system

Abstract

The system according to the embodiment aims to enable a user to plan a trip in a seamless manner, making it easier to grasp the overall image. [Solution] A system according to an embodiment includes a selection unit, a generation unit, a calculation unit, and a reservation unit. The selection unit selects elements such as destination, time, meals, transportation, and weather. The generation unit generates a travel plan based on the elements selected by the selection unit. The calculation unit calculates a route and schedule based on the plan generated by the generation unit. The reservation unit makes a reservation based on the information calculated by the calculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making travel planning fragmented and making it difficult to grasp the overall picture.

[0005] The system according to the embodiment aims to enable a user to plan a trip in a seamless manner, making it easier to grasp the overall image. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a generation unit, a calculation unit, and a reservation unit. The selection unit selects elements such as destination, time, meals, transportation, and weather. The generation unit generates a travel plan based on the elements selected by the selection unit. The calculation unit calculates a route and schedule based on the plan generated by the generation unit. The reservation unit makes a reservation based on the information calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for trip planning to be carried out in a seamless manner, making it easier to grasp the overall image. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning system according to an embodiment of the present invention helps travelers avoid potential problems and maximize their enjoyment of their trips. This system allows users to select factors such as destination, time, meals, transportation, and weather, and a generation AI analyzes these factors to create the most efficient travel plan. For example, a user can select a destination and time, but not meals or transportation, allowing them to enjoy free movement while on the go. The generation AI calculates optimal routes and schedules based on the selected factors and generates a comprehensive plan that provides an overall image. For example, when a destination is selected, the system suggests hotels, meals, and transportation that are optimal for that destination. The user can then make reservations based on the generated plan. For example, the generation AI can make reservations for hotels, restaurants, and transportation suggested by the system all at once. This allows users to prepare for their trip hassle-free. Furthermore, the generation AI can propose plans that adapt to weather fluctuations, thereby avoiding problems caused by bad weather. Furthermore, the system can suggest optimal routes that avoid transportation delays and congestion, ensuring a smooth trip. In this way, a travel planning system utilizing generation AI provides travelers with a convenient and efficient travel experience. This allows the travel planning system to prevent trouble before it happens and allow travelers to enjoy their trip to the fullest.

[0029] A travel planning system according to an embodiment includes a selection unit, a generation unit, a calculation unit, and a reservation unit. The selection unit selects elements such as destination, time, meals, transportation, and weather. For example, a user can select a destination and time, but not meals or transportation, allowing them to enjoy free movement in the destination. The generation unit generates a travel plan based on the elements selected by the selection unit. The generation AI calculates optimal routes and schedules based on the selected elements to generate a comprehensive plan that provides an overall image. For example, when a destination is selected, the AI ​​proposes hotels, meals, and transportation that are optimal for that destination. The calculation unit calculates routes and schedules based on the plan generated by the generation unit. For example, the AI ​​calculates optimal transportation methods and time allocations based on the route and schedule proposed by the AI. The reservation unit makes reservations based on the information calculated by the calculation unit. For example, the AI ​​can make reservations for hotels, restaurants, and transportation proposed by the AI ​​all at once. This allows users to prepare for their trip hassle-free. Furthermore, the AI ​​proposes plans that adapt to weather fluctuations, thereby avoiding problems caused by bad weather. Furthermore, by proposing the optimal route to avoid delays and congestion in public transport, a smooth trip can be realized. As a result, the trip planning system according to the embodiment can prevent trouble before it happens and enable travelers to enjoy their trip to the fullest.

[0030] The travel planning system includes an acquisition unit that acquires weather information. The acquisition unit acquires the weather information. For example, weather data and forecast information can be acquired and reflected in the travel plan. By acquiring the weather information, weather fluctuations can be taken into account in the travel plan. For example, the generation AI can propose a plan that takes into account weather fluctuations, thereby avoiding problems caused by bad weather. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input weather data into the generation AI and have the generation AI analyze the weather information.

[0031] The travel planning system includes a collection unit that collects information about transportation facilities. The collection unit collects the information about transportation facilities. For example, it can collect operation schedules and fare information and reflect this information in the travel plan. By collecting the information about transportation facilities, it is possible to propose the optimal means of travel. For example, it is possible to realize a smooth trip by proposing the optimal route to avoid delays and congestion on transportation facilities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the information about transportation facilities into a generation AI and have the generation AI analyze the information.

[0032] The generation unit can suggest hotels, meals, and transportation options based on the selected elements. For example, the generation unit suggests optimal hotels, meals, and transportation options based on the selected elements. For example, when a destination is selected, the generation unit suggests optimal hotels, meals, and transportation options for that destination. This improves the accuracy of travel plans by making optimal suggestions based on the selected elements. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the selected elements into the generation AI and have the generation AI execute the optimal suggestions.

[0033] The reservation unit can make reservations for hotels, restaurants, and transportation proposed by the generation AI. The reservation unit can, for example, make reservations for hotels, restaurants, and transportation proposed by the generation AI all at once. This can reduce the user's effort by making reservations all at once. For example, by making reservations for hotels, restaurants, and transportation proposed by the generation AI all at once, the user can prepare for a trip without hassle. Some or all of the above-mentioned processing in the reservation unit can be performed using AI, for example, or can be performed without using AI. For example, the reservation unit can input reservation information proposed by the generation AI into the generation AI and have the generation AI execute the reservation.

[0034] The calculation unit can generate a plan that corresponds to weather fluctuations. The calculation unit, for example, generates a plan that corresponds to weather fluctuations. For example, by having the generation AI propose a plan that corresponds to weather fluctuations, troubles caused by bad weather can be avoided. By responding to weather fluctuations in this way, troubles during travel can be prevented before they occur. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input weather information into the generation AI and have the generation AI generate a plan that corresponds to weather fluctuations.

[0035] When making a selection, the selection unit can analyze the user's past travel history and suggest optimal options. For example, the selection unit can suggest similar destinations based on places the user has visited in the past. The selection unit can also suggest related activities based on activities the user has previously preferred. The selection unit can also suggest optimal means of transportation based on transportation modes the user has previously used. In this way, by analyzing the user's past travel history, optimal options can be provided to the user. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past travel history data into a generation AI and have the generation AI suggest optimal options.

