System

Abstract

An object of a system according to an embodiment is to provide an optimal travel plan only by inputting a place where a user wants to go.SOLUTION: A system includes a reception part, a generation part, a provision part, and a real-time data reflection part. The reception unit receives an input of a place the user wants to go from the user. The generation unit generates a travel plan based on the information received by the reception unit. The providing section provides the user with the travel plan generated by the generation section. The real-time data reflecting section reflects the real-time data based on the travel plan provided by the providing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, even if a user decides on the places they want to go, there is a problem in that it is difficult to create an optimal travel plan due to lack of time or knowledge.

[0005] The system according to the embodiment aims to provide an optimal travel plan by allowing a user to simply input the places they want to go. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a real-time data reflection unit. The reception unit receives input of destinations from a user. The generation unit generates a travel plan based on the information received by the reception unit. The provision unit provides the user with the travel plan generated by the generation unit. The real-time data reflection unit reflects real-time data based on the travel plan provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal travel plan simply by inputting the place where the user wants to go. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a travel plan proposal system allows a user to input desired destinations, and a generation AI generates an optimal travel plan and provides it to the user. The travel plan proposal system allows a user to input desired destinations, and a generation AI generates an optimal travel plan based on the destinations. This plan includes the order in which tourist attractions are visited, transportation options, accommodations, and dining options. The generated plan is customized according to the user's preferences and constraints. For example, the travel plan proposal system allows a user to input desired destinations. For example, the user may simply input specific tourist attractions and city names. The travel plan proposal system then uses a generation AI to analyze the input information and generate an optimal travel plan. The generation AI refers to a database of tourist attractions and determines the order in which tourist attractions are visited based on the user's preferences. For example, the generation AI suggests an order for efficiently touring famous tourist attractions in Kyoto. Transportation options, accommodations, and dining options are also suggested. For example, the proposed travel plan may include transportation options that take train and bus timetables into account, accommodations that fit the user's budget, and dining options where users can enjoy local specialties. The generated travel plan is customized according to the user's preferences and constraints. For example, if a user wants to visit a specific tourist spot or travel at a specific time, the plan is adjusted according to the user's request. This allows the user to easily obtain the optimal travel plan for themselves. The travel plan suggestion system also reflects real-time information. For example, it has a function to dynamically adjust the plan taking into account real-time data such as weather and traffic conditions. This prevents travel problems before they occur. Users can always obtain a plan based on the latest information and enjoy their trip with peace of mind. This makes it easy for people who have decided where they want to go but are short on time or knowledge and find it difficult to plan their trip. For example, busy businessmen and first-time travelers can enjoy a fulfilling trip by simply following the plan proposed by the generation AI. The travel plan suggestion system also reflects real-time information. For example, it has a function to dynamically adjust the plan taking into account real-time data such as weather and traffic conditions.This will prevent any troubles during the trip, and users will always be able to get plans based on the latest information, allowing them to enjoy their trip with peace of mind.

[0029] The travel plan proposal system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a real-time data reflection unit. The reception unit accepts input from a user of a desired destination. For example, the user need only input specific tourist spots or city names. The reception unit can accept information such as, for example, "I want to go to Kyoto." The generation unit uses a generation AI to generate a travel plan based on the information accepted by the reception unit. The generation AI references a database of tourist spots and determines the order in which to visit the tourist spots based on the user's preferences. For example, the generation AI proposes an order for efficiently visiting Kyoto's famous tourist spots. The generation unit also proposes transportation options, accommodation options, and dining options. For example, the generation AI proposes transportation options that take train and bus timetables into account, accommodation options that fit the user's budget, and dining options where users can enjoy local specialties. Some or all of the above-described processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). The provision unit provides the travel plan generated by the generation unit to the user. The providing unit customizes the travel plan according to, for example, the user's wishes and restrictions. For example, the plan is adjusted according to the user's requests, such as when the user wants to visit a specific tourist spot or travel at a specific time. The real-time data reflecting unit reflects real-time data based on the travel plan provided by the providing unit. The real-time data reflecting unit dynamically adjusts the plan, taking into account real-time data such as weather and traffic conditions. This makes it possible to prevent trouble during travel. Some or all of the above-mentioned processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). As a result, the travel plan suggestion system according to the embodiment can generate an optimal travel plan based on the user's desired destinations and reflect real-time data to provide the user with an optimal travel plan.

[0030] The reception unit can accept detailed input from the user. The detailed input includes, for example, the purpose of the trip, a budget, and information about traveling companions, but is not limited to these examples. For example, the reception unit accepts the user's input of the purpose of the trip. The reception unit can also accept the user's input of a budget. The reception unit can also accept the user's input of information about traveling companions. By accepting detailed input from the user, a more accurate travel plan can be generated. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's detailed input to AI, and the AI ​​can analyze the information.

[0031] The generation unit can collect and analyze the user's profile data. The profile data includes, but is not limited to, for example, age, gender, hobbies, and past travel history. For example, the generation unit can collect and analyze the user's age. The generation unit can also collect and analyze the user's gender. The generation unit can also collect and analyze the user's hobbies. The generation unit can also collect and analyze the user's past travel history. In this way, by collecting and analyzing the user's profile data, it is possible to generate an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's profile data into the generation AI, which can then analyze the data.

[0032] The generation unit can refer to a database of tourist attractions and determine the order in which tourist attractions should be visited based on the user's preferences. The database of tourist attractions includes, for example, but is not limited to, the names, locations, ratings, and number of visitors of the tourist attractions. For example, the generation unit can refer to the names of the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. The generation unit can also refer to the locations of the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. The generation unit can also refer to the ratings of the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. Furthermore, the generation unit can refer to the number of visitors to the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. This makes it possible to provide an efficient travel plan by referring to the database of tourist attractions and determining the order in which the attractions should be visited based on the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the database of tourist attractions into the generation AI, which can then analyze the data.

[0033] The generation unit can suggest transportation options, accommodation options, and dining options. Examples of transportation options include, but are not limited to, trains, buses, taxis, and rental cars. Examples of accommodation options include, but are not limited to, hotels, inns, guesthouses, and guesthouses. Examples of dining options include, but are not limited to, restaurants, cafes, and fast food restaurants. The generation unit can suggest transportation options taking into account, for example, train timetables. The generation unit can also suggest accommodation options based on the user's budget. The generation unit can also suggest dining options where local specialties can be enjoyed. In this way, by suggesting transportation options, accommodation options, and dining options, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input data on transportation options, accommodation options, and dining options into the generation AI, which can then analyze the data.

