System

Abstract

An object of a system according to an embodiment is to optimally propose transportation, an accommodation facility, and a schedule when a user makes a travel plan.SOLUTION: A system includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives travel information from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes transportation, an accommodation facility, and a schedule on the basis of the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for users to obtain optimal suggestions for transportation, accommodation, and schedules when planning a trip.

[0005] The system according to the embodiment aims to optimally suggest transportation means, accommodations, and schedules when a user makes a travel plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives travel information from a user. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes transportation options, accommodations, and a schedule based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimally suggest transportation means, accommodations, and schedules when a user makes a travel plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning support system according to an embodiment of the present invention accepts travel information from a user, analyzes it using a generation AI, and proposes optimal transportation, accommodation, and schedule options. The travel planning support system accepts travel information such as a user's travel destination, itinerary, and events to attend. The generation AI analyzes the input information and proposes optimal transportation, accommodation, and schedule options. For example, a user may input information such as "I would like to plan a trip to attend a live concert in Tokyo." This information is input to the generation AI. The travel planning support system then analyzes the input information using the generation AI. The generation AI then proposes optimal transportation, accommodation, and schedule options based on detailed information about the destination, itinerary, and event. For example, the generation AI proposes transportation options such as airplanes and bullet trains, accommodation options such as hotels and guesthouses, and schedule options that coincide with the start time of the concert. Furthermore, the generation AI considers the user's preferences, budget, and past travel history. For example, the system proposes hotels the user has used in the past, preferred transportation options, and optimal plans within the user's budget. This allows the user to plan an efficient and comfortable trip. The travel planning support system can efficiently accept and analyze the user's travel information and make optimal proposals. For example, when planning a trip to attend a live concert or event, transportation, accommodation, schedule, etc. are all suggested at once, eliminating the hassle of planning. In addition, the generation AI takes into account the user's preferences and budget, resulting in a highly satisfying travel plan.

[0029] A travel planning support system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives travel information from a user. The travel information includes, but is not limited to, a destination, a budget, an itinerary, and the number of participants. The reception unit provides an interface for the user to input information such as a travel destination, itinerary, and events to attend. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis may be performed using, but is not limited to, data mining, statistical analysis, or a machine learning algorithm. For example, the generation AI performs an analysis to propose optimal transportation, accommodation, and schedule based on detailed information about the destination, itinerary, and event entered by the user. The proposal unit proposes optimal transportation, accommodation, and schedule based on the information analyzed by the analysis unit. The proposal is made based on, for example, a proposal format and a type of proposed item (transportation, accommodation, schedule, etc.), but is not limited to, an example. For example, the proposal unit may propose transportation options such as airplanes and bullet trains, accommodation options such as hotels and guesthouses, and schedules that coincide with the start time of a live performance. As a result, the travel planning support system according to the embodiment can efficiently accept and analyze the user's travel information and make optimal suggestions.

[0030] The suggestion unit includes a consideration unit that makes suggestions based on the user's preferences. The consideration unit makes suggestions by taking the user's preferences into consideration. Preferences include, but are not limited to, past selection history, survey results, and social media data. For example, the consideration unit makes optimal suggestions based on the user's past hotel stays and preferred means of transportation. The consideration unit can also learn the user's preferences in real time to improve the accuracy of suggestions. For example, the consideration unit reflects feedback on whether the user accepted the suggestions in the next suggestion. This enables the suggestion unit to make suggestions based on the user's preferences.

[0031] The suggestion unit includes a budget unit that makes suggestions based on the user's budget. The budget unit makes suggestions taking the user's budget into consideration. The budget may include, but is not limited to, an upper or lower budget limit, a spending priority, and the like. For example, the budget unit suggests optimal means of transportation and accommodation based on the budget information entered by the user. The budget unit can also learn the user's budget in real time to improve the accuracy of the suggestions. For example, the budget unit reflects feedback on whether the user accepted the suggestions in the next suggestion. This enables the suggestion unit to make suggestions based on the user's budget.

[0032] The suggestion unit includes a history unit that makes suggestions based on the user's past travel history. The history unit makes suggestions taking into account the user's past travel history. The travel history includes, but is not limited to, past travel destinations, accommodations, and modes of transportation used. For example, the history unit makes optimal suggestions based on places the user has visited and accommodations the user has used in the past. The history unit can also learn the user's travel history in real time to improve the accuracy of the suggestions. For example, the history unit reflects feedback on whether the user accepted the suggestions in the next suggestion. This enables the suggestion unit to make suggestions based on the user's past travel history.

[0033] The suggestion unit includes a transportation unit that suggests specific means of transportation. The transportation unit suggests the most suitable means of transportation to the user. Examples of transportation include, but are not limited to, airplanes, trains, buses, taxis, etc. For example, the transportation unit suggests the most suitable means of transportation based on the destination and itinerary input by the user. The transportation unit can also make suggestions taking into consideration the user's transportation preferences and budget. For example, the transportation unit suggests means of transportation that the user has used in the past or the most suitable means of transportation within the user's budget. This allows the suggestion unit to suggest the most suitable means of transportation to the user.

[0034] The suggestion unit includes an accommodation unit that suggests specific accommodation options. The accommodation unit suggests the most suitable accommodation options for the user. Examples of accommodation options include, but are not limited to, hotels, inns, guesthouses, and Airbnb. For example, the accommodation unit suggests the most suitable accommodation options based on the destination and dates entered by the user. The accommodation unit can also make suggestions taking into consideration the user's accommodation preferences and budget. For example, the accommodation unit suggests accommodation options that the user has used in the past or the most suitable accommodation options within the user's budget. This allows the suggestion unit to suggest the most suitable accommodation options for the user.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history. The input history includes, for example, past search keywords, input formats, frequency, etc., but is not limited to these examples. For example, the reception unit automatically displays destinations and dates that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information related to a specific event from the user's past input history. This allows the reception unit to suggest the optimal input method based on the user's past input history.

[0036] The reception unit can automatically acquire the user's current location information and calculate the distance to the destination when receiving travel information. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The location information is acquired using technologies such as GPS data, IP address, and Wi-Fi location information. For example, when the user inputs a destination, the reception unit can suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the current location in real time and reflect it as the departure point. This allows the reception unit to provide optimal travel information based on the user's current location.

[0037] When receiving travel information, the reception unit can select the optimal input means depending on the user's input method. For example, the reception unit can automatically set the destination and itinerary simply by the user inputting, for example, "I want to go to a live concert in Tokyo" by voice. Input methods include, but are not limited to, voice input, text input, image input, and the like. For example, the reception unit can easily set travel information by the user performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set travel information more intuitively by combining voice input and gesture input. This allows the reception unit to provide the optimal input means depending on the user's input method.

[0038] When receiving travel information, the reception unit can analyze the user's social media activity and automatically suggest related travel information. The reception unit can suggest related travel information based on, for example, the locations where the user has checked in on social media. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can analyze the content of the user's social media posts and suggest related events and tourist spots. The reception unit can also suggest related travel information based on the activities of the user's friends on social media. This allows the reception unit to provide optimal travel information based on the user's social media activity.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when receiving travel information. The reception unit, for example, suggests an optimal input method based on feedback previously provided by the user. Feedback includes, but is not limited to, evaluation comments, star ratings, survey results, etc. For example, the reception unit preferentially suggests a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. This allows the reception unit to provide an optimal input method based on the user's past feedback.