[0036] When making a selection, the selection unit can filter options based on the user's current health condition or physical condition. For example, if the user is not in good health, the selection unit can suggest destinations or activities that are less strenuous. Furthermore, if the user is in good health, the selection unit can also suggest active activities or tourist spots. Furthermore, if the user has specific health conditions, the selection unit can suggest options that are suitable for those conditions. This allows for more appropriate travel plans to be proposed by providing options based on the user's health condition or physical condition. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input the user's health condition data into the generation AI and have the generation AI filter the options.

[0037] When making a selection, the selection unit can prioritize presenting highly relevant options by taking into account the user's geographical location information. For example, the selection unit can prioritize presenting destinations close to the user's current location. The selection unit can also prioritize presenting transportation methods that are easily accessible from the user's current location. The selection unit can also suggest optimal destinations and activities based on weather information for the user's current location. This makes it possible to provide more relevant options by taking the user's geographical location information into consideration. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to the generation AI and have the generation AI present highly relevant options.

[0038] At the time of selection, the selection unit can analyze the user's social media activity and present relevant options. For example, the selection unit can suggest related destinations based on travel destinations shared by the user on social media. The selection unit can also suggest related activities based on activities the user has "liked" on social media. The selection unit can also suggest related options based on travel-related accounts the user follows on social media. In this way, by analyzing social media activity, relevant options can be provided to the user. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media data into a generation AI and have the generation AI present relevant options.

[0039] The generation unit can adjust the level of detail of the plan based on the importance of the selected elements during generation. For example, the generation unit generates a plan that includes detailed descriptions for elements with high importance. The generation unit can also generate a plan that includes concise descriptions for elements with low importance. The generation unit can also prioritize displaying elements with high importance and postpone displaying elements with low importance. In this way, by adjusting the level of detail of the plan based on the importance of the selected elements, a more appropriate plan can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the selected elements into the generation AI and have the generation AI adjust the level of detail of the plan.

[0040] The generation unit can apply different generation algorithms depending on the category of the selected element during generation. For example, the generation unit can apply a generation algorithm that emphasizes tourist destination information based on the selection of a destination. The generation unit can also apply a generation algorithm that emphasizes restaurant information based on the selection of a meal. The generation unit can also apply a generation algorithm that emphasizes transportation means information based on the selection of a transportation mode. In this way, by applying a generation algorithm according to the category of the selected element, a more appropriate plan can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input category data of the selected element into the generation AI and cause the generation AI to apply different generation algorithms.

[0041] The generation unit can determine the priority of the plan based on the submission times of the selected elements at the time of generation. For example, the generation unit can prioritize elements selected early in the plan. The generation unit can also prioritize elements with high urgency in the plan. The generation unit can also incorporate elements with later submission times into the plan at a later date. In this way, by determining the priority of the plan based on the submission times of the selected elements, a more appropriate plan can be provided. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input submission time data of the selected elements into the generation AI and have the generation AI determine the priority of the plan.

[0042] The generation unit can adjust the order of the plan based on the relevance of the selected elements during generation. For example, the generation unit incorporates highly relevant elements into the plan consecutively. The generation unit can also incorporate less relevant elements into the plan later. The generation unit can also display highly relevant elements preferentially and defer less relevant elements. In this way, by adjusting the order of the plan based on the relevance of the selected elements, a more appropriate plan can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the selected elements into the generation AI and have the generation AI adjust the order of the plan.

[0043] The calculation unit can improve the accuracy of the calculation by taking into account the interrelationships of the selected elements during calculation. The calculation unit, for example, calculates an optimal route based on the interrelationships of the selected elements. The calculation unit can also calculate an optimal schedule based on the interrelationships of the selected elements. The calculation unit can also calculate an optimal plan based on the interrelationships of the selected elements. In this way, the accuracy of the calculation is improved by taking into account the interrelationships of the selected elements. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input interrelationship data of the selected elements into a generation AI and have the generation AI improve the accuracy of the calculation.

[0044] The calculation unit can perform calculations taking into account the attribute information of the submitter of the selected elements. The calculation unit can calculate the optimal route, for example, taking into account the submitter's age. The calculation unit can also calculate the optimal schedule taking into account the submitter's gender. The calculation unit can also calculate the optimal plan taking into account the submitter's health condition. In this way, by taking into account the submitter's attribute information, more appropriate calculation results can be provided. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input the submitter's attribute information data into a generation AI and have the generation AI perform the calculations.

[0045] The calculation unit can perform calculations taking into account the geographic distribution of the selected elements. For example, the calculation unit can calculate an optimal route based on the geographic distribution of the selected elements. The calculation unit can also calculate an optimal schedule based on the geographic distribution of the selected elements. The calculation unit can also calculate an optimal plan based on the geographic distribution of the selected elements. In this way, by taking the geographic distribution into consideration, more appropriate calculation results can be provided. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input geographic distribution data of the selected elements to a generation AI and have the generation AI perform the calculations.

[0046] The calculation unit can improve the accuracy of the calculation by referring to literature related to the selected element during calculation. The calculation unit, for example, calculates an optimal route based on literature related to the selected element. The calculation unit can also calculate an optimal schedule based on literature related to the selected element. The calculation unit can also calculate an optimal plan based on literature related to the selected element. In this way, the accuracy of the calculation is improved by referring to the literature. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input literature data related to the selected element into the generation AI and have the generation AI improve the accuracy of the calculation.

[0047] The reservation unit can improve the accuracy of reservations by taking into account the interrelationships of the selected elements when making a reservation. The reservation unit, for example, makes a reservation for an optimal facility based on the interrelationships of the selected elements. The reservation unit can also make a reservation for an optimal activity based on the interrelationships of the selected elements. The reservation unit can also make a reservation for an optimal means of transportation based on the interrelationships of the selected elements. In this way, the accuracy of reservations is improved by taking into account the interrelationships of the selected elements. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input interrelationship data of the selected elements into a generation AI and have the generation AI improve the accuracy of reservations.

