account their geographical location. For example, it can prioritize displaying tourist attractions and accommodations close to the user's current location. It can also suggest the most suitable mode of transportation and route based on the user's current location. Furthermore, it can suggest destinations that are within the user's budget, taking into account the distance from their current location. In this way, it can provide the most suitable travel plan based on the user's geographical location.

[0064] The travel plan creation system can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for larger screens. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the system to provide the optimal display method based on the user's device information.

[0065] The travel plan creation system can select the optimal display method by referring to the user's past operation history. For example, it can suggest the optimal display method based on the display method the user has preferred in the past. It can also suggest a highly visible display method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and suggest the most efficient display method. In this way, it can provide the optimal display method based on the user's past operation history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk accepts general information such as destination and budget priorities via chat. Step 2: The generation unit generates a travel plan based on the information received by the reception unit. The generation unit uses a generation AI to generate a travel plan considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. For example, the generation AI generates a plan to visit tourist destinations based on map information, suggests the most suitable mode of transportation based on route information, and suggests hotels that can be stayed at within the budget based on hotel travel site information. Step 3: The delivery unit provides the user with the plan generated by the generation unit. The delivery unit provides the user with the generated plan as a details screen and displays the plan through a web page or mobile app. The plan can also be provided as a printed document. Step 4: The correction submission department accepts user corrections. For example, it accepts corrections entered by the user via chat or by writing on a printed document. Step 5: The revision section incorporates the revisions received by the revision reception section. For example, AI is used to understand the user's intentions and revise the plan. If the user instructs to change an overpriced hotel to a cheaper one, the generating AI will suggest an alternative hotel that fits within the budget. By repeating this process, the optimal travel plan that meets the user's wishes is completed.

[0068] (Example of form 2) The travel plan creation system according to an embodiment of the present invention is a system that efficiently creates travel plans using a generation AI and RAG, significantly reducing the effort required from the user. When a user communicates general details such as destination and budget priorities via chat, the generation AI generates a travel plan based on that information. The generated plan is provided to the user as a detailed screen, and the user communicates items to be corrected to the AI ​​via chat or by writing on a printed sheet. The AI ​​understands the user's intentions from the writing and chat and corrects the plan, repeating this process until an optimal travel plan is completed. From the completed plan screen, users can purchase tickets based on route information, make hotel reservations, purchase gift certificates, etc. This system allows users to adjust their itinerary without gathering a large amount of information even in the event of unforeseen circumstances, and convenience is improved by utilizing services within the linked group. Furthermore, an expansion of the user base for group services is expected. For example, a user communicates general details such as destination and budget priorities via chat. For example, they might input something like, "I want to go to Tokyo, and my budget is under 100,000 yen." This information is input to the generation AI. Next, the generation AI analyzes the input information and generates a travel plan. The AI ​​generates plans by considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. For example, it can suggest a plan to visit tourist spots in Tokyo and hotels that fit within the budget. The generated plan is provided to the user as a detailed screen. The user can view this screen and tell the AI ​​what they want to change via chat or by writing on a printed sheet of paper. For example, they might instruct the AI ​​to make a change such as, "This hotel is over budget, so please find a cheaper hotel." The AI ​​understands the user's intentions from their writing or chat and revise the plan. For example, it might suggest another hotel that fits within the budget. By repeating this process, the optimal travel plan that meets the user's wishes is completed. From the completed plan screen, users can purchase tickets from route information, book hotels, and purchase gift certificates. For example, they can "purchase tickets from route information services," "book hotels on travel sites," and "purchase gift certificates through hometown tax donations." This system allows users to adjust their itinerary even in the event of unforeseen circumstances without having to gather a large amount of information.For example, even in the event of sudden weather changes or transportation delays, the AI ​​automatically suggests alternative plans. Furthermore, by utilizing services within the linked group, convenience is improved, and an expansion of the group's user base is expected. As a result, the travel plan creation system can streamline the process of creating travel plans for users, significantly reducing the effort required.