[0040] When accepting travel information, the reception unit can provide an optimal input interface taking into account the user's device information. For example, if the user is using a smartphone, the reception unit provides an input interface tailored to the screen size. Device information includes, but is not limited to, the device type, OS, browser, and resolution. For example, if the user is using a tablet, the reception unit provides an input interface optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also provide a simple and highly visible input interface. This allows the reception unit to provide an optimal input interface based on the user's device information.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the travel information. For example, the analysis unit analyzes information related to important events in detail and generates optimal suggestions. Examples of importance include, but are not limited to, the user's priority, urgency, and impact. For example, the analysis unit quickly analyzes general travel information and generates efficient suggestions. The analysis unit can also adjust the level of detail of the analysis according to the user's preferences. This allows the analysis unit to provide optimal analysis based on the importance of the travel information.

[0042] The analysis unit can apply different analysis algorithms depending on the travel category during analysis. For example, the analysis unit analyzes travel information related to a live event using a specific algorithm. Travel categories include, but are not limited to, business travel, leisure travel, and family travel. For example, the analysis unit analyzes information related to sightseeing travel using a different algorithm. Furthermore, the analysis unit can analyze information related to business travel using yet another algorithm. This allows the analysis unit to provide an optimal analysis algorithm depending on the travel category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, selects the optimal analysis method based on the user's past analysis results. Past analysis results include, but are not limited to, past proposal contents, evaluation results, feedback, etc. For example, the analysis unit extracts specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and adjust the analysis algorithm. This allows the analysis unit to provide an optimal analysis based on the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of travel information. For example, the analysis unit prioritizes analysis of recent travel information and generates prompt suggestions. The submission time includes, but is not limited to, the submission date and time, submission frequency, and submission timing. For example, the analysis unit analyzes future travel information in detail and generates multiple suggestions. The analysis unit can also adjust the priority of analysis based on the user's schedule. This allows the analysis unit to provide an optimal analysis based on the time of submission of travel information.

[0045] During analysis, the analysis unit can adjust the analysis order based on the relevance of travel information. For example, the analysis unit prioritizes analysis of information related to important events. Relevance includes, but is not limited to, common keywords, related topics, and past history. For example, the analysis unit may analyze general travel information later. The analysis unit can also adjust the analysis order according to user preferences. This allows the analysis unit to provide an optimal analysis order based on the relevance of travel information.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit displays the analysis results using detailed technical terminology. Examples of the level of expertise include, but are not limited to, survey results, past selection history, and occupational information. For example, if the user only has general knowledge, the analysis unit displays the analysis results using concise and easy-to-understand terms. The analysis unit can also adjust the use of optimal technical terminology based on the user's past feedback. This allows the analysis unit to provide optimal analysis results according to the user's level of expertise.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the trip when making a suggestion. For example, the suggestion unit makes detailed suggestions related to important events and provides multiple options. The importance level includes, but is not limited to, the depth of information, specificity, and comprehensiveness. For example, the suggestion unit can quickly suggest general travel information and provide efficient options. The suggestion unit can also adjust the level of detail of the suggestion based on the user's preferences. This allows the suggestion unit to provide optimal suggestions based on the importance of the trip.

[0048] The suggestion unit can apply different suggestion algorithms depending on the travel category when making a suggestion. For example, the suggestion unit uses a specific algorithm to make suggestions related to live events. Travel categories include, but are not limited to, business trips, leisure trips, and family trips. For example, the suggestion unit can use a different algorithm to make suggestions related to sightseeing trips. Furthermore, the suggestion unit can also use yet another algorithm to make suggestions related to business trips. This allows the suggestion unit to provide an optimal suggestion algorithm depending on the travel category.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, selects an optimal suggestion method based on the user's past suggestion results. Past suggestion results include, but are not limited to, past suggestion content, evaluation results, feedback, etc. For example, the suggestion unit extracts specific patterns from the user's past suggestion results to improve the accuracy of the suggestion. The suggestion unit can also analyze the user's past suggestion results and adjust the suggestion algorithm. This allows the suggestion unit to provide optimal suggestions based on the user's past suggestion results.

[0050] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of the travel information. For example, the suggestion unit can prioritize suggesting the most recent travel information and provide quick options. The submission time includes, but is not limited to, the submission date and time, the submission frequency, and the submission timing. For example, the suggestion unit can suggest detailed information about future travel and provide multiple options. The suggestion unit can also adjust the priority of the suggestions based on the user's schedule. This allows the suggestion unit to provide optimal suggestions based on the time of submission of the travel information.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of travel information when making suggestions. For example, the suggestion unit prioritizes suggesting information related to important events. Examples of relevance include, but are not limited to, common keywords, related topics, and past history. For example, the suggestion unit may suggest general travel information later. The suggestion unit can also adjust the order of suggestions according to the user's preferences. This allows the suggestion unit to provide an optimal suggestion order based on the relevance of travel information.

[0052] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the suggestion using detailed technical terminology. Examples of the level of expertise include, but are not limited to, survey results, past selection history, and occupational information. For example, if the user has only general knowledge, the suggestion unit makes the suggestion using concise and easy-to-understand terminology. The suggestion unit can also adjust the use of optimal technical terminology based on the user's past feedback. This allows the suggestion unit to provide optimal suggestions according to the user's level of expertise.

[0053] During consideration, the consideration unit can analyze the user's past preferences and select an optimal consideration method. The consideration unit selects an optimal consideration method based on, for example, the user's past preferences. Past preferences include, but are not limited to, past selection history, evaluation results, feedback, etc. For example, the consideration unit extracts a specific pattern from the user's past preferences and adjusts the consideration method. The consideration unit can also analyze the user's past preferences and adjust the consideration algorithm. This allows the consideration unit to provide an optimal consideration method based on the user's past preferences.

[0054] The consideration unit can customize the preferred consideration means based on the user's current living situation during consideration. The consideration unit, for example, selects the optimal consideration means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the consideration unit extracts a specific pattern from the user's current living situation and adjusts the consideration means. The consideration unit can also analyze the user's current living situation and adjust the consideration algorithm. This allows the consideration unit to provide the optimal consideration means based on the user's current living situation.

[0055] The consideration unit can select the optimal consideration method by taking into account the user's geographical location information during consideration. The consideration unit selects the optimal consideration method, for example, based on the user's current location. Geographical location information includes, but is not limited to, GPS data, IP address, Wi-Fi location information, etc. For example, the consideration unit extracts a specific pattern from the user's geographical location information and adjusts the consideration method. The consideration unit can also analyze the user's geographical location information and adjust the consideration algorithm. This allows the consideration unit to provide the optimal consideration method based on the user's geographical location information.

[0056] During consideration, the consideration unit can analyze the user's social media activity and suggest a preferred consideration method. The consideration unit can suggest an optimal consideration method, for example, based on the content of the user's social media posts. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the consideration unit can suggest related consideration methods based on the activities of the user's friends on social media. The consideration unit can also suggest an optimal consideration method based on the user's check-in information on social media. This allows the consideration unit to provide an optimal consideration method based on the user's social media activity.