[0034] The providing unit can customize the travel plan according to the user's wishes and constraints. Examples of the user's wishes and constraints include, but are not limited to, a desire to visit a specific tourist spot or a desire to travel during a specific time period. For example, if the user wishes to visit a specific tourist spot, the providing unit customizes the plan to include that tourist spot. Furthermore, if the user wishes to travel during a specific time period, the providing unit can also customize the plan to match that time period. This allows the travel plan to be customized according to the user's wishes and constraints, thereby providing an optimal travel plan for the user. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's wishes and constraints into AI, which then analyzes the information.

[0035] The real-time data reflecting unit can dynamically adjust the plan taking into account real-time data such as weather and traffic conditions. Real-time data includes, but is not limited to, weather information, traffic information, and event information. The real-time data reflecting unit adjusts the plan taking into account, for example, weather information. The real-time data reflecting unit can also adjust the plan taking into account traffic information. The real-time data reflecting unit can also adjust the plan taking into account event information. In this way, by dynamically adjusting the plan taking into account real-time data such as weather and traffic conditions, it is possible to prevent trouble during travel. Some or all of the above-mentioned processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input weather information and traffic information into the generation AI, which can then analyze the data and adjust the plan.

[0036] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest places that the user will visit during a specific time period based on the user's past travel history. In this way, by analyzing the user's past travel history, the optimal input method for the user can be suggested. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data into a generation AI, which can analyze the data and suggest the optimal input method.

[0037] When a user inputs a desired destination, the reception unit can present input candidates based on the user's current interests. For example, the reception unit can present related place candidates based on tourist attractions or events recently searched by the user. The reception unit can also suggest related tourist attractions based on places the user has "liked" on social media. The reception unit can also present places that the user may be interested in based on the content of articles or blogs the user has recently read. This allows the system to provide the user with an optimal travel plan by presenting input candidates based on the user's current interests. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's interest data into a generation AI, which can then analyze the data and present input candidates.

[0038] The reception unit can select the optimal input means according to the user's input method when inputting a desired destination. For example, the reception unit can automatically set the desired destination simply by the user inputting "I want to go to Kyoto" by voice. The reception unit can also display related tourist destination candidates when the user inputs "Kyoto" by text. The reception unit can also present tourist destinations related to the image as candidates when the user uploads an image. This allows the optimal input means to be selected according to the user's input method, thereby providing the user with an optimal input experience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's input data to a generation AI, which can analyze the data and select the optimal input means.

[0039] When the user inputs a desired destination, the reception unit can prioritize presenting highly relevant locations in consideration of the user's geographical location information. For example, the reception unit can prioritize presenting tourist spots close to the user's current location. The reception unit can also prioritize presenting tourist spots close to places the user has previously visited. The reception unit can also prioritize presenting tourist spots that are easily accessible in consideration of the user's mode of transportation from the user's current location. This allows the user to be provided with an optimal travel plan by prioritizing highly relevant locations in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI, which can analyze the data and present highly relevant locations.

[0040] When the user inputs a desired destination, the reception unit can analyze the user's social media activity and suggest related places. For example, the reception unit can suggest related tourist spots based on the places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. The reception unit can also suggest related tourist spots based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can analyze the data and suggest related places.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has previously preferred. The reception unit can also avoid input methods that the user has previously dissatisfied with and suggest alternative methods. The reception unit can also customize the optimal input method based on the user's past feedback. In this way, by customizing the input method by reflecting the user's past feedback, it is possible to provide the user with an optimal input experience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI, which can then analyze the data to customize the input method.

[0042] When generating a travel plan, the generation unit can improve the accuracy of the plan by referring to the user's past travel history. For example, the generation unit can suggest an optimal visiting order based on tourist spots that the user has visited in the past. The generation unit can also suggest a route that avoids crowds based on the user's past travel history. The generation unit can also analyze the user's past travel history and suggest the most efficient travel plan. In this way, by referring to the user's past travel history, it is possible to provide the user with an optimal travel plan. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's past travel history data into the generation AI, which can then analyze the data to improve the accuracy of the plan.

[0043] When generating a travel plan, the generation unit can analyze the user's profile data in detail and propose an optimal plan. The generation unit can propose optimal tourist spots based on profile data such as the user's age, gender, and hobbies. The generation unit can also propose an optimal travel plan taking into account the user's budget and length of stay. The generation unit can also propose an optimal travel plan based on the user's past travel history and feedback. In this way, by analyzing the user's profile data in detail, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's profile data into the generation AI, which can then analyze the data and propose an optimal plan.

[0044] When generating a travel plan, the generation unit can refer to a database of tourist attractions and reflect the latest information. For example, the generation unit can propose an optimal visiting order, taking into account the business hours and regular holidays of tourist attractions. The generation unit can also propose an optimal travel plan based on the latest event information at tourist attractions. The generation unit can also reflect the congestion status of tourist attractions in real time and propose optimal visiting times. In this way, by referring to the database of tourist attractions and reflecting the latest information, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input a database of tourist attractions into the generation AI, which can analyze the data and reflect the latest information.

[0045] When generating a travel plan, the generation unit can determine the priority of the plan based on the time of submission by the user. For example, if the user submits the plan early, the generation unit can prioritize generating a detailed travel plan. Furthermore, if the user submits the plan at the last minute, the generation unit can also prioritize a concise travel plan that can be generated quickly. Furthermore, if the user is participating in a specific event, the generation unit can also prioritize generating a travel plan that is tailored to the event. In this way, by determining the priority of plans based on the time of submission by the user, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's submission time data into the generation AI, which can analyze the data and determine the priority of the plans.

[0046] When generating a travel plan, the generation unit can adjust the order of the plan based on the user's relevance. For example, the generation unit can suggest an order in which to visit tourist spots that the user is interested in, with priority. The generation unit can also suggest an order in which to visit tourist spots that are highly relevant, with priority, based on the user's past travel history. The generation unit can also suggest an order in which to visit tourist spots that are highly relevant, with priority, based on the user's profile data. By adjusting the order of the plan based on the user's relevance, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's relevance data into the generation AI, which can then analyze the data and adjust the order of the plan.

[0047] When generating a travel plan, the generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user is a novice traveler, the generation unit can avoid technical terminology and explain things in simple terms. Furthermore, if the user is an experienced traveler, the generation unit can also provide detailed explanations using technical terminology. The generation unit can also adjust the optimal level of technical terminology use based on the user's profile data. This allows the optimal travel plan for the user to be provided by adjusting the use of technical terminology in the plan according to the user's level of expertise. Some or all of the above-described processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's level of expertise data into the generation AI, which can then analyze the data and adjust the use of technical terminology.