[0069] The travel plan creation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a modification reception unit, and a modification unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit accepts, for example, general information such as destination and budget priorities from the user via chat. The generation unit generates a travel plan based on the information received by the reception unit. The generation unit generates a travel plan considering, for example, map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc., using a generation AI. For example, the generation AI generates a plan to visit tourist destinations based on map information of tourist destinations. The generation unit can also have the generation AI suggest the most suitable means of transportation based on route information. The generation unit can also have the generation AI suggest hotels that can be booked within the budget based on hotel travel site information. The provision unit provides the plan generated by the generation unit to the user. For example, the provision unit provides the generated plan to the user as a detailed screen. The delivery unit displays plans, for example, through web pages or mobile apps. The delivery unit can also provide plans as printed materials. The revision reception unit accepts revisions from users. The revision reception unit accepts revisions from users, for example, through chat or by writing on printed paper. The revision unit reflects the revisions received by the revision reception unit. The revision unit uses AI to understand the user's intentions and revise the plan. For example, if a user instructs the revision unit to change an over-budget hotel to a cheaper one, the generating AI will suggest another hotel that fits within the budget. By repeating this process, an optimal travel plan that meets the user's wishes is completed. Thus, the travel plan creation system according to this embodiment can efficiently create travel plans by accepting user input, providing generated plans, and reflecting revisions.

[0070] The reception desk accepts user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, users can input their travel destination, budget, travel duration, number of companions, and preferences for specific tourist attractions or activities. In the case of text input, users use a keyboard to enter detailed travel preferences. In the case of voice input, speech recognition technology is used to convert the user's speech into text, and the system analyzes the content. In the case of image input, users upload photos or screenshots of places they want to visit, and image recognition technology is used to identify the locations. By supporting these diverse input methods, the reception desk ensures that users can receive information in the most convenient way. Furthermore, the reception desk accepts users to input general information such as destination and budget priorities in a chat format. By using a chatbot, necessary information can be collected through dialogue with the user, and the user's intentions can be accurately understood. For example, if a user inputs "I want to go on a hot spring trip on the weekend next month," the chatbot will ask additional questions such as "What is your budget?" or "Which region's hot springs would you prefer?" to collect more detailed information. This allows the reception department to accurately understand user needs and smoothly provide that information to the next step, the generation department.

[0071] The generation unit generates travel plans based on information received by the reception unit. For example, using a generation AI, the generation unit generates travel plans considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. Specifically, the generation AI selects the most suitable tourist destinations and activities based on information such as the destination, budget, and travel period entered by the user. For example, if a user enters "I'm planning a 3-day trip to Kyoto," the generation AI collects information on major tourist attractions and events in Kyoto and creates a schedule for efficient sightseeing. The generation AI can also suggest the most suitable mode of transportation based on route information. For example, it calculates a route that minimizes travel time by considering train and bus timetables. Furthermore, the generation AI can suggest hotels that fit within the user's budget based on hotel travel site information. It selects the most suitable accommodation considering the user's budget and desired accommodation conditions (e.g., with hot springs, with breakfast, etc.). The generation AI integrates this information to generate the optimal travel plan for the user. For example, the system can generate a travel plan based on map information of tourist destinations, suggest the most suitable mode of transportation based on route information, and recommend hotels that fit within the user's budget based on hotel travel site information. This allows the generation unit to efficiently create detailed travel plans that meet the user's preferences.

[0072] The service provider provides users with plans generated by the generation unit. For example, the service provider provides users with the generated plans as detailed screens. Specifically, it displays plans through web pages and mobile apps. Users can review the generated plans and view detailed information on each tourist destination and accommodation, as well as transportation schedules. For example, web pages display photos, reviews, and map information of tourist destinations, allowing users to visualize their trip concretely. Mobile apps provide features that support schedule management and navigation during the trip, based on information updated in real time. Furthermore, the service provider can also provide plans in printed form. If desired, users can download the generated plans in PDF format, print them, and carry them with them. This allows them to review their travel plans even in places without internet access. The service provider ensures that users can easily review the generated plans and request modifications or additions as needed. For example, if a user wants to add a specific tourist destination or change accommodation, the service provider accepts the request and provides the information to the modification request unit, which is the next step. This allows the service provider to provide a user-friendly interface, enabling users to smoothly review and modify their travel plans.