[0057] The budgeting unit can analyze the user's past consumption behavior and select an optimal budget consideration method when considering the budget. The budgeting unit selects the optimal budget consideration method, for example, based on the user's past consumption behavior. Consumption behavior includes, but is not limited to, purchase history, spending patterns, credit card usage history, etc. For example, the budgeting unit extracts a specific pattern from the user's past consumption behavior and adjusts the budget consideration method. The budgeting unit can also analyze the user's past consumption behavior and adjust the budget consideration algorithm. In this way, the budgeting unit can provide an optimal budget consideration method based on the user's past consumption behavior.

[0058] The budgeting unit can customize the budgeting consideration means based on the user's current living situation when considering the budget. For example, the budgeting unit selects the optimal budgeting consideration means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the budgeting unit extracts a specific pattern from the user's current living situation and adjusts the budgeting consideration means. The budgeting unit can also analyze the user's current living situation and adjust the budgeting algorithm. In this way, the budgeting unit can provide the optimal budgeting consideration means based on the user's current living situation.

[0059] The budgeting unit may select an optimal budget consideration method by taking into account the user's geographical location information when considering the budget. The budgeting unit may select an optimal budget consideration method based on, for example, the user's current location. Examples of geographical location information include, but are not limited to, GPS data, IP address, and Wi-Fi location information. For example, the budgeting unit may extract a specific pattern from the user's geographical location information and adjust the budget consideration method. The budgeting unit may also analyze the user's geographical location information and adjust the budget consideration algorithm. This allows the budgeting unit to provide an optimal budget consideration method based on the user's geographical location information.

[0060] The budgeting unit may analyze the user's social media activity when considering a budget and suggest budgeting measures. The budgeting unit may suggest optimal budgeting measures, for example, based on the content of the user's social media posts. Social media activity may include, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the budgeting unit may suggest related budgeting measures based on the activity of the user's friends on social media. The budgeting unit may also suggest optimal budgeting measures based on the user's social media check-in information. In this way, the budgeting unit may provide optimal budgeting measures based on the user's social media activity.

[0061] When considering the history, the history unit can analyze the user's past travel history and select an optimal history consideration method. The history unit selects the optimal history consideration method, for example, based on the user's past travel history. The travel history includes, for example, past travel destinations, accommodations, transportation modes used, etc., but is not limited to these examples. For example, the history unit extracts a specific pattern from the user's past travel history and adjusts the history consideration method. The history unit can also analyze the user's past travel history and adjust the history consideration algorithm. This allows the history unit to provide an optimal history consideration method based on the user's past travel history.

[0062] The history unit can customize the history consideration means based on the user's current living situation when considering the history. The history unit, for example, selects the optimal history consideration means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the history unit extracts a specific pattern from the user's current living situation and adjusts the history consideration means. The history unit can also analyze the user's current living situation and adjust the history consideration algorithm. This allows the history unit to provide the optimal history consideration means based on the user's current living situation.

[0063] When considering the history, the history unit can select an optimal history consideration method by taking into account the user's geographical location information. The history unit selects the optimal history consideration method, for example, based on the user's current location. Geographical location information includes, but is not limited to, GPS data, IP address, Wi-Fi location information, etc. For example, the history unit extracts a specific pattern from the user's geographical location information and adjusts the history consideration method. The history unit can also analyze the user's geographical location information and adjust the history consideration algorithm. This allows the history unit to provide an optimal history consideration method based on the user's geographical location information.

[0064] When considering the history, the history unit can analyze the user's social media activity and suggest a history consideration method. The history unit can suggest an optimal history consideration method, for example, based on the content of the user's posts on social media. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the history unit can suggest related history consideration methods based on the activities of the user's friends on social media. The history unit can also suggest an optimal history consideration method based on the user's check-in information on social media. This allows the history unit to provide an optimal history consideration method based on the user's social media activity.

[0065] When suggesting transportation options, the Department of Transportation and Communications can analyze the user's past transportation usage history to select the optimal suggestion method. For example, the Department of Transportation and Communications selects the optimal suggestion method based on the user's past transportation usage history. The transportation usage history may include, but is not limited to, the transportation modes used in the past, the frequency of use, and the time of use. For example, the Department of Transportation and Communications may extract specific patterns from the user's past transportation usage history and adjust the suggestion method. The Department of Transportation and Communications may also analyze the user's past transportation usage history and adjust the suggestion algorithm. This allows the Department of Transportation and Communications to provide the optimal suggestion method based on the user's past transportation usage history.

[0066] When suggesting transportation options, the Department of Transportation and Communications can customize the suggested transportation options based on the user's current living situation. For example, the Department of Transportation and Communications can select the optimal suggested transportation options based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the Department of Transportation and Communications can extract specific patterns from the user's current living situation and adjust the suggested transportation options. The Department of Transportation and Communications can also analyze the user's current living situation and adjust the suggestion algorithm. In this way, the Department of Transportation and Communications can provide the optimal suggested transportation options based on the user's current living situation.

[0067] When suggesting transportation options, the Ministry of Transportation and Communications may select an optimal suggestion method by taking into account the user's geographical location information. For example, the Ministry of Transportation and Communications may select an optimal transportation suggestion method based on the user's current location. Examples of geographical location information include, but are not limited to, GPS data, IP address, and Wi-Fi location information. For example, the Ministry of Transportation and Communications may extract specific patterns from the user's geographical location information and adjust the suggestion method. The Ministry of Transportation and Communications may also analyze the user's geographical location information and adjust the suggestion algorithm. This allows the Ministry of Transportation and Communications to provide an optimal transportation suggestion method based on the user's geographical location information.

[0068] When suggesting transportation options, the Department of Transportation may analyze the user's social media activity to suggest transportation options. The Department of Transportation may suggest optimal transportation options based on, for example, the user's social media posts. Social media activities may include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the Department of Transportation may suggest related transportation options based on the activities of the user's friends on social media. The Department of Transportation may also suggest optimal transportation options based on the user's social media check-in information. This allows the Department of Transportation to provide optimal transportation options based on the user's social media activities.

[0069] When suggesting accommodations, the accommodation unit can analyze the user's past accommodation history and select the optimal suggestion method. The accommodation unit selects the optimal suggestion method, for example, based on the user's past accommodation history. The accommodation history includes, for example, but is not limited to, the facilities where the user has stayed in the past, the frequency of stays, and the time of stay. For example, the accommodation unit extracts a specific pattern from the user's past accommodation history and adjusts the suggestion method. The accommodation unit can also analyze the user's past accommodation history and adjust the suggestion algorithm. This allows the accommodation unit to provide the optimal accommodation suggestion method based on the user's past accommodation history.

[0070] When suggesting accommodations, the accommodation unit can customize the accommodation suggestion means based on the user's current living situation. The accommodation unit, for example, selects the optimal accommodation suggestion means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the accommodation unit extracts a specific pattern from the user's current living situation and adjusts the suggestion means. The accommodation unit can also analyze the user's current living situation and adjust the suggestion algorithm. This allows the accommodation unit to provide the optimal accommodation suggestion means based on the user's current living situation.

[0071] When suggesting accommodations, the accommodation unit can select the optimal suggestion method taking into account the user's geographical location information. The accommodation unit selects the optimal accommodation suggestion method, for example, based on the user's current location. Geographical location information includes, but is not limited to, GPS data, IP address, Wi-Fi location information, etc. For example, the accommodation unit extracts a specific pattern from the user's geographical location information and adjusts the suggestion method. The accommodation unit can also analyze the user's geographical location information and adjust the suggestion algorithm. This allows the accommodation unit to provide the optimal accommodation suggestion method based on the user's geographical location information.