[0048] When providing a travel plan, the providing unit can optimize the delivery method by referring to the user's past feedback. For example, the providing unit prioritizes the use of a delivery method (email, app notification, etc.) that the user has previously preferred. The providing unit can also avoid a delivery method that the user has previously dissatisfied with and use a different method. The providing unit can also customize the optimal delivery method based on the user's past feedback. In this way, the delivery method can be optimized by referring to the user's past feedback, thereby providing the optimal delivery method for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into a generation AI, which can analyze the data to optimize the delivery method.

[0049] When providing a travel plan, the providing unit can customize the provided content based on the user's current situation. For example, if the user is at their current location, the providing unit can suggest the optimal means of transportation from that location. Furthermore, if the user is participating in a specific event, the providing unit can also provide information related to that event. Furthermore, if the user is at a specific time period, the providing unit can also suggest tourist spots and dining spots that are optimal for that time period. By customizing the provided content based on the user's current situation, it is possible to provide an optimal travel plan for the user. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the user's current situation into a generating AI, which can then analyze the data and customize the provided content.

[0050] When providing a travel plan, the providing unit can select the optimal means of provision depending on the user's input method. For example, if the user wants to receive the travel plan by voice, the providing unit can provide audio guidance. Furthermore, if the user wants to receive the travel plan by text, the providing unit can also provide a text message. Furthermore, if the user wants to receive the travel plan by image, the providing unit can also provide guidance with images. In this way, by selecting the optimal means of provision depending on the user's input method, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's input data to a generating AI, which can analyze the data and select the optimal means of provision.

[0051] When providing a travel plan, the providing unit can prioritize providing highly relevant plans by taking into account the user's geographical location information. For example, the providing unit can prioritize providing tourist attractions close to the user's current location. The providing unit can also prioritize providing tourist attractions close to places the user has visited in the past. The providing unit can also prioritize providing tourist attractions that are easily accessible by taking into account the user's mode of transportation from the current location. In this way, by prioritizing highly relevant plans by taking into account the user's geographical location information, it is possible to provide an optimal travel plan for the user. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into a generation AI, which can analyze the data and provide a highly relevant plan.

[0052] When providing a travel plan, the providing unit can analyze the user's social media activity and provide a related plan. For example, the providing unit can suggest related tourist attractions based on the locations where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and suggest tourist attractions that the user may be interested in. The providing unit can also suggest related tourist attractions based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI, which can analyze the data and provide a related plan.

[0053] When providing a travel plan, the providing unit can customize the providing method by reflecting the user's past feedback. For example, the providing unit can prioritize the use of a providing method (email, app notification, etc.) that the user has previously preferred. The providing unit can also avoid a providing method that the user has previously dissatisfied with and use a different method. The providing unit can also customize the optimal providing method based on the user's past feedback. In this way, by customizing the providing method by reflecting the user's past feedback, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then analyze the data and customize the providing method.

[0054] The real-time data reflecting unit can improve the accuracy of the data by referring to the user's past travel history when reflecting the real-time data. The real-time data reflecting unit provides real-time information based on, for example, data on tourist spots the user has visited in the past. The real-time data reflecting unit can also provide real-time data for avoiding crowds based on the user's past travel history. The real-time data reflecting unit can also analyze the user's past travel history and provide the most efficient real-time data. This makes it possible to provide optimal real-time data for the user by referring to the user's past travel history. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's past travel history data into a generation AI, which can then analyze the data to improve the accuracy of the data.

[0055] The real-time data reflecting unit can customize data based on the user's current situation when reflecting real-time data. For example, if the user is at their current location, the real-time data reflecting unit can suggest the optimal means of transportation from that location. Furthermore, if the user is participating in a specific event, the real-time data reflecting unit can provide real-time data related to the event. Furthermore, if the user is at a specific time period, the real-time data reflecting unit can provide real-time data on the best tourist spots and dining places for that time period. By customizing data based on the user's current situation, it is possible to provide optimal real-time data for the user. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's current situation data into a generation AI, which can then analyze the data and customize the data.

[0056] The real-time data reflection unit can select the optimal reflection means depending on the user's input method when reflecting real-time data. For example, if the user wants to receive real-time data by voice, the real-time data reflection unit can provide voice guidance. Furthermore, if the user wants to receive real-time data by text, the real-time data reflection unit can also provide a text message. Furthermore, if the user wants to receive real-time data by image, the real-time data reflection unit can also provide guidance with images. In this way, by selecting the optimal reflection means depending on the user's input method, it is possible to provide optimal real-time data for the user. Some or all of the above-mentioned processing in the real-time data reflection unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflection unit can input the user's input data into a generation AI, which can analyze the data and select the optimal reflection means.

[0057] When reflecting real-time data, the real-time data reflecting unit can prioritize reflecting highly relevant data by taking into account the user's geographical location information. For example, the real-time data reflecting unit can prioritize providing real-time data of tourist spots close to the user's current location. The real-time data reflecting unit can also prioritize providing real-time data of tourist spots close to places the user has previously visited. The real-time data reflecting unit can also prioritize providing real-time data of tourist spots that are easily accessible by taking into account the user's mode of transportation from their current location. This allows the user to be provided with optimal real-time data by preferentially reflecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's geographical location information into a generation AI, which can analyze the data and reflect highly relevant data.

[0058] The real-time data reflecting unit can analyze the user's social media activity and reflect related data when reflecting real-time data. The real-time data reflecting unit, for example, provides real-time data on locations where the user has checked in on social media. The real-time data reflecting unit can also analyze the content of the user's social media posts and provide real-time data on tourist spots that the user may be interested in. The real-time data reflecting unit can also provide real-time data on related tourist spots based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide optimal real-time data for the user. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's social media activity data into a generation AI, which can analyze the data and reflect related data.

[0059] The real-time data reflection unit can customize the data reflection method by reflecting the user's past feedback when reflecting real-time data. For example, the real-time data reflection unit prioritizes the use of a data reflection method (voice, text, etc.) that the user previously preferred. The real-time data reflection unit can also avoid a data reflection method that the user previously dissatisfied with and use a different method. The real-time data reflection unit can also customize the optimal data reflection method based on the user's past feedback. In this way, by customizing the data reflection method by reflecting the user's past feedback, it is possible to provide optimal real-time data for the user. Some or all of the above-mentioned processing in the real-time data reflection unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflection unit can input the user's past feedback data into the generation AI, which can then analyze the data and customize the data reflection method.