[0073] The revision request department accepts user revisions. Specifically, it accepts revisions from users via chat or by writing on printed paper. For example, users can enter requests such as "I want to add this tourist spot" or "I want to change this hotel" through the chatbot. The revision request department receives these requests and provides the information to the revision department, which is the next step. Users can also make handwritten revisions to a printed plan and upload the image. Image recognition technology can be used to analyze the handwritten revisions and reflect them in the system. The revision request department quickly and accurately receives user revision requests and provides the information to the revision department, supporting revisions to the plan that meet the user's wishes.

[0074] The revision department incorporates revisions received by the revision reception department. Specifically, it uses AI to understand the user's intentions and revise the plan. For example, if a user requests a change from an over-budget hotel to a cheaper one, the generating AI will suggest an alternative hotel that fits within the budget. The generating AI analyzes the user's revision request and generates the optimal revision. For example, if a user requests to add a specific tourist destination, the generating AI will generate a new plan including that destination and adjust it to maintain consistency with other schedules and transportation. Also, if a user requests to change a hotel, the generating AI will suggest an alternative hotel that matches the budget and desired conditions, and reconstruct the entire plan. By repeating these processes, the revision department completes the optimal travel plan that meets the user's wishes. Furthermore, the revision department can continuously improve the accuracy and effectiveness of the revisions based on user feedback. For example, if a user makes an additional request regarding the revised plan, the generating AI will incorporate that request and generate an even more optimal plan. In this way, the revision department can flexibly revise plans according to the user's wishes and ultimately provide a travel plan that satisfies the user.

[0075] The generation unit can generate travel plans by considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. For example, the generation unit can generate a travel plan for visiting tourist destinations based on map information of tourist destinations. For example, the generation unit can also suggest the most suitable mode of transportation based on route information. For example, the generation unit can suggest hotels that can be booked within a budget based on hotel travel site information. In this way, by generating travel plans by considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc., the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data such as map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, and entrance fees into a generation AI and have the generation AI generate a travel plan.

[0076] The service provider can provide the generated plan to the user as a detail screen. For example, the service provider can display the generated plan as a detail screen through a web page or mobile app. The service provider can also provide the plan as a printed document. By providing the generated plan to the user as a detail screen, it makes it easier for the user to check the contents of the plan. Some or all of the above processing in the service provider may be performed using a generation AI, for example, or without a generation AI. For example, the service provider can input the data of the generated plan into a generation AI and have the generation AI perform the generation of the detail screen.

[0077] The revision reception unit can accept user comments via chat or on printed paper. For example, the revision reception unit can accept items to be revised entered by the user via chat. The revision reception unit can also accept revision items written by the user on printed paper by scanning them. This allows users to easily revise their plans by accepting comments via chat or on printed paper. Some or all of the above processing in the revision reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the revision reception unit can input revision items written by the user via chat or on printed paper into a generative AI and have the generative AI analyze the revision content.

[0078] The modification unit can understand the user's intentions and modify the plan. For example, the modification unit uses AI to understand the user's intentions and modify the plan. For example, if the user instructs the modification unit to change an over-budget hotel to a cheaper one, the generating AI will suggest another hotel that fits within the budget. For example, if the user instructs the modification unit to change the order of tourist attractions, the generating AI will change them to the optimal order. In this way, by understanding the user's intentions and modifying the plan, the modification unit can provide the optimal plan that meets the user's wishes. Some or all of the above processing in the modification unit may be performed using, for example, the generating AI, or without the generating AI. For example, the modification unit can input data to understand the user's intentions into the generating AI and have the generating AI perform the plan modification.

[0079] The service provider can enable users to purchase tickets from a route information service, book hotels on travel websites, and purchase gift certificates through the Furusato Nozei (hometown tax donation) program from the completed plan screen. For example, the service provider can provide a link to a route information service from the completed plan screen, allowing users to purchase tickets directly. For example, the service provider can provide a link to a travel website, allowing users to book hotels directly. For example, the service provider can provide a link to the Furusato Nozei program, allowing users to purchase gift certificates directly. This improves user convenience by enabling direct purchase of tickets, hotel reservations, and gift certificates from the completed plan screen. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the data from the completed plan screen into a generation AI and have the generation AI generate the links.

