System

Abstract

An object of a system according to an embodiment is to propose an individual travel plan on the basis of a user's preference or a past travel experience and to quickly cope with an unexpected situation during a travel.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, an association unit, and a learning unit. The collection unit collects information on at least one of a user's preference, past travel experience, favorite activity, and tourist site. The analysis unit analyzes the information collected by the collection unit. The proposing section proposes a travel plan based on the information analyzed by the analyzing section. The responder collects local information on an unexpected situation or an urgent request during the travel and proposes a countermeasure. The learning unit learns through the travel experience of the user and reflects it in the next travel plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to propose individual travel plans based on a user's preferences and past travel experiences, and of making it difficult to respond quickly to unexpected situations during travel.

[0005] The system according to the embodiment aims to propose an individual travel plan based on the user's preferences and past travel experiences, and to quickly respond to unexpected situations during travel. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a response unit, and a learning unit. The collection unit collects at least one piece of information from the user's preferences or past travel experiences, favorite activities, and tourist destinations. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. The response unit collects local information and proposes countermeasures for unexpected situations or sudden requests during travel. The learning unit learns from the user's travel experiences and reflects this in the next travel plan. [Effects of the Invention]

[0007] The system according to the embodiment proposes a personalized travel plan based on the user's preferences and past travel experiences, and can quickly respond to unexpected situations during travel. [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 assistant system according to an embodiment of the present invention understands a user's preferences and needs and proposes an optimal, personalized travel plan based on their past travel experiences, favorite activities, and tourist destinations. The travel assistant system collects information about the user's preferences, past travel experiences, favorite activities, and tourist destinations, and uses AI to analyze the information and propose an optimal travel plan. It also responds to unexpected situations and sudden requests during the trip by utilizing local information and AI functions. Furthermore, the system learns from the user's travel experiences and incorporates this information into its next travel plan, enabling a more enjoyable trip. For example, the travel assistant system collects information about the user's previously visited tourist destinations, activities they have participated in, and preferred meals and accommodations. Then, AI analyzes the collected information and proposes an optimal travel plan for the user. For example, it proposes similar tourist destinations and activities based on the user's previously visited tourist destinations and activities they have participated in. It also takes into account the user's budget, schedule, and special requests. Furthermore, the AI ​​collects local information and proposes optimal countermeasures for unexpected situations and sudden requests during the trip. For example, the AI ​​collects local information and proposes optimal countermeasures for unexpected situations such as sudden changes in weather or transportation delays. In addition, the AI ​​can respond to sudden requests based on local information. It also learns from the user's travel experience and reflects this in its next travel plan. For example, it can record the user's favorite tourist spots and activities and reflect them in its next travel plan. This allows the travel assistant system to understand the user's preferences and needs and not only propose personalized travel plans, but also respond to unexpected situations and sudden requests during the trip and reflect these in its next travel plan, making for a more enjoyable trip. In this way, the travel assistant system learns from the user's travel experience and reflects these in its next travel plan, making for a more enjoyable trip. For example, by proposing the next travel plan based on tourist spots the user has visited and activities they have participated in in the past, it can provide the user with a more attractive trip. Furthermore, by quickly responding to unexpected situations and sudden requests during the trip, it can improve the user's travel experience.

[0029] A travel assistant system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a response unit, and a learning unit. The collection unit collects at least one of information on a user's preferences or past travel experiences, favorite activities, and tourist destinations. The collection unit collects information such as tourist destinations visited by the user in the past, activities participated in by the user, and favorite meals and accommodations. The collection unit can also collect related information based on the user's preferences and needs. For example, the collection unit can collect information on related tourist destinations and activities based on tourist destinations visited by the user in the past and activities participated in by the user. The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, propose an optimal travel plan to the user based on the collected information. The analysis unit can analyze the collected information using AI and propose an optimal travel plan based on the user's preferences and needs. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. The proposal unit proposes an optimal travel plan based on information such as the user's preferences, past travel experiences, favorite activities, and tourist destinations. The proposal unit can also take into account the user's budget, schedule, and special requests. The response unit collects local information and proposes countermeasures in response to unexpected situations or sudden requests during the trip. The response unit collects local information and proposes optimal countermeasures in response to unexpected situations, such as sudden changes in weather or transportation delays. The response unit can use AI to collect local information and propose optimal countermeasures. The learning unit learns through the user's travel experience and reflects it in the next travel plan. The learning unit, for example, records the user's favorite tourist spots and activities and reflects them in the next travel plan. The learning unit can use AI to learn from the user's travel experience and reflect it in the next travel plan. As a result, the travel assistant system according to the embodiment can propose an individual travel plan based on the user's preferences and needs and respond to unexpected situations during the trip.