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

[0061] The generation unit can collect the user's ratings of past travel plans and reflect them in the next plan generation. For example, tourist spots that the user has previously rated highly can be included preferentially. Tourist spots that the user has previously rated poorly can also be excluded. Furthermore, similar tourist spots can be suggested based on the user's ratings. This makes it possible to provide an optimal travel plan that reflects the user's past ratings.

[0062] The provision unit can collect data on the user's behavior during the trip and adjust the plan in real time. For example, if the user visits a tourist spot earlier than planned, the next tourist spot can be suggested early. Also, if the user is running behind schedule, the plan can be adjusted to make the schedule more reasonable. Furthermore, if the user stays at a particular location for a long time, additional information related to that location can be provided. This makes it possible to provide a flexible travel plan that matches the user's behavior.

[0063] The generation unit can analyze photos and videos taken during the user's trip and reflect them in the next travel plan. For example, tourist spots where the user has taken many photos can be prioritized for inclusion in the next plan. It can also suggest activities for which the user has taken videos in the next plan. Furthermore, if the user has taken photos based on a specific theme, tourist spots related to that theme can be suggested. This makes it possible to provide the user with an optimal travel plan based on their interests.

[0064] The generation unit can analyze the success rate of the user's past travel plans and reflect this in the generation of the next plan. For example, elements of plans that the user has previously succeeded in can be incorporated into the next plan. Elements of plans that the user has previously failed in can also be avoided. Furthermore, similar plans can be suggested based on the user's success rate. This makes it possible to provide an optimal travel plan that reflects the user's past successful experiences.

[0065] The real-time data reflecting unit can provide real-time data taking into account the user's current activity level. For example, if the user is actively moving, the unit can prioritize suggesting the next activity. Also, if the user is taking a break, the unit can suggest places and activities where the user can relax. Furthermore, if the user stays in a particular place for a long time, the unit can provide additional information related to that place. This makes it possible to provide optimal real-time data according to the user's activity level.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit accepts input from the user about the place they want to go. The user only needs to input the specific tourist spot or city name. For example, the reception unit accepts information input by the user such as "I want to go to Kyoto." Step 2: The generation unit uses the generation AI to generate a travel plan based on the information received by the reception unit. The generation AI references a database of tourist attractions and determines the order in which to visit the tourist attractions based on the user's preferences. It also suggests transportation methods, accommodations, and places to eat. For example, it suggests transportation methods that take train and bus timetables into account, accommodations that fit the user's budget, and places to eat where you can enjoy local specialties. Step 3: The provider provides the travel plan generated by the generator to the user. The provider customizes the travel plan according to the user's wishes and restrictions. For example, if the user wants to visit a specific tourist spot or travel during a specific time period, the plan is adjusted according to the user's requests. Step 4: The real-time data reflecting unit reflects real-time data based on the travel plan provided by the providing unit. The real-time data reflecting unit dynamically adjusts the plan, taking into account real-time data such as weather and traffic conditions. This makes it possible to prevent trouble during the trip.

[0068] (Example 2) In an embodiment of the present invention, a travel plan proposal system allows a user to input desired destinations, and a generation AI generates an optimal travel plan and provides it to the user. The travel plan proposal system allows a user to input desired destinations, and a generation AI generates an optimal travel plan based on the destinations. This plan includes the order in which tourist attractions are visited, transportation options, accommodations, and dining options. The generated plan is customized according to the user's preferences and constraints. For example, the travel plan proposal system allows a user to input desired destinations. For example, the user may simply input specific tourist attractions and city names. The travel plan proposal system then uses a generation AI to analyze the input information and generate an optimal travel plan. The generation AI refers to a database of tourist attractions and determines the order in which tourist attractions are visited based on the user's preferences. For example, the generation AI suggests an order for efficiently touring famous tourist attractions in Kyoto. Transportation options, accommodations, and dining options are also suggested. For example, the proposed travel plan may include transportation options that take train and bus timetables into account, accommodations that fit the user's budget, and dining options where users can enjoy local specialties. The generated travel plan is customized according to the user's preferences and constraints. For example, if a user wants to visit a specific tourist spot or travel at a specific time, the plan is adjusted according to the user's request. This allows the user to easily obtain the optimal travel plan for themselves. The travel plan suggestion system also reflects real-time information. For example, it has a function to dynamically adjust the plan taking into account real-time data such as weather and traffic conditions. This prevents travel problems before they occur. Users can always obtain a plan based on the latest information and enjoy their trip with peace of mind. This makes it easy for people who have decided where they want to go but are short on time or knowledge and find it difficult to plan their trip. For example, busy businessmen and first-time travelers can enjoy a fulfilling trip by simply following the plan proposed by the generation AI. The travel plan suggestion system also reflects real-time information. For example, it has a function to dynamically adjust the plan taking into account real-time data such as weather and traffic conditions.This will prevent any troubles during the trip, and users will always be able to get plans based on the latest information, allowing them to enjoy their trip with peace of mind.

[0069] The travel plan proposal system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a real-time data reflection unit. The reception unit accepts input from a user of a desired destination. For example, the user need only input specific tourist spots or city names. The reception unit can accept information such as, for example, "I want to go to Kyoto." The generation unit uses a generation AI to generate a travel plan based on the information accepted by the reception unit. The generation AI references a database of tourist spots and determines the order in which to visit the tourist spots based on the user's preferences. For example, the generation AI proposes an order for efficiently visiting Kyoto's famous tourist spots. The generation unit also proposes transportation options, accommodation options, and dining options. For example, the generation AI proposes transportation options that take train and bus timetables into account, accommodation options that fit the user's budget, and dining options where users can enjoy local specialties. Some or all of the above-described processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). The provision unit provides the travel plan generated by the generation unit to the user. The providing unit customizes the travel plan according to, for example, the user's wishes and restrictions. For example, the plan is adjusted according to the user's requests, such as when the user wants to visit a specific tourist spot or travel at a specific time. The real-time data reflecting unit reflects real-time data based on the travel plan provided by the providing unit. The real-time data reflecting unit dynamically adjusts the plan, taking into account real-time data such as weather and traffic conditions. This makes it possible to prevent trouble during travel. Some or all of the above-mentioned processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). As a result, the travel plan suggestion system according to the embodiment can generate an optimal travel plan based on the user's desired destinations and reflect real-time data to provide the user with an optimal travel plan.