[0080] The reception desk can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of destination and budget. This reduces user stress and enables efficient input by adjusting the priority of input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI adjust the priority of input content.

[0081] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display destination candidates based on places the user has visited in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest destinations related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past travel history data into a generative AI and have the generative AI suggest the optimal input method.

[0082] The reception desk can filter input content based on the user's current interests and preferences. For example, the reception desk can display destination suggestions based on tourist destinations or events the user has recently searched for. For example, the reception desk can analyze the user's social media activity and suggest relevant destinations and budgets. For example, the reception desk can filter destination suggestions based on themes the user is interested in (history, nature, food, etc.). By filtering input content based on the user's current interests and preferences, the reception desk can provide the user with highly relevant information. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's current interests and preferences data into a generative AI and have the generative AI perform the filtering of the input content.

[0083] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the design of the input interface based on the user's emotions, user stress is reduced and the input process becomes more enjoyable. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the design of the input interface.

[0084] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location during input. For example, the reception desk can prioritize displaying tourist attractions and hotels close to the user's current location. For example, the reception desk can suggest the most suitable means of transportation and routes based on the user's current location. For example, the reception desk can suggest destinations that are within the user's budget, taking into account the distance from the user's current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized for input. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI prioritize inputting highly relevant information.

[0085] The reception desk can analyze the user's social media activity during input and automatically input relevant information. For example, the reception desk can display destination suggestions based on tourist spots and events that the user has "liked" on social media. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant destinations and budgets. For example, the reception desk can filter destination suggestions based on posts from influencers that the user follows. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI perform the automatic input of relevant information.

[0086] The generation unit can estimate the user's emotions and adjust the content of the generated plan based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a relaxed sightseeing plan. If the user is in a hurry, the generation unit will generate a plan that efficiently visits tourist spots. If the user is excited, the generation unit will generate a plan that includes active activities. In this way, by adjusting the content of the generated plan based on the user's emotions, the system can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the content of the plan.

[0087] The generation unit can adjust the level of detail in the plan based on the purpose of the trip during generation. For example, if the purpose is sightseeing, the generation unit will generate a plan that includes detailed information about tourist destinations. If the purpose is business, the generation unit will generate a plan that is tailored to the schedule of meetings and business negotiations. If the purpose is relaxation, the generation unit will generate a plan that includes information about resorts and spas. In this way, by adjusting the level of detail in the plan based on the purpose of the trip, the user can be provided with the most suitable plan. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the purpose of the trip data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0088] The generation unit can apply different generation algorithms depending on the travel category during generation. For example, in the case of a family trip, the generation unit applies an algorithm that generates a plan that includes activities for children. For example, in the case of a couple's trip, the generation unit applies an algorithm that generates a plan that includes romantic spots. For example, in the case of a solo trip, the generation unit applies an algorithm that generates a plan with a high degree of flexibility. In this way, by applying different generation algorithms depending on the travel category, the user can be provided with the most suitable plan. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input travel category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the order of the generated plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit will suggest an order for leisurely sightseeing. If the user is in a hurry, the generation unit will suggest an order for efficient sightseeing. If the user is excited, the generation unit will suggest active activities first. In this way, by adjusting the order of the generated plan based on the user's emotions, the system can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the order of the plan.

[0090] The generation unit can determine the priority of plans based on the time of year of the trip during the generation process. For example, for a summer trip, the generation unit will prioritize plans that include beaches and swimming pools. For a winter trip, the generation unit will prioritize plans that include skiing and hot springs. For a spring trip, the generation unit will prioritize plans that include cherry blossom viewing and hiking. By prioritizing plans based on the time of year of the trip, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input travel time data into a generation AI and have the generation AI perform the determination of plan priorities.

[0091] The generation unit can adjust the plan content based on the relevance of the trip during generation. For example, if the user is interested in history, the generation unit will generate a plan that includes historical tourist attractions. For example, if the user is interested in nature, the generation unit will generate a plan that includes nature parks and hiking trails. For example, if the user is interested in gourmet food, the generation unit will generate a plan that includes local specialty dishes. In this way, by adjusting the plan content based on the relevance of the trip, the generation unit can provide the user with the most suitable plan. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input travel relevance data into a generation AI and have the generation AI perform the adjustment of the plan content.