[0072] When suggesting accommodations, the accommodation unit can analyze the user's social media activity and suggest accommodation suggestion means. The accommodation unit can suggest optimal accommodation suggestion means, for example, based on the content of the user's social media posts. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the accommodation unit can suggest related accommodation suggestion means based on the activity of the user's friends on social media. The accommodation unit can also suggest optimal accommodation suggestion means based on the user's check-in information on social media. This allows the accommodation unit to provide optimal accommodation suggestion means based on the user's social media activity.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The analysis unit can take real-time weather information into account when analyzing the user's travel information. For example, the analysis unit can obtain the weather forecast for the destination and prioritize indoor tourist spots if it is raining. If the user prefers outdoor activities, the analysis unit can also suggest a schedule for sunny days. Furthermore, the analysis unit can adjust transportation options based on the weather information and suggest safer transportation methods in bad weather. This allows the analysis unit to make the user's travel plans more flexible and safe.

[0075] The suggestion unit can provide information about local culture and customs for the user's travel plans. For example, the suggestion unit can include cultural etiquette and points of caution for the country or region the user will visit in the suggestion. The suggestion unit can also provide information about local festivals and events, allowing the user to enjoy special experiences during their trip. Furthermore, the suggestion unit can suggest recommended local restaurants and dishes based on the user's food preferences. This allows the suggestion unit to further enrich the user's travel experience.

[0076] The suggestion unit can suggest eco-friendly options for the user's travel plans. For example, the suggestion unit can preferentially suggest environmentally friendly accommodations and means of transportation. The suggestion unit can also suggest nature conservation activities and eco-tours in areas the user will visit. Furthermore, the suggestion unit can calculate the carbon footprint of the user during the user's travel and suggest environmentally friendly options. In this way, the suggestion unit can enable the user to enjoy an environmentally friendly trip.

[0077] The suggestion unit can provide information about health management for the user's travel plans. For example, the suggestion unit can include advice and precautions for the user to maintain their health during their trip in the suggestion. The suggestion unit can also provide information about medical facilities and pharmacies in the area the user will visit, allowing the user to prepare for emergencies. Furthermore, the suggestion unit can suggest appropriate activities and meal options according to the user's health condition. This allows the suggestion unit to enable the user to enjoy their trip with peace of mind.

[0078] The suggestion unit can provide local transportation information in real time for the user's travel plans. For example, the suggestion unit can provide operation status and delay information for the transportation means the user plans to use. The suggestion unit can also suggest the optimal travel route for the user to arrive at their destination and provide advice on how to avoid traffic congestion. Furthermore, the suggestion unit can provide information on public transportation means available to the user locally and how to purchase tickets. This allows the suggestion unit to smooth the user's travel and reduce travel stress.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The reception unit receives travel information from the user. The travel information includes, for example, the destination, budget, itinerary, number of participants, etc. The reception unit provides an interface for the user to input information such as the travel destination, itinerary, and events to attend. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI performs analysis to suggest optimal transportation, accommodation, and schedules based on the destination, schedule, and event details entered by the user. Step 3: The suggestion unit proposes optimal means of transportation, accommodations, and schedules based on the information analyzed by the analysis unit. Suggestions are made based on the format of the proposal and the type of items to be proposed (means of transportation, accommodations, schedule, etc.). For example, the suggestion unit proposes means of transportation such as airplanes and bullet trains, accommodations such as hotels and guesthouses, and schedules that coincide with the start time of the live performance.

[0081] (Example 2) A travel planning support system according to an embodiment of the present invention accepts travel information from a user, analyzes it using a generation AI, and proposes optimal transportation, accommodation, and schedule options. The travel planning support system accepts travel information such as a user's travel destination, itinerary, and events to attend. The generation AI analyzes the input information and proposes optimal transportation, accommodation, and schedule options. For example, a user may input information such as "I would like to plan a trip to attend a live concert in Tokyo." This information is input to the generation AI. The travel planning support system then analyzes the input information using the generation AI. The generation AI then proposes optimal transportation, accommodation, and schedule options based on detailed information about the destination, itinerary, and event. For example, the generation AI proposes transportation options such as airplanes and bullet trains, accommodation options such as hotels and guesthouses, and schedule options that coincide with the start time of the concert. Furthermore, the generation AI considers the user's preferences, budget, and past travel history. For example, the system proposes hotels the user has used in the past, preferred transportation options, and optimal plans within the user's budget. This allows the user to plan an efficient and comfortable trip. The travel planning support system can efficiently accept and analyze the user's travel information and make optimal proposals. For example, when planning a trip to attend a live concert or event, transportation, accommodation, schedule, etc. are all suggested at once, eliminating the hassle of planning. In addition, the generation AI takes into account the user's preferences and budget, resulting in a highly satisfying travel plan.

[0082] A travel planning support system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives travel information from a user. The travel information includes, but is not limited to, a destination, a budget, an itinerary, and the number of participants. The reception unit provides an interface for the user to input information such as a travel destination, itinerary, and events to attend. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis may be performed using, but is not limited to, data mining, statistical analysis, or a machine learning algorithm. For example, the generation AI performs an analysis to propose optimal transportation, accommodation, and schedule based on detailed information about the destination, itinerary, and event entered by the user. The proposal unit proposes optimal transportation, accommodation, and schedule based on the information analyzed by the analysis unit. The proposal is made based on, for example, a proposal format and a type of proposed item (transportation, accommodation, schedule, etc.), but is not limited to, an example. For example, the proposal unit may propose transportation options such as airplanes and bullet trains, accommodation options such as hotels and guesthouses, and schedules that coincide with the start time of a live performance. As a result, the travel planning support system according to the embodiment can efficiently accept and analyze the user's travel information and make optimal suggestions.

[0083] The suggestion unit includes a consideration unit that makes suggestions based on the user's preferences. The consideration unit makes suggestions by taking the user's preferences into consideration. Preferences include, but are not limited to, past selection history, survey results, and social media data. For example, the consideration unit makes optimal suggestions based on the user's past hotel stays and preferred means of transportation. The consideration unit can also learn the user's preferences in real time to improve the accuracy of suggestions. For example, the consideration unit reflects feedback on whether the user accepted the suggestions in the next suggestion. This enables the suggestion unit to make suggestions based on the user's preferences.

[0084] The suggestion unit includes a budget unit that makes suggestions based on the user's budget. The budget unit makes suggestions taking the user's budget into consideration. The budget may include, but is not limited to, an upper or lower budget limit, a spending priority, and the like. For example, the budget unit suggests optimal means of transportation and accommodation based on the budget information entered by the user. The budget unit can also learn the user's budget in real time to improve the accuracy of the suggestions. For example, the budget unit reflects feedback on whether the user accepted the suggestions in the next suggestion. This enables the suggestion unit to make suggestions based on the user's budget.