[0030] The collection unit can collect at least one piece of information about tourist spots visited by the user in the past, activities participated in by the user, and preferred meals or accommodations. The collection unit, for example, collects information about tourist spots visited by the user in the past. For example, the collection unit can collect information about cities, natural landscapes, historical buildings, etc. visited by the user in the past. The collection unit can also collect information about activities the user participated in in the past. For example, the collection unit can collect information about sports, sightseeing, shopping, etc. participated in by the user in the past. The collection unit can also collect information about the user's preferred meals and accommodations. For example, the collection unit can collect information about the user's preferred meals, such as Japanese food, Western food, or vegetarian food. The collection unit can also collect information about the user's preferred accommodations, such as hotels, guesthouses, and resorts. By collecting information based on the user's past travel experiences, a more personalized travel plan can be proposed. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input information about tourist spots visited by the user in the past into AI, which can then collect information about related tourist spots.

[0031] The analysis unit can propose a travel plan to the user based on the collected information. The analysis unit, for example, proposes an optimal travel plan to the user based on the collected information. For example, the analysis unit can propose similar tourist spots and activities based on tourist spots the user has previously visited or activities they have participated in. The analysis unit can also propose a travel plan that takes into account the user's budget, schedule, and special requests based on the user's preferences and needs. For example, the analysis unit can propose an optimal travel plan based on the user's budget. The analysis unit can also adjust the travel plan to match the user's schedule. Furthermore, the analysis unit can customize the travel plan based on the user's special requests. In this way, by analyzing the collected information, the optimal travel plan can be proposed to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected information into AI, which then proposes an optimal travel plan.

[0032] The suggestion unit can suggest a travel plan based on the user's budget, schedule, and special requests. The suggestion unit, for example, suggests a travel plan based on the user's budget. For example, the suggestion unit can suggest an optimal travel plan based on the user's budget. The suggestion unit can also suggest a travel plan based on the user's schedule. For example, the suggestion unit can adjust the travel plan to match the user's schedule. The suggestion unit can also suggest a travel plan based on the user's special requests. For example, the suggestion unit can customize the travel plan according to the user's special requests. This makes it possible to suggest a more appropriate travel plan by taking the user's budget, schedule, and special requests into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's budget, schedule, and special requests into AI, which can then suggest an optimal travel plan.

[0033] The response unit can collect local information and propose countermeasures for unexpected situations such as sudden changes in weather and transportation delays. The response unit, for example, collects local information for sudden changes in weather and proposes countermeasures. For example, the response unit can collect local weather information for sudden changes in weather and propose optimal countermeasures. The response unit can also collect local information for transportation delays and propose countermeasures. For example, the response unit can collect local traffic information and propose optimal countermeasures for transportation delays. Furthermore, the response unit can collect local information for other unexpected situations and propose countermeasures. For example, the response unit can collect local accommodation information for problems with accommodation and propose optimal countermeasures. This allows for rapid response to unexpected situations. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input local information into AI, which then proposes optimal countermeasures.

[0034] The learning unit can learn from the user's travel experience and reflect that in the next travel plan. The learning unit, for example, learns from the user's travel experience and reflects that in the next travel plan. For example, the learning unit can record the user's favorite tourist spots and activities and reflect them in the next travel plan. The learning unit can also customize the next travel plan based on the user's travel experience. For example, the learning unit can suggest the next travel plan based on tourist spots the user has visited and activities they have participated in in the past. Furthermore, the learning unit can improve the next travel plan based on user feedback. For example, the learning unit can collect user feedback and optimize the next travel plan based on that feedback. In this way, by learning the user's travel experience, the next travel plan becomes more appropriate. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the user's travel experience data into AI, which can then optimize the next travel plan.

[0035] The collection unit can analyze the user's past travel history and select an information collection method. The collection unit, for example, analyzes the user's past travel history and selects the optimal information collection method. For example, the collection unit can prioritize collecting information on tourist spots the user has visited in the past. The collection unit can also collect information on similar activities based on information on activities the user has participated in in the past. For example, the collection unit can collect information on related activities based on information on activities the user has participated in in the past. The collection unit can also collect related information based on information on meals and accommodations the user has previously preferred. For example, the collection unit can collect related information based on information on meals and accommodations the user has previously preferred. This allows for analyzing the user's past travel history to select a more appropriate information collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past travel history data into AI, which can select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current interests. For example, the collection unit can prioritize collecting information on tourist destinations in which the user is currently interested. For example, the collection unit can prioritize collecting information on tourist destinations in which the user is currently interested. The collection unit can also prioritize collecting information on activities in which the user is currently interested. For example, the collection unit can prioritize collecting information on activities in which the user is currently interested. Furthermore, the collection unit can also prioritize collecting information on restaurants and accommodations in which the user is currently interested. For example, the collection unit can prioritize collecting information on restaurants and accommodations in which the user is currently interested. This allows more relevant information to be collected by filtering information based on the user's current interests. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current interests into AI, which can then filter the information.