[0070] The reception unit can accept detailed input from the user. The detailed input includes, for example, the purpose of the trip, a budget, and information about traveling companions, but is not limited to these examples. For example, the reception unit accepts the user's input of the purpose of the trip. The reception unit can also accept the user's input of a budget. The reception unit can also accept the user's input of information about traveling companions. By accepting detailed input from the user, a more accurate travel plan can be generated. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's detailed input to AI, and the AI ​​can analyze the information.

[0071] The generation unit can collect and analyze the user's profile data. The profile data includes, but is not limited to, for example, age, gender, hobbies, and past travel history. For example, the generation unit can collect and analyze the user's age. The generation unit can also collect and analyze the user's gender. The generation unit can also collect and analyze the user's hobbies. The generation unit can also collect and analyze the user's past travel history. In this way, by collecting and analyzing the user's profile data, it is possible to generate an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's profile data into the generation AI, which can then analyze the data.

[0072] The generation unit can refer to a database of tourist attractions and determine the order in which tourist attractions should be visited based on the user's preferences. The database of tourist attractions includes, for example, but is not limited to, the names, locations, ratings, and number of visitors of the tourist attractions. For example, the generation unit can refer to the names of the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. The generation unit can also refer to the locations of the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. The generation unit can also refer to the ratings of the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. Furthermore, the generation unit can refer to the number of visitors to the tourist attractions and determine the order in which the attractions should be visited based on the user's preferences. This makes it possible to provide an efficient travel plan by referring to the database of tourist attractions and determining the order in which the attractions should be visited based on the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the database of tourist attractions into the generation AI, which can then analyze the data.

[0073] The generation unit can suggest transportation options, accommodation options, and dining options. Examples of transportation options include, but are not limited to, trains, buses, taxis, and rental cars. Examples of accommodation options include, but are not limited to, hotels, inns, guesthouses, and guesthouses. Examples of dining options include, but are not limited to, restaurants, cafes, and fast food restaurants. The generation unit can suggest transportation options taking into account, for example, train timetables. The generation unit can also suggest accommodation options based on the user's budget. The generation unit can also suggest dining options where local specialties can be enjoyed. In this way, by suggesting transportation options, accommodation options, and dining options, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input data on transportation options, accommodation options, and dining options into the generation AI, which can then analyze the data.

[0074] The providing unit can customize the travel plan according to the user's wishes and constraints. Examples of the user's wishes and constraints include, but are not limited to, a desire to visit a specific tourist spot or a desire to travel during a specific time period. For example, if the user wishes to visit a specific tourist spot, the providing unit customizes the plan to include that tourist spot. Furthermore, if the user wishes to travel during a specific time period, the providing unit can also customize the plan to match that time period. This allows the travel plan to be customized according to the user's wishes and constraints, thereby providing an optimal travel plan for the user. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's wishes and constraints into AI, which then analyzes the information.

[0075] The real-time data reflecting unit can dynamically adjust the plan taking into account real-time data such as weather and traffic conditions. Real-time data includes, but is not limited to, weather information, traffic information, and event information. The real-time data reflecting unit adjusts the plan taking into account, for example, weather information. The real-time data reflecting unit can also adjust the plan taking into account traffic information. The real-time data reflecting unit can also adjust the plan taking into account event information. In this way, by dynamically adjusting the plan taking into account real-time data such as weather and traffic conditions, it is possible to prevent trouble during travel. Some or all of the above-mentioned processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input weather information and traffic information into the generation AI, which can then analyze the data and adjust the plan.

[0076] The reception unit can estimate the user's emotions and adjust the input method for the destination based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the destination. This allows the input method to be adjusted according to the user's emotions, thereby providing an optimal input experience for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then analyze the data and adjust the input method.

[0077] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest places that the user will visit during a specific time period based on the user's past travel history. In this way, by analyzing the user's past travel history, the optimal input method for the user can be suggested. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data into a generation AI, which can analyze the data and suggest the optimal input method.

[0078] When a user inputs a desired destination, the reception unit can present input candidates based on the user's current interests. For example, the reception unit can present related place candidates based on tourist attractions or events recently searched by the user. The reception unit can also suggest related tourist attractions based on places the user has "liked" on social media. The reception unit can also present places that the user may be interested in based on the content of articles or blogs the user has recently read. This allows the system to provide the user with an optimal travel plan by presenting input candidates based on the user's current interests. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's interest data into a generation AI, which can then analyze the data and present input candidates.

[0079] The reception unit can select the optimal input means according to the user's input method when inputting a desired destination. For example, the reception unit can automatically set the desired destination simply by the user inputting "I want to go to Kyoto" by voice. The reception unit can also display related tourist destination candidates when the user inputs "Kyoto" by text. The reception unit can also present tourist destinations related to the image as candidates when the user uploads an image. This allows the optimal input means to be selected according to the user's input method, thereby providing the user with an optimal input experience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's input data to a generation AI, which can analyze the data and select the optimal input means.

[0080] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize displaying important input items and postpone displaying other items. Furthermore, if the user is relaxed, the reception unit can display detailed input items in order, allowing the user to freely select. Furthermore, if the user is in a hurry, the reception unit can display only the most important input items, allowing the user to quickly complete input. This allows the user to have an optimal input experience by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can analyze the data and prioritize the input content.

[0081] When the user inputs a desired destination, the reception unit can prioritize presenting highly relevant locations in consideration of the user's geographical location information. For example, the reception unit can prioritize presenting tourist spots close to the user's current location. The reception unit can also prioritize presenting tourist spots close to places the user has previously visited. The reception unit can also prioritize presenting tourist spots that are easily accessible in consideration of the user's mode of transportation from the user's current location. This allows the user to be provided with an optimal travel plan by prioritizing highly relevant locations in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI, which can analyze the data and present highly relevant locations.

[0082] When the user inputs a desired destination, the reception unit can analyze the user's social media activity and suggest related places. For example, the reception unit can suggest related tourist spots based on the places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest tourist spots that the user may be interested in. The reception unit can also suggest related tourist spots based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can analyze the data and suggest related places.

[0083] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has previously preferred. The reception unit can also avoid input methods that the user has previously dissatisfied with and suggest alternative methods. The reception unit can also customize the optimal input method based on the user's past feedback. In this way, by customizing the input method by reflecting the user's past feedback, it is possible to provide the user with an optimal input experience. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI, which can then analyze the data to customize the input method.