[0085] The suggestion unit includes a history unit that makes suggestions based on the user's past travel history. The history unit makes suggestions taking into account the user's past travel history. The travel history includes, but is not limited to, past travel destinations, accommodations, and modes of transportation used. For example, the history unit makes optimal suggestions based on places the user has visited and accommodations the user has used in the past. The history unit can also learn the user's travel history in real time to improve the accuracy of the suggestions. For example, the history unit reflects feedback on whether the user accepted the suggestions in the next suggestion. This enables the suggestion unit to make suggestions based on the user's past travel history.

[0086] The suggestion unit includes a transportation unit that suggests specific means of transportation. The transportation unit suggests the most suitable means of transportation to the user. Examples of transportation include, but are not limited to, airplanes, trains, buses, taxis, etc. For example, the transportation unit suggests the most suitable means of transportation based on the destination and itinerary input by the user. The transportation unit can also make suggestions taking into consideration the user's transportation preferences and budget. For example, the transportation unit suggests means of transportation that the user has used in the past or the most suitable means of transportation within the user's budget. This allows the suggestion unit to suggest the most suitable means of transportation to the user.

[0087] The suggestion unit includes an accommodation unit that suggests specific accommodation options. The accommodation unit suggests the most suitable accommodation options for the user. Examples of accommodation options include, but are not limited to, hotels, inns, guesthouses, and Airbnb. For example, the accommodation unit suggests the most suitable accommodation options based on the destination and dates entered by the user. The accommodation unit can also make suggestions taking into consideration the user's accommodation preferences and budget. For example, the accommodation unit suggests accommodation options that the user has used in the past or the most suitable accommodation options within the user's budget. This allows the suggestion unit to suggest the most suitable accommodation options for the user.

[0088] The reception unit can estimate the user's emotions and customize the travel information input interface based on the estimated user emotions. The reception unit, for example, estimates the user's emotions when entering travel information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick entry of travel information. This allows the reception unit to provide an optimal input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0089] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history. The input history includes, for example, past search keywords, input formats, frequency, etc., but is not limited to these examples. For example, the reception unit automatically displays destinations and dates that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information related to a specific event from the user's past input history. This allows the reception unit to suggest the optimal input method based on the user's past input history.

[0090] The reception unit can automatically acquire the user's current location information and calculate the distance to the destination when receiving travel information. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. The location information is acquired using technologies such as GPS data, IP address, and Wi-Fi location information. For example, when the user inputs a destination, the reception unit can suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while traveling, the reception unit can update the current location in real time and reflect it as the departure point. This allows the reception unit to provide optimal travel information based on the user's current location.

[0091] When receiving travel information, the reception unit can select the optimal input means depending on the user's input method. For example, the reception unit can automatically set the destination and itinerary simply by the user inputting, for example, "I want to go to a live concert in Tokyo" by voice. Input methods include, but are not limited to, voice input, text input, image input, and the like. For example, the reception unit can easily set travel information by the user performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set travel information more intuitively by combining voice input and gesture input. This allows the reception unit to provide the optimal input means depending on the user's input method.

[0092] The reception unit can estimate the user's emotions and determine the priority of input information based on the estimated user emotions. For example, if the user is nervous, the reception unit prioritizes input of important information and postpones input of other information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the reception unit sequentially inputs detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize input of the minimum necessary information. This allows the reception unit to provide optimal input information priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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.

[0093] When receiving travel information, the reception unit can analyze the user's social media activity and automatically suggest related travel information. The reception unit can suggest related travel information based on, for example, the locations where the user has checked in on social media. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can analyze the content of the user's social media posts and suggest related events and tourist spots. The reception unit can also suggest related travel information based on the activities of the user's friends on social media. This allows the reception unit to provide optimal travel information based on the user's social media activity.

[0094] The reception unit can customize the input method by reflecting the user's past feedback when receiving travel information. The reception unit, for example, suggests an optimal input method based on feedback previously provided by the user. Feedback includes, but is not limited to, evaluation comments, star ratings, survey results, etc. For example, the reception unit preferentially suggests a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. This allows the reception unit to provide an optimal input method based on the user's past feedback.

[0095] When accepting travel information, the reception unit can provide an optimal input interface taking into account the user's device information. For example, if the user is using a smartphone, the reception unit provides an input interface tailored to the screen size. Device information includes, but is not limited to, the device type, OS, browser, and resolution. For example, if the user is using a tablet, the reception unit provides an input interface optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also provide a simple and highly visible input interface. This allows the reception unit to provide an optimal input interface based on the user's device information.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and generates multiple suggestions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the analysis unit performs a quick analysis and generates the most efficient suggestions. Furthermore, if the user is excited, the analysis unit can also generate visually appealing suggestions. This allows the analysis unit to provide an optimal analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the travel information. For example, the analysis unit analyzes information related to important events in detail and generates optimal suggestions. Examples of importance include, but are not limited to, the user's priority, urgency, and impact. For example, the analysis unit quickly analyzes general travel information and generates efficient suggestions. The analysis unit can also adjust the level of detail of the analysis according to the user's preferences. This allows the analysis unit to provide optimal analysis based on the importance of the travel information.

[0098] The analysis unit can apply different analysis algorithms depending on the travel category during analysis. For example, the analysis unit analyzes travel information related to a live event using a specific algorithm. Travel categories include, but are not limited to, business travel, leisure travel, and family travel. For example, the analysis unit analyzes information related to sightseeing travel using a different algorithm. Furthermore, the analysis unit can analyze information related to business travel using yet another algorithm. This allows the analysis unit to provide an optimal analysis algorithm depending on the travel category.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, selects the optimal analysis method based on the user's past analysis results. Past analysis results include, but are not limited to, past proposal contents, evaluation results, feedback, etc. For example, the analysis unit extracts specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and adjust the analysis algorithm. This allows the analysis unit to provide an optimal analysis based on the user's past analysis results.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the analysis unit to provide an optimal display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of travel information. For example, the analysis unit prioritizes analysis of recent travel information and generates prompt suggestions. The submission time includes, but is not limited to, the submission date and time, submission frequency, and submission timing. For example, the analysis unit analyzes future travel information in detail and generates multiple suggestions. The analysis unit can also adjust the priority of analysis based on the user's schedule. This allows the analysis unit to provide an optimal analysis based on the time of submission of travel information.

[0102] During analysis, the analysis unit can adjust the analysis order based on the relevance of travel information. For example, the analysis unit prioritizes analysis of information related to important events. Relevance includes, but is not limited to, common keywords, related topics, and past history. For example, the analysis unit may analyze general travel information later. The analysis unit can also adjust the analysis order according to user preferences. This allows the analysis unit to provide an optimal analysis order based on the relevance of travel information.

[0103] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit displays the analysis results using detailed technical terminology. Examples of the level of expertise include, but are not limited to, survey results, past selection history, and occupational information. For example, if the user only has general knowledge, the analysis unit displays the analysis results using concise and easy-to-understand terms. The analysis unit can also adjust the use of optimal technical terminology based on the user's past feedback. This allows the analysis unit to provide optimal analysis results according to the user's level of expertise.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions and presents multiple options. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the suggestion unit can provide quick and concise suggestions. Also, if the user is excited, the suggestion unit can provide visually appealing suggestions. This allows the suggestion unit to provide an optimal way to express suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the trip when making a suggestion. For example, the suggestion unit makes detailed suggestions related to important events and provides multiple options. The importance level includes, but is not limited to, the depth of information, specificity, and comprehensiveness. For example, the suggestion unit can quickly suggest general travel information and provide efficient options. The suggestion unit can also adjust the level of detail of the suggestion based on the user's preferences. This allows the suggestion unit to provide optimal suggestions based on the importance of the trip.