[0048] When making a reservation, the reservation unit can make a reservation taking into account the attribute information of the submitter of the selected elements. The reservation unit can, for example, reserve the most suitable facility taking into account the submitter's age. The reservation unit can also reserve the most suitable activity taking into account the submitter's gender. The reservation unit can also reserve the most suitable means of transportation taking into account the submitter's health condition. In this way, by taking into account the submitter's attribute information, a more appropriate reservation can be provided. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the submitter's attribute information data into a generation AI and have the generation AI make the reservation.

[0049] The reservation unit can make a reservation taking into account the geographical distribution of the selected elements when making a reservation. For example, the reservation unit makes a reservation for the most appropriate facility based on the geographical distribution of the selected elements. The reservation unit can also make a reservation for the most appropriate activity based on the geographical distribution of the selected elements. The reservation unit can also make a reservation for the most appropriate means of transportation based on the geographical distribution of the selected elements. This makes it possible to provide a more appropriate reservation by taking the geographical distribution into consideration. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input geographical distribution data of the selected elements into a generation AI and have the generation AI make a reservation.

[0050] The reservation unit can improve the accuracy of reservations by referring to literature related to the selected elements when making a reservation. The reservation unit, for example, reserves the most appropriate facility based on literature related to the selected elements. The reservation unit can also reserve the most appropriate activity based on literature related to the selected elements. The reservation unit can also reserve the most appropriate means of transportation based on literature related to the selected elements. In this way, the accuracy of reservations is improved by referring to the related literature. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input literature data related to the selected elements into a generation AI and have the generation AI improve the accuracy of reservations.

[0051] When acquiring weather information, the acquisition unit can analyze the user's past travel history and select the optimal acquisition method. The acquisition unit selects the optimal acquisition method, for example, based on weather information for places the user has visited in the past. The acquisition unit can also adjust the frequency of acquiring weather information based on the user's past travel history. The acquisition unit can also analyze the user's past travel history and select the most efficient method of acquiring weather information. In this way, more appropriate weather information can be provided by analyzing the past travel history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past travel history data into a generation AI and have the generation AI select the optimal acquisition method.

[0052] When acquiring weather information, the acquisition unit can perform filtering based on the user's current travel plan. For example, the acquisition unit can prioritize acquisition of relevant weather information based on the user's current travel plan. The acquisition unit can also filter unnecessary weather information based on the user's current travel plan. The acquisition unit can also acquire optimal weather information based on the user's current travel plan. This makes it possible to provide more appropriate weather information by filtering based on the current travel plan. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's current travel plan data to the generation AI and have the generation AI filter the weather information.

[0053] When acquiring weather information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring relevant weather information based on the user's current location. The acquisition unit can also prioritize acquiring relevant weather information based on the user's destination. The acquisition unit can also prioritize acquiring relevant weather information based on the user's travel route. This makes it possible to provide more appropriate weather information by taking the geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant weather information.

[0054] When acquiring weather information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit can prioritize acquiring weather information for travel destinations shared by the user on social media. The acquisition unit can also prioritize acquiring weather information for places that the user has "liked" on social media. The acquisition unit can also prioritize acquiring weather information from travel-related accounts that the user follows on social media. This allows for more appropriate weather information to be provided by analyzing social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, or without, AI, for example. For example, the acquisition unit can input the user's social media data into the generation AI and have the generation AI acquire related weather information.

[0055] When collecting transportation information, the collection unit can analyze the user's past travel history and select the optimal collection method. The collection unit selects the optimal collection method, for example, based on information about transportation modes used by the user in the past. The collection unit can also adjust the frequency of collecting transportation information based on the user's past travel history. The collection unit can also analyze the user's past travel history and select the most efficient method of collecting transportation information. In this way, more appropriate transportation information can be provided by analyzing the past travel history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past travel history data into the generation AI and have the generation AI select the optimal collection method.

[0056] When collecting transportation information, the collection unit can filter the transportation information based on the user's current travel plan. For example, the collection unit prioritizes collecting relevant transportation information based on the user's current travel plan. The collection unit can also filter unnecessary transportation information based on the user's current travel plan. The collection unit can also collect optimal transportation information based on the user's current travel plan. This makes it possible to provide more appropriate transportation information by collecting filtering based on the current travel plan. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's current travel plan data to the generation AI and have the generation AI filter the transportation information.

[0057] When collecting transportation information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting relevant transportation information based on the user's current location. The collection unit can also prioritize collecting relevant transportation information based on the user's destination. The collection unit can also prioritize collecting relevant transportation information based on the user's travel route. This makes it possible to provide more appropriate transportation information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant transportation information.

[0058] When collecting transportation information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can prioritize collecting transportation information for travel destinations shared by the user on social media. The collection unit can also prioritize collecting transportation information for places that the user has "liked" on social media. The collection unit can also prioritize collecting transportation information from travel-related accounts that the user follows on social media. This allows for more appropriate transportation information to be provided by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect related transportation information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The travel planning system can be equipped with a health monitoring unit that monitors the user's health condition during the trip. The health monitoring unit acquires vital data such as the user's heart rate, blood pressure, and body temperature in real time, and if an abnormality is detected, it can suggest appropriate measures. For example, if the user's heart rate suddenly rises, it can suggest taking a break. Also, if the user's blood pressure is high, it can suggest a relaxing activity. Furthermore, if the user's body temperature is high, it can suggest moving to a cooler place. This reduces health risks during travel and allows the user to enjoy their trip with peace of mind.

[0061] The travel planning system may include an expense management unit that manages expenses incurred by a user during a trip. The expense management unit records expenses incurred by a user during a trip in real time, supporting the user in staying within a budget. For example, if a user pays an entrance fee at a specific tourist attraction, the expense is recorded. If a user pays for a meal at a specific restaurant, the expense can also be recorded. Furthermore, if a user pays for a fare for a specific means of transportation, the expense can also be recorded. This allows the user to manage expenses during a trip and prevent the user from going over budget.