[0092] The service provider can estimate the user's emotions and adjust how the plan is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. For example, if the user is relaxed, the service provider can provide a display that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting how the plan is displayed based on the user's emotions, the service provider can provide the user with the most suitable display method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the plan is displayed.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may propose the optimal display method based on the display method the user has preferred to use in the past. For example, the service provider may propose a display method with high visibility based on the user's past operation history. For example, the service provider may analyze the user's past operation history and propose the most efficient display method. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider may input the user's past operation history data into a generation AI and have the generation AI select the optimal display method.

[0094] The service provider can customize the displayed plan content based on the user's current situation at the time of delivery. For example, if the user is on the move, the service provider can provide a concise and highly visible display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by customizing the displayed plan content based on the user's current situation, the service provider can provide the user with the most suitable display content. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's current situation data into a generative AI and have the generative AI perform the customization of the displayed plan content.

[0095] The service provider can estimate the user's emotions and determine the priority of the plans offered based on the estimated emotions. For example, if the user is relaxed, the service provider might suggest a leisurely tour of tourist attractions. If the user is in a hurry, the service provider might suggest an efficient tour of tourist attractions. If the user is excited, the service provider might suggest active activities first. By prioritizing the plans offered based on the user's emotions, the service provider can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of the plans.

[0096] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input user device information data into a generation AI and have the generation AI select the optimal display method.

[0097] The service provider can analyze the user's social media activity and display relevant information at the time of delivery. For example, the service provider can display destination suggestions based on tourist destinations and events that the user has "liked" on social media. For example, the service provider can analyze the content of the user's social media posts and suggest relevant destinations and budgets. For example, the service provider can filter destination suggestions based on posts from influencers that the user follows. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI and have the generative AI display relevant information.

[0098] The correction reception unit can estimate the user's emotions and adjust the priority of corrections based on the estimated emotions. For example, if the user is stressed, the correction reception unit will prioritize important corrections. For example, if the user is relaxed, the correction reception unit will suggest detailed corrections. For example, if the user is in a hurry, the correction reception unit will quickly reflect the corrections. In this way, by adjusting the priority of corrections based on the user's emotions, the system can provide the user with the most suitable corrections. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the correction reception unit may be performed using a generative AI, or not using a generative AI. For example, the correction reception unit can input user emotion data into a generative AI and have the generative AI adjust the priority of corrections.

[0099] The correction reception unit can analyze the user's past correction history and propose the optimal correction method when a correction is submitted. For example, the correction reception unit proposes the optimal correction method based on the user's past corrections. For example, the correction reception unit prioritizes proposing frequently performed corrections from the user's past correction history. For example, the correction reception unit analyzes the user's past correction history and proposes an efficient correction method. In this way, the optimal correction method can be proposed by analyzing the user's past correction history. Some or all of the above processing in the correction reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the correction reception unit can input the user's past correction history data into a generation AI and have the generation AI propose the optimal correction method.

[0100] The correction reception unit can filter corrections based on the user's current situation when a correction is received. For example, if the user is on the move, the correction reception unit will prioritize suggesting concise and easily visible corrections. For example, if the user is relaxed, the correction reception unit will suggest detailed corrections. For example, if the user is in a hurry, the correction reception unit will quickly implement the corrections. In this way, by filtering corrections based on the user's current situation, the system can provide the user with the most suitable corrections. Some or all of the above processing in the correction reception unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the correction reception unit can input the user's current situation data into a generation AI and have the generation AI perform the filtering of corrections.

[0101] The correction reception unit can estimate the user's emotions and adjust the design of the correction interface based on the estimated emotions. For example, if the user is tense, the correction reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the correction reception unit can provide an interface with bright colors to make the correction process enjoyable. For example, if the user is tired, the correction reception unit can provide a simple and highly visible interface to facilitate the correction process. In this way, by adjusting the design of the correction interface based on the user's emotions, the correction interface can be provided to the user in the most optimal way. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the correction reception unit may be performed using a generative AI, or not using a generative AI. For example, the correction reception unit can input user emotion data into a generative AI and have the generative AI adjust the design of the correction interface.