[0084] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit can generate a travel plan that adds visually stimulating effects. By adjusting the travel plan generation method according to the user's emotions, it is possible to provide an optimal travel plan for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the travel plan generation method.

[0085] When generating a travel plan, the generation unit can improve the accuracy of the plan by referring to the user's past travel history. For example, the generation unit can suggest an optimal visiting order based on tourist spots that the user has visited in the past. The generation unit can also suggest a route that avoids crowds based on the user's past travel history. The generation unit can also analyze the user's past travel history and suggest the most efficient travel plan. In this way, by referring to the user's past travel history, it is possible to provide the user with an optimal travel plan. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's past travel history data into the generation AI, which can then analyze the data to improve the accuracy of the plan.

[0086] When generating a travel plan, the generation unit can analyze the user's profile data in detail and propose an optimal plan. The generation unit can propose optimal tourist spots based on profile data such as the user's age, gender, and hobbies. The generation unit can also propose an optimal travel plan taking into account the user's budget and length of stay. The generation unit can also propose an optimal travel plan based on the user's past travel history and feedback. In this way, by analyzing the user's profile data in detail, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's profile data into the generation AI, which can then analyze the data and propose an optimal plan.

[0087] When generating a travel plan, the generation unit can refer to a database of tourist attractions and reflect the latest information. For example, the generation unit can propose an optimal visiting order, taking into account the business hours and regular holidays of tourist attractions. The generation unit can also propose an optimal travel plan based on the latest event information at tourist attractions. The generation unit can also reflect the congestion status of tourist attractions in real time and propose optimal visiting times. In this way, by referring to the database of tourist attractions and reflecting the latest information, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input a database of tourist attractions into the generation AI, which can analyze the data and reflect the latest information.

[0088] The generation unit can estimate the user's emotions and adjust the level of detail of the travel plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan with detailed descriptions. If the user is in a hurry, the generation unit can generate a concise travel plan that focuses on the main points. If the user is excited, the generation unit can generate a travel plan with visually stimulating effects. This allows the user to be provided with an optimal travel plan by adjusting the level of detail of the travel plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the level of detail of the travel plan.

[0089] When generating a travel plan, the generation unit can determine the priority of the plan based on the time of submission by the user. For example, if the user submits the plan early, the generation unit can prioritize generating a detailed travel plan. Furthermore, if the user submits the plan at the last minute, the generation unit can also prioritize a concise travel plan that can be generated quickly. Furthermore, if the user is participating in a specific event, the generation unit can also prioritize generating a travel plan that is tailored to the event. In this way, by determining the priority of plans based on the time of submission by the user, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's submission time data into the generation AI, which can analyze the data and determine the priority of the plans.

[0090] When generating a travel plan, the generation unit can adjust the order of the plan based on the user's relevance. For example, the generation unit can suggest an order in which to visit tourist spots that the user is interested in, with priority. The generation unit can also suggest an order in which to visit tourist spots that are highly relevant, with priority, based on the user's past travel history. The generation unit can also suggest an order in which to visit tourist spots that are highly relevant, with priority, based on the user's profile data. By adjusting the order of the plan based on the user's relevance, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's relevance data into the generation AI, which can then analyze the data and adjust the order of the plan.

[0091] When generating a travel plan, the generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user is a novice traveler, the generation unit can avoid technical terminology and explain things in simple terms. Furthermore, if the user is an experienced traveler, the generation unit can also provide detailed explanations using technical terminology. The generation unit can also adjust the optimal level of technical terminology use based on the user's profile data. This allows the optimal travel plan for the user to be provided by adjusting the use of technical terminology in the plan according to the user's level of expertise. Some or all of the above-described processing in the generation unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation unit can input the user's level of expertise data into the generation AI, which can then analyze the data and adjust the use of technical terminology.

[0092] The providing unit can estimate the user's emotions and adjust the way the travel plan is provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide a travel plan with detailed descriptions. If the user is in a hurry, the providing unit can provide a concise travel plan that focuses on the main points. If the user is excited, the providing unit can provide a travel plan with visually stimulating effects. This allows the optimal travel plan for the user to be provided by adjusting the way the travel plan is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the way the travel plan is provided.

[0093] When providing a travel plan, the providing unit can optimize the delivery method by referring to the user's past feedback. For example, the providing unit prioritizes the use of a delivery method (email, app notification, etc.) that the user has previously preferred. The providing unit can also avoid a delivery method that the user has previously dissatisfied with and use a different method. The providing unit can also customize the optimal delivery method based on the user's past feedback. In this way, the delivery method can be optimized by referring to the user's past feedback, thereby providing the optimal delivery method for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into a generation AI, which can analyze the data to optimize the delivery method.

[0094] When providing a travel plan, the providing unit can customize the provided content based on the user's current situation. For example, if the user is at their current location, the providing unit can suggest the optimal means of transportation from that location. Furthermore, if the user is participating in a specific event, the providing unit can also provide information related to that event. Furthermore, if the user is at a specific time period, the providing unit can also suggest tourist spots and dining spots that are optimal for that time period. By customizing the provided content based on the user's current situation, it is possible to provide an optimal travel plan for the user. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input data on the user's current situation into a generating AI, which can then analyze the data and customize the provided content.

[0095] When providing a travel plan, the providing unit can select the optimal means of provision depending on the user's input method. For example, if the user wants to receive the travel plan by voice, the providing unit can provide audio guidance. Furthermore, if the user wants to receive the travel plan by text, the providing unit can also provide a text message. Furthermore, if the user wants to receive the travel plan by image, the providing unit can also provide guidance with images. In this way, by selecting the optimal means of provision depending on the user's input method, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's input data to a generating AI, which can analyze the data and select the optimal means of provision.

[0096] The providing unit can estimate the user's emotions and prioritize the plans to provide based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important information and postpone other information. Furthermore, if the user is relaxed, the providing unit can provide detailed information in order, allowing the user to freely select. Furthermore, if the user is in a hurry, the providing unit can provide only the most important information, allowing the user to quickly understand. This allows the optimal travel plan to be provided by prioritizing the plans to be provided based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into a generation AI, which then analyzes the data to prioritize the plans to provide.