[0062] The travel planning system may include a photo organizing unit that organizes photos taken by a user during their trip. The photo organizing unit automatically organizes photos taken by the user during their trip and creates albums. For example, the photo organizing unit may organize photos taken by the user at specific tourist spots and create an album for each tourist spot. The system may also organize photos taken by the user during specific activities and create an album for each activity. Furthermore, the system may organize photos taken at specific restaurants and create an album for each restaurant. This allows users to organize their travel memories and easily look back on them later.

[0063] The travel planning system may include a communication support unit that supports communication during the user's trip. The communication support unit supports the user in smoothly communicating with local people. For example, if the user does not understand the local language, it may provide a translation function. It may also provide information to help the user understand the local culture and customs. It may also provide event information for the user to interact with local people. This allows the user to smoothly communicate with local people and enjoy their trip more.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The selection unit selects the elements of destination, time, meal, transportation, and weather. For example, the user can select the destination and time, but not the meal or transportation, allowing them to enjoy free activities at the destination. Step 2: The generator generates a travel plan based on the elements selected by the selector. The generator calculates the optimal route and schedule based on the selected elements, and generates a comprehensive plan that gives an overall image. For example, if a destination is selected, it will suggest the best hotels, meals, and transportation options for that destination. Step 3: The calculation unit calculates routes and schedules based on the plans generated by the generation unit. For example, it calculates the optimal means of transportation and time allocation based on the routes and schedules proposed by the generation AI. Step 4: The reservation unit makes reservations based on the information calculated by the calculation unit. For example, reservations can be made for hotels, restaurants, and transportation suggested by the generation AI all at once.

[0066] (Example 2) A travel planning system according to an embodiment of the present invention helps travelers avoid potential problems and maximize their enjoyment of their trips. This system allows users to select factors such as destination, time, meals, transportation, and weather, and a generation AI analyzes these factors to create the most efficient travel plan. For example, a user can select a destination and time, but not meals or transportation, allowing them to enjoy free movement while on the go. The generation AI calculates optimal routes and schedules based on the selected factors and generates a comprehensive plan that provides an overall image. For example, when a destination is selected, the system suggests hotels, meals, and transportation that are optimal for that destination. The user can then make reservations based on the generated plan. For example, the generation AI can make reservations for hotels, restaurants, and transportation suggested by the system all at once. This allows users to prepare for their trip hassle-free. Furthermore, the generation AI can propose plans that adapt to weather fluctuations, thereby avoiding problems caused by bad weather. Furthermore, the system can suggest optimal routes that avoid transportation delays and congestion, ensuring a smooth trip. In this way, a travel planning system utilizing generation AI provides travelers with a convenient and efficient travel experience. This allows the travel planning system to prevent trouble before it happens and allow travelers to enjoy their trip to the fullest.

[0067] A travel planning system according to an embodiment includes a selection unit, a generation unit, a calculation unit, and a reservation unit. The selection unit selects elements such as destination, time, meals, transportation, and weather. For example, a user can select a destination and time, but not meals or transportation, allowing them to enjoy free movement in the destination. The generation unit generates a travel plan based on the elements selected by the selection unit. The generation AI calculates optimal routes and schedules based on the selected elements to generate a comprehensive plan that provides an overall image. For example, when a destination is selected, the AI ​​proposes hotels, meals, and transportation that are optimal for that destination. The calculation unit calculates routes and schedules based on the plan generated by the generation unit. For example, the AI ​​calculates optimal transportation methods and time allocations based on the route and schedule proposed by the AI. The reservation unit makes reservations based on the information calculated by the calculation unit. For example, the AI ​​can make reservations for hotels, restaurants, and transportation proposed by the AI ​​all at once. This allows users to prepare for their trip hassle-free. Furthermore, the AI ​​proposes plans that adapt to weather fluctuations, thereby avoiding problems caused by bad weather. Furthermore, by proposing the optimal route to avoid delays and congestion in public transport, a smooth trip can be realized. As a result, the trip planning system according to the embodiment can prevent trouble before it happens and enable travelers to enjoy their trip to the fullest.

[0068] The travel planning system includes an acquisition unit that acquires weather information. The acquisition unit acquires the weather information. For example, weather data and forecast information can be acquired and reflected in the travel plan. By acquiring the weather information, weather fluctuations can be taken into account in the travel plan. For example, the generation AI can propose a plan that takes into account weather fluctuations, thereby avoiding problems caused by bad weather. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input weather data into the generation AI and have the generation AI analyze the weather information.

[0069] The travel planning system includes a collection unit that collects information about transportation facilities. The collection unit collects the information about transportation facilities. For example, it can collect operation schedules and fare information and reflect this information in the travel plan. By collecting the information about transportation facilities, it is possible to propose the optimal means of travel. For example, it is possible to realize a smooth trip by proposing the optimal route to avoid delays and congestion on transportation facilities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the information about transportation facilities into a generation AI and have the generation AI analyze the information.

[0070] The generation unit can suggest hotels, meals, and transportation options based on the selected elements. For example, the generation unit suggests optimal hotels, meals, and transportation options based on the selected elements. For example, when a destination is selected, the generation unit suggests optimal hotels, meals, and transportation options for that destination. This improves the accuracy of travel plans by making optimal suggestions based on the selected elements. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the selected elements into the generation AI and have the generation AI execute the optimal suggestions.

[0071] The reservation unit can make reservations for hotels, restaurants, and transportation proposed by the generation AI. The reservation unit can, for example, make reservations for hotels, restaurants, and transportation proposed by the generation AI all at once. This can reduce the user's effort by making reservations all at once. For example, by making reservations for hotels, restaurants, and transportation proposed by the generation AI all at once, the user can prepare for a trip without hassle. Some or all of the above-mentioned processing in the reservation unit can be performed using AI, for example, or can be performed without using AI. For example, the reservation unit can input reservation information proposed by the generation AI into the generation AI and have the generation AI execute the reservation.

[0072] The calculation unit can generate a plan that corresponds to weather fluctuations. The calculation unit, for example, generates a plan that corresponds to weather fluctuations. For example, by having the generation AI propose a plan that corresponds to weather fluctuations, troubles caused by bad weather can be avoided. By responding to weather fluctuations in this way, troubles during travel can be prevented before they occur. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input weather information into the generation AI and have the generation AI generate a plan that corresponds to weather fluctuations.