[0102] The correction reception unit can prioritize accepting corrections that are highly relevant, taking into account the user's geographical location information. For example, the correction reception unit can prioritize accepting corrections for tourist destinations or hotels close to the user's current location. For example, the correction reception unit can suggest corrections for the most suitable means of transportation or route based on the user's current location. For example, the correction reception unit can suggest corrections for destinations that are within the user's budget, taking into account the distance from the user's current location. In this way, by considering the user's geographical location information, the correction reception unit can prioritize accepting corrections that are highly relevant. Some or all of the above processing in the correction reception unit may be performed using, for example, a generation AI, or without a generation AI. For example, the correction reception unit can input the user's geographical location information data into a generation AI and have the generation AI prioritize accepting corrections that are highly relevant.

[0103] The correction reception unit can analyze the user's social media activity and automatically accept relevant corrections when a correction is requested. For example, the correction reception unit prioritizes accepting corrections for tourist destinations or events that the user has "liked" on social media. For example, the correction reception unit analyzes the content of the user's social media posts and suggests relevant corrections. For example, the correction reception unit filters corrections based on posts from influencers that the user follows. In this way, by analyzing the user's social media activity, it is possible to automatically accept relevant corrections. Some or all of the above processes in the correction reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the correction reception unit can input the user's social media activity data into a generative AI and have the generative AI perform the automatic acceptance of relevant corrections.

[0104] The editing unit can estimate the user's emotions and adjust the edits based on the estimated emotions. For example, if the user is relaxed, the editing unit will reflect detailed edits. For example, if the user is in a hurry, the editing unit will quickly reflect edits. For example, if the user is excited, the editing unit will reflect edits that include active activities. This allows the editing unit to provide the user with the most suitable edits by adjusting the edits based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using a generative AI, or not. For example, the editing unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the edits.

[0105] The correction unit can select the optimal correction method by referring to the user's past correction history when making corrections. For example, the correction unit proposes the optimal correction method based on the user's past corrections. For example, the correction unit prioritizes and proposes frequently made corrections from the user's past correction history. For example, the correction unit analyzes the user's past correction history and proposes an efficient correction method. In this way, the optimal correction method can be provided by referring to the user's past correction history. Some or all of the above processes in the correction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the correction unit can input the user's past correction history data into a generation AI and have the generation AI select the optimal correction method.

[0106] The editing unit can customize the edits based on the user's current situation. For example, if the user is on the move, the editing unit will prioritize suggesting concise and easily visible edits. If the user is relaxed, for example, the editing unit will suggest detailed edits. If the user is in a hurry, for example, the editing unit will quickly reflect the edits. This allows the editing unit to provide the user with the most suitable edits by customizing them based on the user's current situation. Some or all of the above processing in the editing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the editing unit can input the user's current situation data into a generative AI and have the generative AI perform the customization of the edits.

[0107] The editing unit can estimate the user's emotions and determine the priority of the edits based on the estimated emotions. For example, if the user is relaxed, the editing unit will prioritize detailed edits. If the user is in a hurry, the editing unit will prioritize important edits. If the user is excited, the editing unit will prioritize edits that include active activities. This allows the editing unit to provide the user with the most appropriate edits by prioritizing the edits based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using a generative AI, or not. For example, the editing unit can input user emotion data into a generative AI and have the generative AI determine the priority of the edits.

[0108] The editing unit can select the optimal editing method while considering the user's geographical location information. For example, the editing unit may prioritize editing changes for tourist destinations or hotels close to the user's current location. For example, the editing unit may suggest the optimal mode of transport and route based on the user's current location. For example, the editing unit may suggest editing changes for destinations that are within the user's budget, taking into account the distance from the user's current location. In this way, the optimal editing method can be provided by considering the user's geographical location information. Some or all of the above processing in the editing unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the editing unit can input the user's geographical location information data into a generative AI and have the generative AI select the optimal editing method.