[0097] When providing a travel plan, the providing unit can prioritize providing highly relevant plans by taking into account the user's geographical location information. For example, the providing unit can prioritize providing tourist attractions close to the user's current location. The providing unit can also prioritize providing tourist attractions close to places the user has visited in the past. The providing unit can also prioritize providing tourist attractions that are easily accessible by taking into account the user's mode of transportation from the current location. In this way, by prioritizing highly relevant plans by taking into account the user's geographical location information, it is possible to provide an optimal travel plan for the user. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into a generation AI, which can analyze the data and provide a highly relevant plan.

[0098] When providing a travel plan, the providing unit can analyze the user's social media activity and provide a related plan. For example, the providing unit can suggest related tourist attractions based on the locations where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and suggest tourist attractions that the user may be interested in. The providing unit can also suggest related tourist attractions based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI, which can analyze the data and provide a related plan.

[0099] When providing a travel plan, the providing unit can customize the providing method by reflecting the user's past feedback. For example, the providing unit can prioritize the use of a providing method (email, app notification, etc.) that the user has previously preferred. The providing unit can also avoid a providing method that the user has previously dissatisfied with and use a different method. The providing unit can also customize the optimal providing method based on the user's past feedback. In this way, by customizing the providing method by reflecting the user's past feedback, it is possible to provide an optimal travel plan for the user. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can then analyze the data and customize the providing method.

[0100] The real-time data reflecting unit can estimate the user's emotions and adjust the method of reflecting the real-time data based on the estimated user emotions. For example, if the user is relaxed, the real-time data reflecting unit can provide detailed real-time data. If the user is in a hurry, the real-time data reflecting unit can also provide concise real-time data that focuses on the main points. If the user is excited, the real-time data reflecting unit can also provide real-time data with visually stimulating effects. This allows the user to be provided with an optimal travel plan by adjusting the method of reflecting the real-time data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the real-time data reflecting unit can be performed using or without the generation AI. For example, the real-time data reflecting unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the method of reflecting the real-time data.

[0101] The real-time data reflecting unit can improve the accuracy of the data by referring to the user's past travel history when reflecting the real-time data. The real-time data reflecting unit provides real-time information based on, for example, data on tourist spots the user has visited in the past. The real-time data reflecting unit can also provide real-time data for avoiding crowds based on the user's past travel history. The real-time data reflecting unit can also analyze the user's past travel history and provide the most efficient real-time data. This makes it possible to provide optimal real-time data for the user by referring to the user's past travel history. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's past travel history data into a generation AI, which can then analyze the data to improve the accuracy of the data.

[0102] The real-time data reflecting unit can customize data based on the user's current situation when reflecting real-time data. For example, if the user is at their current location, the real-time data reflecting unit can suggest the optimal means of transportation from that location. Furthermore, if the user is participating in a specific event, the real-time data reflecting unit can provide real-time data related to the event. Furthermore, if the user is at a specific time period, the real-time data reflecting unit can provide real-time data on the best tourist spots and dining places for that time period. By customizing data based on the user's current situation, it is possible to provide optimal real-time data for the user. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's current situation data into a generation AI, which can then analyze the data and customize the data.

[0103] The real-time data reflection unit can select the optimal reflection means depending on the user's input method when reflecting real-time data. For example, if the user wants to receive real-time data by voice, the real-time data reflection unit can provide voice guidance. Furthermore, if the user wants to receive real-time data by text, the real-time data reflection unit can also provide a text message. Furthermore, if the user wants to receive real-time data by image, the real-time data reflection unit can also provide guidance with images. In this way, by selecting the optimal reflection means depending on the user's input method, it is possible to provide optimal real-time data for the user. Some or all of the above-mentioned processing in the real-time data reflection unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflection unit can input the user's input data into a generation AI, which can analyze the data and select the optimal reflection means.

[0104] The real-time data reflecting unit can estimate the user's emotions and determine the priority of data to be reflected based on the estimated user emotions. For example, if the user is nervous, the real-time data reflecting unit can prioritize providing important real-time data and postpone other data. Furthermore, if the user is relaxed, the real-time data reflecting unit can provide detailed real-time data in order, allowing the user to freely select. Furthermore, if the user is in a hurry, the real-time data reflecting unit can provide only the most important real-time data, allowing the user to quickly understand. This allows the optimal real-time data to be provided by determining the priority of data to be reflected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the real-time data reflecting unit can be performed using the generation AI, or without the generation AI. For example, the real-time data reflecting unit can input the user's emotion data into the generation AI, which can then analyze the data and determine the priority of the data to be reflected.

[0105] When reflecting real-time data, the real-time data reflecting unit can prioritize reflecting highly relevant data by taking into account the user's geographical location information. For example, the real-time data reflecting unit can prioritize providing real-time data of tourist spots close to the user's current location. The real-time data reflecting unit can also prioritize providing real-time data of tourist spots close to places the user has previously visited. The real-time data reflecting unit can also prioritize providing real-time data of tourist spots that are easily accessible by taking into account the user's mode of transportation from their current location. This allows the user to be provided with optimal real-time data by preferentially reflecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's geographical location information into a generation AI, which can analyze the data and reflect highly relevant data.

[0106] The real-time data reflecting unit can analyze the user's social media activity and reflect related data when reflecting real-time data. The real-time data reflecting unit, for example, provides real-time data on locations where the user has checked in on social media. The real-time data reflecting unit can also analyze the content of the user's social media posts and provide real-time data on tourist spots that the user may be interested in. The real-time data reflecting unit can also provide real-time data on related tourist spots based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide optimal real-time data for the user. Some or all of the above-described processing in the real-time data reflecting unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflecting unit can input the user's social media activity data into a generation AI, which can analyze the data and reflect related data.