[0073] The selection unit can estimate the user's emotions and adjust the presentation order of options based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize presenting relaxing destinations and activities. Furthermore, if the user is excited, the selection unit can prioritize presenting active activities and tourist spots. Furthermore, if the user is tired, the selection unit can prioritize presenting relaxing hotels and spas. This allows for adjusting the presentation order of options according to the user's emotions, thereby providing more appropriate options. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0074] When making a selection, the selection unit can analyze the user's past travel history and suggest optimal options. For example, the selection unit can suggest similar destinations based on places the user has visited in the past. The selection unit can also suggest related activities based on activities the user has previously preferred. The selection unit can also suggest optimal means of transportation based on transportation modes the user has previously used. In this way, by analyzing the user's past travel history, optimal options can be provided to the user. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past travel history data into a generation AI and have the generation AI suggest optimal options.

[0075] When making a selection, the selection unit can filter options based on the user's current health condition or physical condition. For example, if the user is not in good health, the selection unit can suggest destinations or activities that are less strenuous. Furthermore, if the user is in good health, the selection unit can also suggest active activities or tourist spots. Furthermore, if the user has specific health conditions, the selection unit can suggest options that are suitable for those conditions. This allows for more appropriate travel plans to be proposed by providing options based on the user's health condition or physical condition. Some or all of the above-described processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input the user's health condition data into the generation AI and have the generation AI filter the options.

[0076] The selection unit can estimate the user's emotions and adjust the number of options based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can reduce the number of options to simplify the options. Furthermore, if the user is relaxed, the selection unit can increase the number of options to provide a variety of options. Furthermore, if the user is in a hurry, the selection unit can present only the most important options. This allows for adjusting the number of options according to the user's emotions, thereby providing more appropriate options. 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 selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0077] When making a selection, the selection unit can prioritize presenting highly relevant options by taking into account the user's geographical location information. For example, the selection unit can prioritize presenting destinations close to the user's current location. The selection unit can also prioritize presenting transportation methods that are easily accessible from the user's current location. The selection unit can also suggest optimal destinations and activities based on weather information for the user's current location. This makes it possible to provide more relevant options by taking the user's geographical location information into consideration. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's geographical location information to the generation AI and have the generation AI present highly relevant options.

[0078] At the time of selection, the selection unit can analyze the user's social media activity and present relevant options. For example, the selection unit can suggest related destinations based on travel destinations shared by the user on social media. The selection unit can also suggest related activities based on activities the user has "liked" on social media. The selection unit can also suggest related options based on travel-related accounts the user follows on social media. In this way, by analyzing social media activity, relevant options can be provided to the user. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media data into a generation AI and have the generation AI present relevant options.

[0079] The generation unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a plan that emphasizes the shortest route. If the user is excited, the generation unit can also generate a plan that adds visually stimulating effects. This allows for adjusting the way the plan is presented based on the user's emotions, thereby providing a more appropriate plan. 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, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the plan is presented.

[0080] The generation unit can adjust the level of detail of the plan based on the importance of the selected elements during generation. For example, the generation unit generates a plan that includes detailed descriptions for elements with high importance. The generation unit can also generate a plan that includes concise descriptions for elements with low importance. The generation unit can also prioritize displaying elements with high importance and postpone displaying elements with low importance. In this way, by adjusting the level of detail of the plan based on the importance of the selected elements, a more appropriate plan can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the selected elements into the generation AI and have the generation AI adjust the level of detail of the plan.

[0081] The generation unit can apply different generation algorithms depending on the category of the selected element during generation. For example, the generation unit can apply a generation algorithm that emphasizes tourist destination information based on the selection of a destination. The generation unit can also apply a generation algorithm that emphasizes restaurant information based on the selection of a meal. The generation unit can also apply a generation algorithm that emphasizes transportation means information based on the selection of a transportation mode. In this way, by applying a generation algorithm according to the category of the selected element, a more appropriate plan can be provided. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input category data of the selected element into the generation AI and cause the generation AI to apply different generation algorithms.

[0082] The generation unit can estimate the user's emotions and adjust the length of the plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise plan. If the user is relaxed, the generation unit can generate a longer plan with detailed explanations. If the user is excited, the generation unit can generate a plan with visually stimulating effects. This allows for adjusting the length of the plan according to the user's emotions, thereby providing a more appropriate plan. The 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 generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the plan.

[0083] The generation unit can determine the priority of the plan based on the submission times of the selected elements at the time of generation. For example, the generation unit can prioritize elements selected early in the plan. The generation unit can also prioritize elements with high urgency in the plan. The generation unit can also incorporate elements with later submission times into the plan at a later date. In this way, by determining the priority of the plan based on the submission times of the selected elements, a more appropriate plan can be provided. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input submission time data of the selected elements into the generation AI and have the generation AI determine the priority of the plan.

[0084] The generation unit can adjust the order of the plan based on the relevance of the selected elements during generation. For example, the generation unit incorporates highly relevant elements into the plan consecutively. The generation unit can also incorporate less relevant elements into the plan later. The generation unit can also display highly relevant elements preferentially and defer less relevant elements. In this way, by adjusting the order of the plan based on the relevance of the selected elements, a more appropriate plan can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the selected elements into the generation AI and have the generation AI adjust the order of the plan.

[0085] The calculation unit can estimate the user's emotions and adjust the calculation criteria based on the estimated user's emotions. For example, if the user is relaxed, the calculation unit can apply a calculation criterion that progresses at a leisurely pace. If the user is in a hurry, the calculation unit can also apply a calculation criterion that emphasizes the shortest route. If the user is excited, the calculation unit can also apply a calculation criterion that adds a visually stimulating effect. By adjusting the calculation criteria according to the user's emotions, more appropriate calculation results can be provided. 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 calculation unit can be performed using, for example, an AI, or without an AI. For example, the calculation unit can input the user's emotion data into the generation AI and have the generation AI adjust the calculation criteria.