[0106] The suggestion unit can apply different suggestion algorithms depending on the travel category when making a suggestion. For example, the suggestion unit uses a specific algorithm to make suggestions related to live events. Travel categories include, but are not limited to, business trips, leisure trips, and family trips. For example, the suggestion unit can use a different algorithm to make suggestions related to sightseeing trips. Furthermore, the suggestion unit can also use yet another algorithm to make suggestions related to business trips. This allows the suggestion unit to provide an optimal suggestion algorithm depending on the travel category.

[0107] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, selects an optimal suggestion method based on the user's past suggestion results. Past suggestion results include, but are not limited to, past suggestion content, evaluation results, feedback, etc. For example, the suggestion unit extracts specific patterns from the user's past suggestion results to improve the accuracy of the suggestion. The suggestion unit can also analyze the user's past suggestion results and adjust the suggestion algorithm. This allows the suggestion unit to provide optimal suggestions based on the user's past suggestion results.

[0108] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit provides short, to-the-point suggestions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the suggestion unit to provide the optimal length of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0109] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the time of submission of the travel information. For example, the suggestion unit can prioritize suggesting the most recent travel information and provide quick options. The submission time includes, but is not limited to, the submission date and time, the submission frequency, and the submission timing. For example, the suggestion unit can suggest detailed information about future travel and provide multiple options. The suggestion unit can also adjust the priority of the suggestions based on the user's schedule. This allows the suggestion unit to provide optimal suggestions based on the time of submission of the travel information.

[0110] The suggestion unit can adjust the order of suggestions based on the relevance of travel information when making suggestions. For example, the suggestion unit prioritizes suggesting information related to important events. Examples of relevance include, but are not limited to, common keywords, related topics, and past history. For example, the suggestion unit may suggest general travel information later. The suggestion unit can also adjust the order of suggestions according to the user's preferences. This allows the suggestion unit to provide an optimal suggestion order based on the relevance of travel information.

[0111] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the suggestion using detailed technical terminology. Examples of the level of expertise include, but are not limited to, survey results, past selection history, and occupational information. For example, if the user has only general knowledge, the suggestion unit makes the suggestion using concise and easy-to-understand terminology. The suggestion unit can also adjust the use of optimal technical terminology based on the user's past feedback. This allows the suggestion unit to provide optimal suggestions according to the user's level of expertise.

[0112] The consideration unit can estimate the user's emotions and adjust a preference consideration method based on the estimated user emotions. For example, when the user is relaxed, the consideration unit makes a suggestion by taking into account detailed preferences. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is in a hurry, the consideration unit quickly considers preferences and makes a suggestion. Furthermore, when the user is excited, the consideration unit can also make a visually appealing suggestion. This allows the consideration unit to provide an optimal preference consideration method according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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.

[0113] During consideration, the consideration unit can analyze the user's past preferences and select an optimal consideration method. The consideration unit selects an optimal consideration method based on, for example, the user's past preferences. Past preferences include, but are not limited to, past selection history, evaluation results, feedback, etc. For example, the consideration unit extracts a specific pattern from the user's past preferences and adjusts the consideration method. The consideration unit can also analyze the user's past preferences and adjust the consideration algorithm. This allows the consideration unit to provide an optimal consideration method based on the user's past preferences.

[0114] The consideration unit can customize the preferred consideration means based on the user's current living situation during consideration. The consideration unit, for example, selects the optimal consideration means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the consideration unit extracts a specific pattern from the user's current living situation and adjusts the consideration means. The consideration unit can also analyze the user's current living situation and adjust the consideration algorithm. This allows the consideration unit to provide the optimal consideration means based on the user's current living situation.

[0115] The consideration unit can estimate the user's emotions and determine the priority of preferences based on the estimated user emotions. For example, if the user is nervous, the consideration unit prioritizes important preferences. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the consideration unit sequentially considers detailed preferences. Furthermore, if the user is in a hurry, the consideration unit can prioritize the minimum necessary preferences. This allows the consideration unit to provide an optimal preference priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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.

[0116] The consideration unit can select the optimal consideration method by taking into account the user's geographical location information during consideration. The consideration unit selects the optimal consideration method, for example, based on the user's current location. Geographical location information includes, but is not limited to, GPS data, IP address, Wi-Fi location information, etc. For example, the consideration unit extracts a specific pattern from the user's geographical location information and adjusts the consideration method. The consideration unit can also analyze the user's geographical location information and adjust the consideration algorithm. This allows the consideration unit to provide the optimal consideration method based on the user's geographical location information.

[0117] During consideration, the consideration unit can analyze the user's social media activity and suggest a preferred consideration method. The consideration unit can suggest an optimal consideration method, for example, based on the content of the user's social media posts. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the consideration unit can suggest related consideration methods based on the activities of the user's friends on social media. The consideration unit can also suggest an optimal consideration method based on the user's check-in information on social media. This allows the consideration unit to provide an optimal consideration method based on the user's social media activity.

[0118] The budgeting unit can estimate the user's emotions and adjust the budget consideration method based on the estimated user emotions. For example, if the user is relaxed, the budgeting unit makes a proposal taking into account a detailed budget. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the budgeting unit can quickly consider the budget and make a proposal. Furthermore, if the user is excited, the budgeting unit can also make a visually appealing proposal. This allows the budgeting unit to provide an optimal budget consideration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The budgeting unit can analyze the user's past consumption behavior and select an optimal budget consideration method when considering the budget. The budgeting unit selects the optimal budget consideration method, for example, based on the user's past consumption behavior. Consumption behavior includes, but is not limited to, purchase history, spending patterns, credit card usage history, etc. For example, the budgeting unit extracts a specific pattern from the user's past consumption behavior and adjusts the budget consideration method. The budgeting unit can also analyze the user's past consumption behavior and adjust the budget consideration algorithm. In this way, the budgeting unit can provide an optimal budget consideration method based on the user's past consumption behavior.

[0120] The budgeting unit can customize the budgeting consideration means based on the user's current living situation when considering the budget. For example, the budgeting unit selects the optimal budgeting consideration means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the budgeting unit extracts a specific pattern from the user's current living situation and adjusts the budgeting consideration means. The budgeting unit can also analyze the user's current living situation and adjust the budgeting algorithm. In this way, the budgeting unit can provide the optimal budgeting consideration means based on the user's current living situation.

[0121] The budgeting unit can estimate the user's emotions and determine budget priorities based on the estimated user emotions. For example, if the user is nervous, the budgeting unit prioritizes important budget items. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the budgeting unit sequentially considers detailed budget items. Alternatively, if the user is in a hurry, the budgeting unit can prioritize the minimum necessary budget items. This allows the budgeting unit to provide optimal budget priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0122] The budgeting unit may select an optimal budget consideration method by taking into account the user's geographical location information when considering the budget. The budgeting unit may select an optimal budget consideration method based on, for example, the user's current location. Examples of geographical location information include, but are not limited to, GPS data, IP address, and Wi-Fi location information. For example, the budgeting unit may extract a specific pattern from the user's geographical location information and adjust the budget consideration method. The budgeting unit may also analyze the user's geographical location information and adjust the budget consideration algorithm. This allows the budgeting unit to provide an optimal budget consideration method based on the user's geographical location information.