[0109] The editing unit can analyze the user's social media activity and suggest relevant revisions during the revision process. For example, the editing unit may prioritize revisions to tourist destinations or events that the user has "liked" on social media. For example, the editing unit may analyze the user's social media posts and suggest relevant revisions. For example, the editing unit may filter revisions based on posts from influencers that the user follows. In this way, relevant revisions can be provided by analyzing the user's social media activity. Some or all of the above processes in the editing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the editing unit may input the user's social media activity data into a generative AI and have the generative AI suggest relevant revisions.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The travel planning system can monitor the user's health status and adjust the travel plan based on that status. For example, if the user is tired, it can suggest a plan that includes relaxing tourist destinations or spas. If the user is active, it can suggest a plan that includes hiking or sports activities. Furthermore, if the user has a specific health problem, it can provide a travel plan that addresses that problem. This allows the system to provide the optimal travel plan tailored to the user's health condition.

[0112] A travel plan creation system can analyze a user's past travel history and suggest plans based on their preferences and tendencies from past trips. For example, it can suggest similar tourist destinations and accommodations based on the user's ratings of previously visited tourist spots and accommodations. It can also suggest plans that include similar activities and events based on information about activities and events the user has participated in in the past. Furthermore, it can suggest the most suitable modes of transportation and routes based on information about the modes of transport and routes the user has used in the past. In this way, it can provide the optimal travel plan based on the user's past travel history.

[0113] A travel planning system can adjust travel plans based on the user's current mood and emotions. For example, if the user is feeling stressed, it can suggest a plan that includes relaxing tourist destinations and activities. If the user is excited, it can suggest a plan that includes active activities and adventurous tourist destinations. Furthermore, if the user is feeling calm, it can suggest a plan that includes quiet tourist destinations and cultural activities. This allows the system to provide the optimal travel plan based on the user's current mood and emotions.

[0114] A travel planning system can analyze a user's social media activity and suggest travel plans based on their interests and preferences on social media. For example, it can display potential destinations based on tourist spots and events that the user has "liked" on social media. It can also analyze the content of the user's social media posts and suggest relevant destinations and activities. Furthermore, it can filter potential destinations based on posts from influencers the user follows. This allows the system to provide the most suitable travel plan based on the user's social media activity.

[0115] A travel planning system can propose the most suitable travel plan from a user's current location, taking into account their geographical location. For example, it can prioritize displaying tourist attractions and accommodations close to the user's current location. It can also suggest the most suitable mode of transportation and route based on the user's current location. Furthermore, it can suggest destinations that are within the user's budget, taking into account the distance from their current location. In this way, it can provide the most suitable travel plan based on the user's geographical location.

[0116] A travel planning system can estimate the user's emotions and adjust the priority of the travel plan based on those emotions. For example, if the user is relaxed, it can suggest a leisurely order of sightseeing. If the user is in a hurry, it can suggest an efficient order of sightseeing. Furthermore, if the user is excited, it can suggest active activities first. This allows the system to provide an optimal travel plan based on the user's emotions.

[0117] The travel plan creation system can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for larger screens. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the system to provide the optimal display method based on the user's device information.

[0118] A travel plan creation system can estimate the user's emotions and adjust how the plan is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display with more detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. This allows the system to provide the optimal display method based on the user's emotions.

[0119] The travel plan creation system can select the optimal display method by referring to the user's past operation history. For example, it can suggest the optimal display method based on the display method the user has preferred in the past. It can also suggest a highly visible display method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and suggest the most efficient display method. In this way, it can provide the optimal display method based on the user's past operation history.