[0086] The calculation unit can improve the accuracy of the calculation by taking into account the interrelationships of the selected elements during calculation. The calculation unit, for example, calculates an optimal route based on the interrelationships of the selected elements. The calculation unit can also calculate an optimal schedule based on the interrelationships of the selected elements. The calculation unit can also calculate an optimal plan based on the interrelationships of the selected elements. In this way, the accuracy of the calculation is improved by taking into account the interrelationships of the selected elements. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input interrelationship data of the selected elements into a generation AI and have the generation AI improve the accuracy of the calculation.

[0087] The calculation unit can perform calculations taking into account the attribute information of the submitter of the selected elements. The calculation unit can calculate the optimal route, for example, taking into account the submitter's age. The calculation unit can also calculate the optimal schedule taking into account the submitter's gender. The calculation unit can also calculate the optimal plan taking into account the submitter's health condition. In this way, by taking into account the submitter's attribute information, more appropriate calculation results can be provided. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input the submitter's attribute information data into a generation AI and have the generation AI perform the calculations.

[0088] The calculation unit can estimate the user's emotions and adjust the display order of the calculation results based on the estimated user's emotions. For example, when the user is relaxed, the calculation unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the calculation unit can prioritize displaying information that focuses on the main points. Furthermore, when the user is excited, the calculation unit can prioritize displaying visually stimulating information. This allows for adjusting the display order according to the user's emotions to provide more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order of the calculation results.

[0089] The calculation unit can perform calculations taking into account the geographic distribution of the selected elements. For example, the calculation unit can calculate an optimal route based on the geographic distribution of the selected elements. The calculation unit can also calculate an optimal schedule based on the geographic distribution of the selected elements. The calculation unit can also calculate an optimal plan based on the geographic distribution of the selected elements. In this way, by taking the geographic distribution into consideration, more appropriate calculation results can be provided. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input geographic distribution data of the selected elements to a generation AI and have the generation AI perform the calculations.

[0090] The calculation unit can improve the accuracy of the calculation by referring to literature related to the selected element during calculation. The calculation unit, for example, calculates an optimal route based on literature related to the selected element. The calculation unit can also calculate an optimal schedule based on literature related to the selected element. The calculation unit can also calculate an optimal plan based on literature related to the selected element. In this way, the accuracy of the calculation is improved by referring to the literature. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input literature data related to the selected element into the generation AI and have the generation AI improve the accuracy of the calculation.

[0091] The reservation unit can estimate the user's emotions and prioritize reservations based on the estimated user emotions. For example, if the user is relaxed, the reservation unit can prioritize reservations at relaxing facilities. Furthermore, if the user is in a hurry, the reservation unit can prioritize reservations at facilities that can be quickly completed. Furthermore, if the user is excited, the reservation unit can prioritize reservations for active activities. This allows for more appropriate reservations by prioritizing reservations according to the user's emotions. The 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 reservation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI determine the reservation priorities.

[0092] The reservation unit can improve the accuracy of reservations by taking into account the interrelationships of the selected elements when making a reservation. The reservation unit, for example, makes a reservation for an optimal facility based on the interrelationships of the selected elements. The reservation unit can also make a reservation for an optimal activity based on the interrelationships of the selected elements. The reservation unit can also make a reservation for an optimal means of transportation based on the interrelationships of the selected elements. In this way, the accuracy of reservations is improved by taking into account the interrelationships of the selected elements. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input interrelationship data of the selected elements into a generation AI and have the generation AI improve the accuracy of reservations.

[0093] When making a reservation, the reservation unit can make a reservation taking into account the attribute information of the submitter of the selected elements. The reservation unit can, for example, reserve the most suitable facility taking into account the submitter's age. The reservation unit can also reserve the most suitable activity taking into account the submitter's gender. The reservation unit can also reserve the most suitable means of transportation taking into account the submitter's health condition. In this way, by taking into account the submitter's attribute information, a more appropriate reservation can be provided. Some or all of the above-mentioned processing in the reservation unit can be performed using, for example, AI, or can be performed without using AI. For example, the reservation unit can input the submitter's attribute information data into a generation AI and have the generation AI make the reservation.

[0094] The reservation unit can estimate the user's emotions and adjust the display method of the reservation based on the estimated user's emotions. For example, if the user is relaxed, the reservation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the reservation unit can prioritize displaying information that focuses on the main points. Furthermore, if the user is excited, the reservation unit can prioritize displaying visually stimulating information. This allows for adjusting the display method according to the user's emotions to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reservation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the reservation.

[0095] The reservation unit can make a reservation taking into account the geographical distribution of the selected elements when making a reservation. For example, the reservation unit makes a reservation for the most appropriate facility based on the geographical distribution of the selected elements. The reservation unit can also make a reservation for the most appropriate activity based on the geographical distribution of the selected elements. The reservation unit can also make a reservation for the most appropriate means of transportation based on the geographical distribution of the selected elements. This makes it possible to provide a more appropriate reservation by taking the geographical distribution into consideration. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input geographical distribution data of the selected elements into a generation AI and have the generation AI make a reservation.

[0096] The reservation unit can improve the accuracy of reservations by referring to literature related to the selected elements when making a reservation. The reservation unit, for example, reserves the most appropriate facility based on literature related to the selected elements. The reservation unit can also reserve the most appropriate activity based on literature related to the selected elements. The reservation unit can also reserve the most appropriate means of transportation based on literature related to the selected elements. In this way, the accuracy of reservations is improved by referring to the related literature. Some or all of the above-described processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input literature data related to the selected elements into a generation AI and have the generation AI improve the accuracy of reservations.

[0097] The acquisition unit can estimate the user's emotions and adjust the timing of weather information acquisition based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit periodically acquires weather information. Furthermore, when the user is in a hurry, the acquisition unit can also acquire weather information in real time. Furthermore, when the user is excited, the acquisition unit can frequently acquire weather information. This allows for adjusting the acquisition timing according to the user's emotions, thereby providing more appropriate weather information. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of weather information acquisition.