[0123] The budgeting unit may analyze the user's social media activity when considering a budget and suggest budgeting measures. The budgeting unit may suggest optimal budgeting measures, for example, based on the content of the user's social media posts. Social media activity may include, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the budgeting unit may suggest related budgeting measures based on the activity of the user's friends on social media. The budgeting unit may also suggest optimal budgeting measures based on the user's social media check-in information. In this way, the budgeting unit may provide optimal budgeting measures based on the user's social media activity.

[0124] The history unit can estimate the user's emotions and adjust the history consideration method based on the estimated user emotions. For example, if the user is relaxed, the history unit makes suggestions by considering detailed history. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the history unit can quickly consider the history and make suggestions. Furthermore, if the user is excited, the history unit can also make visually appealing suggestions. This allows the history unit to provide an optimal history consideration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, 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.

[0125] When considering the history, the history unit can analyze the user's past travel history and select an optimal history consideration method. The history unit selects the optimal history consideration method, for example, based on the user's past travel history. The travel history includes, for example, past travel destinations, accommodations, transportation modes used, etc., but is not limited to these examples. For example, the history unit extracts a specific pattern from the user's past travel history and adjusts the history consideration method. The history unit can also analyze the user's past travel history and adjust the history consideration algorithm. This allows the history unit to provide an optimal history consideration method based on the user's past travel history.

[0126] The history unit can customize the history consideration means based on the user's current living situation when considering the history. The history unit, for example, selects the optimal history consideration means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the history unit extracts a specific pattern from the user's current living situation and adjusts the history consideration means. The history unit can also analyze the user's current living situation and adjust the history consideration algorithm. This allows the history unit to provide the optimal history consideration means based on the user's current living situation.

[0127] The history unit can estimate the user's emotions and determine the priority of history entries based on the estimated user emotions. For example, if the user is nervous, the history unit prioritizes important history entries. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the history unit sequentially considers detailed history entries. Furthermore, if the user is in a hurry, the history unit can prioritize the minimum number of history entries necessary. This allows the history unit to provide optimal history priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0128] When considering the history, the history unit can select an optimal history consideration method by taking into account the user's geographical location information. The history unit selects the optimal history consideration method, for example, based on the user's current location. Geographical location information includes, but is not limited to, GPS data, IP address, Wi-Fi location information, etc. For example, the history unit extracts a specific pattern from the user's geographical location information and adjusts the history consideration method. The history unit can also analyze the user's geographical location information and adjust the history consideration algorithm. This allows the history unit to provide an optimal history consideration method based on the user's geographical location information.

[0129] When considering the history, the history unit can analyze the user's social media activity and suggest a history consideration method. The history unit can suggest an optimal history consideration method, for example, based on the content of the user's posts on social media. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the history unit can suggest related history consideration methods based on the activities of the user's friends on social media. The history unit can also suggest an optimal history consideration method based on the user's check-in information on social media. This allows the history unit to provide an optimal history consideration method based on the user's social media activity.

[0130] The traffic department can estimate the user's emotions and adjust the method of suggesting transportation options based on the estimated user emotions. For example, if the user is relaxed, the traffic department can suggest detailed transportation options and present multiple options. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the traffic department can suggest fast and efficient transportation options. Furthermore, if the user is excited, the traffic department can suggest visually appealing transportation options. This allows the traffic department to provide an optimal transportation option suggestion method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] When suggesting transportation options, the Department of Transportation and Communications can analyze the user's past transportation usage history to select the optimal suggestion method. For example, the Department of Transportation and Communications selects the optimal suggestion method based on the user's past transportation usage history. The transportation usage history may include, but is not limited to, the transportation modes used in the past, the frequency of use, and the time of use. For example, the Department of Transportation and Communications may extract specific patterns from the user's past transportation usage history and adjust the suggestion method. The Department of Transportation and Communications may also analyze the user's past transportation usage history and adjust the suggestion algorithm. This allows the Department of Transportation and Communications to provide the optimal suggestion method based on the user's past transportation usage history.

[0132] When suggesting transportation options, the Department of Transportation and Communications can customize the suggested transportation options based on the user's current living situation. For example, the Department of Transportation and Communications can select the optimal suggested transportation options based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the Department of Transportation and Communications can extract specific patterns from the user's current living situation and adjust the suggested transportation options. The Department of Transportation and Communications can also analyze the user's current living situation and adjust the suggestion algorithm. In this way, the Department of Transportation and Communications can provide the optimal suggested transportation options based on the user's current living situation.

[0133] The traffic department can estimate the user's emotions and prioritize transportation options based on the estimated user emotions. For example, if the user is nervous, the traffic department can prioritize important transportation options. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the traffic department can sequentially recommend detailed transportation options. Furthermore, if the user is in a hurry, the traffic department can prioritize the minimum necessary transportation options. This allows the traffic department to provide optimal transportation options prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0134] When suggesting transportation options, the Ministry of Transportation and Communications may select an optimal suggestion method by taking into account the user's geographical location information. For example, the Ministry of Transportation and Communications may select an optimal transportation suggestion method based on the user's current location. Examples of geographical location information include, but are not limited to, GPS data, IP address, and Wi-Fi location information. For example, the Ministry of Transportation and Communications may extract specific patterns from the user's geographical location information and adjust the suggestion method. The Ministry of Transportation and Communications may also analyze the user's geographical location information and adjust the suggestion algorithm. This allows the Ministry of Transportation and Communications to provide an optimal transportation suggestion method based on the user's geographical location information.

[0135] When suggesting transportation options, the Department of Transportation may analyze the user's social media activity to suggest transportation options. The Department of Transportation may suggest optimal transportation options based on, for example, the user's social media posts. Social media activities may include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the Department of Transportation may suggest related transportation options based on the activities of the user's friends on social media. The Department of Transportation may also suggest optimal transportation options based on the user's social media check-in information. This allows the Department of Transportation to provide optimal transportation options based on the user's social media activities.

[0136] The accommodation unit can estimate the user's emotions and adjust the accommodation suggestion method based on the estimated user emotions. For example, if the user is relaxed, the accommodation unit suggests detailed accommodations and presents multiple options. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the accommodation unit suggests quick and efficient accommodations. Furthermore, if the user is excited, the accommodation unit can also suggest visually appealing accommodations. This allows the accommodation unit to provide an optimal accommodation suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0137] When suggesting accommodations, the accommodation unit can analyze the user's past accommodation history and select the optimal suggestion method. The accommodation unit selects the optimal suggestion method, for example, based on the user's past accommodation history. The accommodation history includes, for example, but is not limited to, the facilities where the user has stayed in the past, the frequency of stays, and the time of stay. For example, the accommodation unit extracts a specific pattern from the user's past accommodation history and adjusts the suggestion method. The accommodation unit can also analyze the user's past accommodation history and adjust the suggestion algorithm. This allows the accommodation unit to provide the optimal accommodation suggestion method based on the user's past accommodation history.