[0107] The real-time data reflection unit can customize the data reflection method by reflecting the user's past feedback when reflecting real-time data. For example, the real-time data reflection unit prioritizes the use of a data reflection method (voice, text, etc.) that the user previously preferred. The real-time data reflection unit can also avoid a data reflection method that the user previously dissatisfied with and use a different method. The real-time data reflection unit can also customize the optimal data reflection method based on the user's past feedback. In this way, by customizing the data reflection method by reflecting the user's past feedback, it is possible to provide optimal real-time data for the user. Some or all of the above-mentioned processing in the real-time data reflection unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the real-time data reflection unit can input the user's past feedback data into the generation AI, which can then analyze the data and customize the data reflection method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and real-time data reflection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input of a desired destination from a user using the reception device 38 of the smart device 14. The generation unit can generate a travel plan using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated travel plan to the user using the output device 40 of the smart device 14. The real-time data reflection unit can reflect real-time data by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, and real-time data reflection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input of a desired destination from the user using the microphone 238 of the smart glasses 214. The generation unit can generate a travel plan using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated travel plan to the user using the speaker 240 of the smart glasses 214. The real-time data reflection unit can reflect real-time data by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and real-time data reflection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive input of a desired destination from the user using the microphone 238 of the headset type terminal 314. The generation unit can generate a travel plan using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated travel plan to the user using the speaker 240 of the headset type terminal 314. The real-time data reflection unit can reflect real-time data by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and real-time data reflection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of a desired destination from the user using the microphone 238 of the robot 414. The generation unit can generate a travel plan using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated travel plan to the user using the speaker 240 of the robot 414. The real-time data reflection unit can reflect real-time data by the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can adjust the travel plan taking into account the user's current health condition. For example, if the user is tired, the reception unit can suggest a plan that includes many rest periods. Also, if the user is active, the reception unit can suggest a plan that includes many activities. Furthermore, if the user has specific health constraints, the reception unit can provide a plan that meets those constraints. This makes it possible to provide the optimal travel plan that suits the user's health condition.

[0110] The generation unit can collect the user's ratings of past travel plans and reflect them in the next plan generation. For example, tourist spots that the user has previously rated highly can be included preferentially. Tourist spots that the user has previously rated poorly can also be excluded. Furthermore, similar tourist spots can be suggested based on the user's ratings. This makes it possible to provide an optimal travel plan that reflects the user's past ratings.

[0111] The provision unit can collect data on the user's behavior during the trip and adjust the plan in real time. For example, if the user visits a tourist spot earlier than planned, the next tourist spot can be suggested early. Also, if the user is running behind schedule, the plan can be adjusted to make the schedule more reasonable. Furthermore, if the user stays at a particular location for a long time, additional information related to that location can be provided. This makes it possible to provide a flexible travel plan that matches the user's behavior.

[0112] The real-time data reflection unit can estimate the user's emotions and provide real-time tourist destination information based on the estimated emotions. For example, if the user is excited, popular tourist destination and event information can be provided preferentially. Also, if the user is relaxed, quiet places and relaxing activities can be suggested. Furthermore, if the user is stressed, information useful for relieving stress can be provided. This makes it possible to provide optimal real-time information according to the user's emotions.

[0113] The generation unit can analyze photos and videos taken during the user's trip and reflect them in the next travel plan. For example, tourist spots where the user has taken many photos can be prioritized for inclusion in the next plan. It can also suggest activities for which the user has taken videos in the next plan. Furthermore, if the user has taken photos based on a specific theme, tourist spots related to that theme can be suggested. This makes it possible to provide the user with an optimal travel plan based on their interests.

[0114] The providing unit can estimate the user's emotions and adjust the timing of providing the travel plan based on the estimated emotions. For example, if the user is relaxed, a detailed plan can be provided slowly. If the user is in a hurry, a concise plan can be provided quickly. Furthermore, if the user is excited, a visually appealing plan can be provided. This makes it possible to provide the travel plan at the optimal timing according to the user's emotions.

[0115] The reception unit can suggest themes for travel plans based on the user's current mood. For example, if the user is in the mood to relax, a plan including hot springs and spas can be suggested. If the user is seeking adventure, a plan including activities and outdoor activities can be suggested. Furthermore, if the user is seeking a cultural experience, a plan including museums and historical sites can be suggested. This makes it possible to provide the user with the optimal travel plan according to their mood.

[0116] The generation unit can analyze the success rate of the user's past travel plans and reflect this in the generation of the next plan. For example, elements of plans that the user has previously succeeded in can be incorporated into the next plan. Elements of plans that the user has previously failed in can also be avoided. Furthermore, similar plans can be suggested based on the user's success rate. This makes it possible to provide an optimal travel plan that reflects the user's past successful experiences.

[0117] The providing unit can estimate the user's emotions and collect feedback on the travel plan based on the estimated emotions. For example, if the user is satisfied, detailed feedback can be requested. If the user is dissatisfied, brief feedback can be requested. Furthermore, if the user is excited, a visually attractive feedback form can be provided. This makes it possible to collect feedback in an optimal manner according to the user's emotions.

[0118] The real-time data reflecting unit can provide real-time data taking into account the user's current activity level. For example, if the user is actively moving, the unit can prioritize suggesting the next activity. Also, if the user is taking a break, the unit can suggest places and activities where the user can relax. Furthermore, if the user stays in a particular place for a long time, the unit can provide additional information related to that place. This makes it possible to provide optimal real-time data according to the user's activity level.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit accepts input from the user about the place they want to go. The user only needs to input the specific tourist spot or city name. For example, the reception unit accepts information input by the user such as "I want to go to Kyoto." Step 2: The generation unit uses the generation AI to generate a travel plan based on the information received by the reception unit. The generation AI references a database of tourist attractions and determines the order in which to visit the tourist attractions based on the user's preferences. It also suggests transportation methods, accommodations, and places to eat. For example, it suggests transportation methods that take train and bus timetables into account, accommodations that fit the user's budget, and places to eat where you can enjoy local specialties. Step 3: The provider provides the travel plan generated by the generator to the user. The provider customizes the travel plan according to the user's wishes and restrictions. For example, if the user wants to visit a specific tourist spot or travel during a specific time period, the plan is adjusted according to the user's requests. Step 4: The real-time data reflecting unit reflects real-time data based on the travel plan provided by the providing unit. The real-time data reflecting unit dynamically adjusts the plan, taking into account real-time data such as weather and traffic conditions. This makes it possible to prevent trouble during the trip.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] 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.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a desired destination from a user; a generation unit that generates a travel plan based on the information received by the reception unit; a providing unit that provides the travel plan generated by the generating unit to a user; a real-time data reflecting unit that reflects real-time data based on the travel plan provided by the providing unit; A system characterized by:

2. The reception unit Accept user details The system of claim 1 .

3. The generation unit Collect and analyze user profile data The system of claim 1 .

4. The generation unit Refer to the database of tourist attractions and determine the order in which to visit the tourist attractions based on the user's wishes The system of claim 1 .

5. The generation unit Suggest transportation, accommodation, and dining options The system of claim 1 .

6. The providing unit Customize your travel plans based on your preferences and restrictions The system of claim 1 .

7. The real-time data reflection unit Dynamically adjust your plan to take into account real-time data like weather and traffic conditions The system of claim 1 .

8. The reception unit Estimate the user's emotions and adjust the way to input the destination based on the estimated user emotions. The system of claim 1 .

Images