[0098] When acquiring weather information, the acquisition unit can analyze the user's past travel history and select the optimal acquisition method. The acquisition unit selects the optimal acquisition method, for example, based on weather information for places the user has visited in the past. The acquisition unit can also adjust the frequency of acquiring weather information based on the user's past travel history. The acquisition unit can also analyze the user's past travel history and select the most efficient method of acquiring weather information. In this way, more appropriate weather information can be provided by analyzing the past travel history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past travel history data into a generation AI and have the generation AI select the optimal acquisition method.

[0099] When acquiring weather information, the acquisition unit can perform filtering based on the user's current travel plan. For example, the acquisition unit can prioritize acquisition of relevant weather information based on the user's current travel plan. The acquisition unit can also filter unnecessary weather information based on the user's current travel plan. The acquisition unit can also acquire optimal weather information based on the user's current travel plan. This makes it possible to provide more appropriate weather information by filtering based on the current travel plan. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's current travel plan data to the generation AI and have the generation AI filter the weather information.

[0100] The acquisition unit can estimate the user's emotions and determine the priority of weather information to be acquired based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit can prioritize acquiring long-term weather information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring short-term weather information. Furthermore, when the user is excited, the acquisition unit can prioritize acquiring detailed weather information. This allows for determining priorities according to the user's emotions, thereby providing more appropriate weather information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the weather information.

[0101] When acquiring weather information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring relevant weather information based on the user's current location. The acquisition unit can also prioritize acquiring relevant weather information based on the user's destination. The acquisition unit can also prioritize acquiring relevant weather information based on the user's travel route. This makes it possible to provide more appropriate weather information by taking the geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant weather information.

[0102] When acquiring weather information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit can prioritize acquiring weather information for travel destinations shared by the user on social media. The acquisition unit can also prioritize acquiring weather information for places that the user has "liked" on social media. The acquisition unit can also prioritize acquiring weather information from travel-related accounts that the user follows on social media. This allows for more appropriate weather information to be provided by analyzing social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, or without, AI, for example. For example, the acquisition unit can input the user's social media data into the generation AI and have the generation AI acquire related weather information.

[0103] The collection unit can estimate the user's emotions and adjust the timing of collecting transportation information based on the estimated user emotions. For example, when the user is relaxed, the collection unit can periodically collect transportation information. Furthermore, when the user is in a hurry, the collection unit can also collect transportation information in real time. Furthermore, when the user is excited, the collection unit can frequently collect transportation information. This allows for adjusting the collection timing according to the user's emotions, thereby providing more appropriate transportation information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of collecting transportation information.

[0104] When collecting transportation information, the collection unit can analyze the user's past travel history and select the optimal collection method. The collection unit selects the optimal collection method, for example, based on information about transportation modes used by the user in the past. The collection unit can also adjust the frequency of collecting transportation information based on the user's past travel history. The collection unit can also analyze the user's past travel history and select the most efficient method of collecting transportation information. In this way, more appropriate transportation information can be provided by analyzing the past travel history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past travel history data into the generation AI and have the generation AI select the optimal collection method.

[0105] When collecting transportation information, the collection unit can filter the transportation information based on the user's current travel plan. For example, the collection unit prioritizes collecting relevant transportation information based on the user's current travel plan. The collection unit can also filter unnecessary transportation information based on the user's current travel plan. The collection unit can also collect optimal transportation information based on the user's current travel plan. This makes it possible to provide more appropriate transportation information by collecting filtering based on the current travel plan. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's current travel plan data to the generation AI and have the generation AI filter the transportation information.

[0106] The collection unit can estimate the user's emotions and determine the priority of transportation information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting long-term transportation information. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting short-term transportation information. Furthermore, when the user is excited, the collection unit can also prioritize collecting detailed transportation information. This allows for determining priorities according to the user's emotions, thereby providing more appropriate transportation information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of transportation information.

[0107] When collecting transportation information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting relevant transportation information based on the user's current location. The collection unit can also prioritize collecting relevant transportation information based on the user's destination. The collection unit can also prioritize collecting relevant transportation information based on the user's travel route. This makes it possible to provide more appropriate transportation information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant transportation information.

[0108] When collecting transportation information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can prioritize collecting transportation information for travel destinations shared by the user on social media. The collection unit can also prioritize collecting transportation information for places that the user has "liked" on social media. The collection unit can also prioritize collecting transportation information from travel-related accounts that the user follows on social media. This allows for more appropriate transportation information to be provided by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, or without, AI, for example. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect related transportation information. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, generation unit, calculation unit, reservation unit, acquisition unit, and collection unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, allowing the user to select a destination and time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates a travel plan based on the selected elements. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route and schedule based on the generated plan. The reservation unit is implemented, for example, by the control unit 46A of the smart device 14, and makes reservations for suggested hotels, restaurants, and transportation. The acquisition unit acquires weather information using, for example, the camera 42 and communication I / F 44 of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit collects transportation information using, for example, the communication I / F 44 of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, generation unit, calculation unit, reservation unit, acquisition unit, and collection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214, allowing the user to select a destination and time. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a travel plan based on the selected elements. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route and schedule based on the generated plan. The reservation unit is realized, for example, by the control unit 46A of the smart glasses 214, and makes reservations for suggested hotels, restaurants, and transportation. The acquisition unit, for example, acquires weather information using the camera 42 and communication I / F 44 of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit, for example, collects transportation information using the communication I / F 44 of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the selection unit, generation unit, calculation unit, reservation unit, acquisition unit, and collection unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to select a destination and time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates a travel plan based on the selected elements. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route and schedule based on the generated plan. The reservation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and makes reservations for suggested hotels, restaurants, and transportation. The acquisition unit, for example, acquires weather information using the camera 42 and communication I / F 44 of the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit collects information on transportation facilities using, for example, the communication I / F 44 of the headset terminal 314 , and the information is analyzed by the specific processing unit 290 of the data processing device 12 . === Hard Collateral 1-4 === Each of the multiple elements, including the selection unit, generation unit, calculation unit, reservation unit, acquisition unit, and collection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414, allowing the user to select a destination and time. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a travel plan based on the selected elements. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a route and schedule based on the generated plan. The reservation unit is realized, for example, by the control unit 46A of the robot 414, and makes reservations for suggested hotels, restaurants, and transportation. The acquisition unit, for example, acquires weather information using the camera 42 and communication I / F 44 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The collection unit, for example, collects transportation information using the communication I / F 44 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The travel planning system can be equipped with a health monitoring unit that monitors the user's health condition during the trip. The health monitoring unit acquires vital data such as the user's heart rate, blood pressure, and body temperature in real time, and if an abnormality is detected, it can suggest appropriate measures. For example, if the user's heart rate suddenly rises, it can suggest taking a break. Also, if the user's blood pressure is high, it can suggest a relaxing activity. Furthermore, if the user's body temperature is high, it can suggest moving to a cooler place. This reduces health risks during travel and allows the user to enjoy their trip with peace of mind.