[0138] When suggesting accommodations, the accommodation unit can customize the accommodation suggestion means based on the user's current living situation. The accommodation unit, for example, selects the optimal accommodation suggestion means based on the user's current living situation. Examples of living situations include, but are not limited to, occupation, family structure, and health status. For example, the accommodation unit extracts a specific pattern from the user's current living situation and adjusts the suggestion means. The accommodation unit can also analyze the user's current living situation and adjust the suggestion algorithm. This allows the accommodation unit to provide the optimal accommodation suggestion means based on the user's current living situation.

[0139] The accommodation unit can estimate the user's emotions and prioritize accommodations based on the estimated user emotions. For example, if the user is nervous, the accommodation unit prioritizes important accommodations. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the accommodation unit sequentially recommends detailed accommodations. Furthermore, if the user is in a hurry, the accommodation unit can prioritize the minimum necessary accommodations. This allows the accommodation unit to provide optimal accommodation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0140] When suggesting accommodations, the accommodation unit can select the optimal suggestion method taking into account the user's geographical location information. The accommodation unit selects the optimal accommodation suggestion method, for example, based on the user's current location. Geographical location information includes, but is not limited to, GPS data, IP address, Wi-Fi location information, etc. For example, the accommodation unit extracts a specific pattern from the user's geographical location information and adjusts the suggestion method. The accommodation unit can also analyze the user's geographical location information and adjust the suggestion algorithm. This allows the accommodation unit to provide the optimal accommodation suggestion method based on the user's geographical location information.

[0141] When suggesting accommodations, the accommodation unit can analyze the user's social media activity and suggest accommodation suggestion means. The accommodation unit can suggest optimal accommodation suggestion means, for example, based on the content of the user's social media posts. Social media activity includes, but is not limited to, the content of posts, the number of likes, the number of followers, etc. For example, the accommodation unit can suggest related accommodation suggestion means based on the activity of the user's friends on social media. The accommodation unit can also suggest optimal accommodation suggestion means based on the user's check-in information on social media. This allows the accommodation unit to provide optimal accommodation suggestion means based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive travel information from a user using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14 and suggests optimal transportation means, accommodations, and schedules. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive travel information from the user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214 and suggests optimal transportation means, accommodations, and schedules. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive travel information from the user using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314 and suggests optimal means of transportation, accommodations, and schedules. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive travel information from the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414 and suggests optimal means of transportation, accommodations, and schedules.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] The analysis unit can take real-time weather information into account when analyzing the user's travel information. For example, the analysis unit can obtain the weather forecast for the destination and prioritize indoor tourist spots if it is raining. If the user prefers outdoor activities, the analysis unit can also suggest a schedule for sunny days. Furthermore, the analysis unit can adjust transportation options based on the weather information and suggest safer transportation methods in bad weather. This allows the analysis unit to make the user's travel plans more flexible and safe.

[0144] The suggestion unit can provide information about local culture and customs for the user's travel plans. For example, the suggestion unit can include cultural etiquette and points of caution for the country or region the user will visit in the suggestion. The suggestion unit can also provide information about local festivals and events, allowing the user to enjoy special experiences during their trip. Furthermore, the suggestion unit can suggest recommended local restaurants and dishes based on the user's food preferences. This allows the suggestion unit to further enrich the user's travel experience.

[0145] The suggestion unit can suggest eco-friendly options for the user's travel plans. For example, the suggestion unit can preferentially suggest environmentally friendly accommodations and means of transportation. The suggestion unit can also suggest nature conservation activities and eco-tours in areas the user will visit. Furthermore, the suggestion unit can calculate the carbon footprint of the user during the user's travel and suggest environmentally friendly options. In this way, the suggestion unit can enable the user to enjoy an environmentally friendly trip.

[0146] The suggestion unit can provide information about health management for the user's travel plans. For example, the suggestion unit can include advice and precautions for the user to maintain their health during their trip in the suggestion. The suggestion unit can also provide information about medical facilities and pharmacies in the area the user will visit, allowing the user to prepare for emergencies. Furthermore, the suggestion unit can suggest appropriate activities and meal options according to the user's health condition. This allows the suggestion unit to enable the user to enjoy their trip with peace of mind.

[0147] The suggestion unit can provide local transportation information in real time for the user's travel plans. For example, the suggestion unit can provide operation status and delay information for the transportation means the user plans to use. The suggestion unit can also suggest the optimal travel route for the user to arrive at their destination and provide advice on how to avoid traffic congestion. Furthermore, the suggestion unit can provide information on public transportation means available to the user locally and how to purchase tickets. This allows the suggestion unit to smooth the user's travel and reduce travel stress.

[0148] The reception unit can estimate a user's emotions and customize the travel information input interface based on the estimated user emotions. For example, the reception unit estimates the user's emotions when inputting travel information. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of travel information. In this way, the reception unit can provide an optimal input interface according to the user's emotions.

[0149] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and generates multiple suggestions. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the analysis unit performs a quick analysis and generates the most efficient suggestions. Furthermore, if the user is excited, the analysis unit can generate visually appealing suggestions. This allows the analysis unit to provide an optimal analysis algorithm according to the user's emotions.

[0150] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit provides detailed suggestions and presents multiple options. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the suggestion unit can provide quick and concise suggestions. Furthermore, if the user is excited, the suggestion unit can also provide visually appealing suggestions. This allows the suggestion unit to provide an optimal way in which suggestions are expressed according to the user's emotion.

[0151] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. The emotion can be estimated using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the suggestion unit to provide an optimal length of suggestion according to the user's emotion.

[0152] The consideration unit can estimate the user's emotions and adjust a preference consideration method based on the estimated user emotions. For example, when the user is relaxed, the consideration unit makes a suggestion by taking into account detailed preferences. Emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. For example, when the user is in a hurry, the consideration unit can quickly consider the preferences and make a suggestion. Furthermore, when the user is excited, the consideration unit can also make a visually appealing suggestion. This allows the consideration unit to provide an optimal preference consideration method according to the user's emotions.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The reception unit receives travel information from the user. The travel information includes, for example, the destination, budget, itinerary, number of participants, etc. The reception unit provides an interface for the user to input information such as the travel destination, itinerary, and events to attend. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI performs analysis to suggest optimal transportation, accommodation, and schedules based on the destination, schedule, and event details entered by the user. Step 3: The suggestion unit proposes optimal means of transportation, accommodations, and schedules based on the information analyzed by the analysis unit. Suggestions are made based on the format of the proposal and the type of items to be proposed (means of transportation, accommodations, schedule, etc.). For example, the suggestion unit proposes means of transportation such as airplanes and bullet trains, accommodations such as hotels and guesthouses, and schedules that coincide with the start time of the live performance.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[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 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.

[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 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.

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

[0173] 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.

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

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0206] 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.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0213] 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."

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives travel information from a user; an analysis unit that analyzes the information received by the reception unit; a suggestion unit that suggests transportation means, accommodations, and schedules based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. A consideration unit that makes suggestions based on user preferences 2. The system of claim 1.

3. It has a budget department that makes proposals based on the user's budget.

2. The system of claim 1.

4. It has a history unit that makes suggestions based on the user's past travel history.

2. The system of claim 1.

5. Equipped with a transportation department that proposes specific transportation methods 2. The system of claim 1.

6. Have an accommodation department that proposes specific accommodations 2. The system of claim 1.

7. The reception unit To estimate a user's emotion and customize a travel information input interface based on the estimated user's emotion.

2. The system of claim 1.

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

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