[0120] The travel plan creation system can estimate the user's emotions and adjust the priority of revisions based on those emotions. For example, if the user is stressed, important revisions can be prioritized. If the user is relaxed, detailed revisions can be suggested. Furthermore, if the user is in a hurry, revisions can be quickly implemented. This allows the system to provide optimal revisions based on the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk accepts general information such as destination and budget priorities via chat. Step 2: The generation unit generates a travel plan based on the information received by the reception unit. The generation unit uses a generation AI to generate a travel plan considering map information of tourist destinations, route information, hotel travel site information, accommodation costs, transportation costs, entrance fees, etc. For example, the generation AI generates a plan to visit tourist destinations based on map information, suggests the most suitable mode of transportation based on route information, and suggests hotels that can be stayed at within the budget based on hotel travel site information. Step 3: The delivery unit provides the user with the plan generated by the generation unit. The delivery unit provides the user with the generated plan as a details screen and displays the plan through a web page or mobile app. The plan can also be provided as a printed document. Step 4: The correction submission department accepts user corrections. For example, it accepts corrections entered by the user via chat or by writing on a printed document. Step 5: The revision section incorporates the revisions received by the revision reception section. For example, AI is used to understand the user's intentions and revise the plan. If the user instructs to change an overpriced hotel to a cheaper one, the generating AI will suggest an alternative hotel that fits within the budget. By repeating this process, the optimal travel plan that meets the user's wishes is completed.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, modification reception unit, and modification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a travel plan using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated plan to the user. The modification reception unit is implemented by the reception device 38 of the smart device 14 and receives user modifications. The modification unit is implemented by the specific processing unit 290 of the data processing device 12 and modifies the plan using AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, modification reception unit, and modification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated plan to the user. The modification reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user modifications. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and modifies the plan using AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, modification reception unit, and modification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides the generated plan to the user. The modification reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user modifications. The modification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and modifies the plan using AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, modification reception unit, and modification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated plan to the user. The modification reception unit is implemented by the microphone 238 of the robot 414 and receives user modifications. The modification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and modifies the plan using AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception area that receives user input, A generation unit that generates a travel plan based on the information received by the reception unit, A provisioning unit that provides the plan generated by the generation unit to the user, A correction submission department that accepts user corrections, The system includes a modification unit that reflects the modifications received by the modification reception unit. A system characterized by the following features. (Note 2) The generating unit is The system generates travel plans considering factors such as tourist destination map information, route information, hotel travel site information, accommodation costs, transportation costs, and entrance fees. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generated plan is provided to the user as a details screen. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned correction acceptance unit is: It accepts user chats and comments on printed paper. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned modification section is, Understand the user's intentions and revise the plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, From the completed plan screen, you can purchase tickets from the route information service, book hotels on travel websites, and purchase gift certificates through the Furusato Nozei (hometown tax donation) program. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting information, the input content is filtered based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input data, the system prioritizes inputting more relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the content of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, adjust the level of detail in the plan based on the purpose of the trip. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the travel category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the order of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the plan is prioritized based on the travel dates. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the plan content is adjusted based on the relevance of the trip. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust how the plans we offer are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the displayed plan content will be customized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the plans to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, it analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned correction acceptance unit is: It estimates user sentiment and adjusts the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned correction acceptance unit is: When a correction request is submitted, we analyze the user's past correction history and propose the most suitable correction method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned correction acceptance unit is: When a correction request is submitted, the correction content is filtered based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned correction acceptance unit is: It estimates the user's emotions and adjusts the design of the modified interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned correction acceptance unit is: When receiving a correction request, the system prioritizes accepting corrections that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned correction acceptance unit is: When a correction request is submitted, the system analyzes the user's social media activity and automatically accepts relevant corrections. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned modification section is, The system estimates the user's emotions and adjusts the modifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned modification section is, When making corrections, the system will refer to the user's past correction history to select the most appropriate correction method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned modification section is, When making corrections, customize the corrections based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned modification section is, The system estimates user sentiment and prioritizes the necessary modifications based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned modification section is, When making corrections, the optimal correction method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned modification section is, When making corrections, we analyze users' social media activity and suggest relevant corrections. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, A generation unit that generates a travel plan based on the information received by the reception unit, A provisioning unit that provides the plan generated by the generation unit to the user, A correction submission department that accepts user corrections, The system includes a modification unit that reflects the modifications received by the modification reception unit. A system characterized by the following features.

2. The generating unit is The system generates travel plans considering factors such as tourist destination map information, route information, hotel travel site information, accommodation costs, transportation costs, and entrance fees. The system according to feature 1.

3. The aforementioned supply unit is, The generated plan is provided to the user as a details screen. The system according to feature 1.

4. The aforementioned correction acceptance unit is: It accepts user chats and comments on printed paper. The system according to feature 1.

5. The aforementioned modification section is, Understand the user's intentions and revise the plan accordingly. The system according to feature 1.

6. The aforementioned supply unit is, From the completed plan screen, you can purchase tickets from the route information service, book hotels on travel websites, and purchase gift certificates through the Furusato Nozei (hometown tax donation) program. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system according to feature 1.

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