[0111] The travel planning system may include an emotion recording unit that records the user's emotions during the trip. The emotion recording unit records changes in the user's emotions during the trip, allowing them to be reviewed later. For example, if the user is impressed by a particular tourist spot, the emotion can be recorded. Also, if the user enjoys a particular activity, the emotion can be recorded. Furthermore, if the user is satisfied with a particular restaurant, the emotion can be recorded. This can enrich travel memories and be useful for planning the next trip.

[0112] The travel planning system may include an expense management unit that manages expenses incurred by a user during a trip. The expense management unit records expenses incurred by a user during a trip in real time, supporting the user in staying within a budget. For example, if a user pays an entrance fee at a specific tourist attraction, the expense is recorded. If a user pays for a meal at a specific restaurant, the expense can also be recorded. Furthermore, if a user pays for a fare for a specific means of transportation, the expense can also be recorded. This allows the user to manage expenses during a trip and prevent the user from going over budget.

[0113] The travel planning system may include a photo organizing unit that organizes photos taken by a user during their trip. The photo organizing unit automatically organizes photos taken by the user during their trip and creates albums. For example, the photo organizing unit may organize photos taken by the user at specific tourist spots and create an album for each tourist spot. The system may also organize photos taken by the user during specific activities and create an album for each activity. Furthermore, the system may organize photos taken at specific restaurants and create an album for each restaurant. This allows users to organize their travel memories and easily look back on them later.

[0114] The travel planning system may include a communication support unit that supports communication during the user's trip. The communication support unit supports the user in smoothly communicating with local people. For example, if the user does not understand the local language, it may provide a translation function. It may also provide information to help the user understand the local culture and customs. It may also provide event information for the user to interact with local people. This allows the user to smoothly communicate with local people and enjoy their trip more.

[0115] The trip planning system can estimate the user's emotions and suggest activities for the trip based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing activities. If the user is excited, it can also suggest active activities. Furthermore, if the user is tired, it can suggest relaxing spa or massages. In this way, activities can be suggested according to the user's emotions, making the trip more enjoyable.

[0116] The trip planning system can estimate the user's emotions and suggest meals to eat during the trip based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a restaurant with a relaxing atmosphere. If the user is excited, it can also suggest a lively restaurant. Furthermore, if the user is tired, it can suggest a cafe or bar where the user can relax. In this way, meals can be suggested according to the user's emotions, making the trip more enjoyable.

[0117] The travel planning system can estimate the user's emotions and suggest accommodations for the trip based on the estimated emotions. For example, if the user is feeling stressed, a hotel where the user can relax can be suggested. If the user is excited, a hotel with an active atmosphere can be suggested. Furthermore, if the user is tired, a hotel with a spa where the user can relax can be suggested. In this way, accommodations can be suggested according to the user's emotions, making the trip more enjoyable.

[0118] The trip planning system can estimate the user's emotions and suggest transportation methods for the trip based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a relaxing transportation method. If the user is excited, it can also suggest an active transportation method. Furthermore, if the user is tired, it can also suggest a comfortable transportation method. In this way, transportation methods according to the user's emotions can be suggested, making the trip more enjoyable.

[0119] The trip planning system can estimate the user's emotions and suggest tourist spots during the trip based on the estimated emotions. For example, if the user is feeling stressed, it can suggest tourist spots where the user can relax. If the user is excited, it can also suggest tourist spots where the user can be active. Furthermore, if the user is tired, it can suggest tourist spots with natural scenery where the user can relax. In this way, tourist spots can be suggested according to the user's emotions, making the trip more enjoyable.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The selection unit selects the elements of destination, time, meal, transportation, and weather. For example, the user can select the destination and time, but not the meal or transportation, allowing them to enjoy free activities at the destination. Step 2: The generator generates a travel plan based on the elements selected by the selector. The generator calculates the optimal route and schedule based on the selected elements, and generates a comprehensive plan that gives an overall image. For example, if a destination is selected, it will suggest the best hotels, meals, and transportation options for that destination. Step 3: The calculation unit calculates routes and schedules based on the plans generated by the generation unit. For example, it calculates the optimal means of transportation and time allocation based on the routes and schedules proposed by the generation AI. Step 4: The reservation unit makes reservations based on the information calculated by the calculation unit. For example, reservations can be made for hotels, restaurants, and transportation suggested by the generation AI all at once.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 selection section for selecting destination, time, meal, transportation, and weather elements; a generation unit that generates a travel plan based on the elements selected by the selection unit; a calculation unit that calculates a route and a schedule based on the plan generated by the generation unit; a reservation unit that makes a reservation based on the information calculated by the calculation unit. A system characterized by:

2. Equipped with an acquisition unit that acquires weather information 2. The system of claim 1.

3. Equipped with a collection department that collects information on transportation 2. The system of claim 1.

4. The generation unit Hotel, dining and transportation suggestions based on selected factors 2. The system of claim 1.

5. The reservation unit Make reservations for hotels, restaurants, and transportation suggested by the generative AI 2. The system of claim 1.

6. The calculation unit Generate plans that adapt to weather fluctuations 2. The system of claim 1.

7. The selection unit Estimate the user's emotions and adjust the order in which options are presented based on the estimated user emotions.

2. The system of claim 1.

8. The selection unit When making a selection, the system analyzes the user's past travel history and suggests the best options.

2. The system of claim 1.

Images