[0037] When collecting information, the collection unit can select a collection means according to the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can collect information using text analysis technology. For example, when the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect information using image recognition technology. For example, when the user uses image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into AI, which can select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. The collection unit, for example, prioritizes collecting information about tourist attractions near the user's current location. For example, the collection unit can prioritize collecting information about tourist attractions near the user's current location. The collection unit can also prioritize collecting information about activities near the user's current location. For example, the collection unit can prioritize collecting information about activities near the user's current location. The collection unit can also prioritize collecting information about restaurants and accommodations near the user's current location. For example, the collection unit can prioritize collecting information about restaurants and accommodations near the user's current location. This allows highly relevant information to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can then preferentially collect highly relevant information.

[0039] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information on places where the user has checked in on social media. For example, the collection unit can collect information on places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect information on related tourist spots and activities. For example, the collection unit can analyze the content of the user's social media posts and collect information on related tourist spots and activities. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit can collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can collect related information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can prioritize collecting information sources that the user has previously rated highly. For example, the collection unit can prioritize collecting information sources that the user has previously rated highly. The collection unit can also collect information while avoiding information sources that the user has previously rated poorly. For example, the collection unit can collect information while avoiding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. For example, the collection unit can optimize the collection method based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI, which can customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance. For example, the analysis unit can perform a detailed analysis of information with high importance. Furthermore, the analysis unit can perform a simplified analysis of information with low importance. For example, the analysis unit can perform a simplified analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit can determine the priority of the analysis according to the importance of the information. As a result, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to AI, and the AI ​​can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a tourist destination-specific analysis algorithm to tourist destination information. For example, the analysis unit can apply a tourist destination-specific analysis algorithm to tourist destination information. The analysis unit can also apply an activity-specific analysis algorithm to activity information. For example, the analysis unit can apply an activity-specific analysis algorithm to activity information. The analysis unit can also apply specific analysis algorithms to meal and accommodation information. For example, the analysis unit can apply specific analysis algorithms to meal and accommodation information. This allows for applying different analysis algorithms depending on the category of information, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information category data into AI, and the AI ​​can apply different analysis algorithms.

[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, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. For example, the analysis unit can extract patterns for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit can prioritize analysis of the latest information. For example, the analysis unit can prioritize analysis of the latest information. Furthermore, the analysis unit can lower the priority of analysis of older information. For example, the analysis unit can lower the priority of analysis of older information. Furthermore, the analysis unit can adjust the analysis schedule according to the time of submission of information. For example, the analysis unit can adjust the analysis schedule according to the time of submission of information. In this way, by determining the priority of analysis based on the time of submission of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information into AI, and the AI ​​can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize analysis of highly relevant information. For example, the analysis unit can prioritize analysis of highly relevant information. Furthermore, the analysis unit can lower the priority of analysis of less relevant information. For example, the analysis unit can lower the priority of analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can adjust the order of analysis based on the relevance of the information. As a result, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the information into AI, and the AI ​​can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terms in the analysis.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel plan when making a suggestion. For example, the suggestion unit can make a detailed suggestion for a highly important travel plan. For example, the suggestion unit can make a detailed suggestion for a highly important travel plan. Furthermore, the suggestion unit can make a simplified suggestion for a less important travel plan. For example, the suggestion unit can make a simplified suggestion for a less important travel plan. Furthermore, the suggestion unit can determine the priority of the suggestion based on the importance of the travel plan. For example, the suggestion unit can determine the priority of the suggestion based on the importance of the travel plan. As a result, by adjusting the level of detail of the suggestion based on the importance of the travel plan, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input importance data of the travel plan into AI, which can adjust the level of detail of the suggestion.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the travel plan. For example, the suggestion unit can apply a suggestion algorithm specific to a tourist destination when suggesting tourist destinations. For example, the suggestion unit can apply a suggestion algorithm specific to a tourist destination when suggesting tourist destinations. Furthermore, the suggestion unit can apply a suggestion algorithm specific to an activity when suggesting activities. For example, the suggestion unit can apply a suggestion algorithm specific to an activity when suggesting activities. Furthermore, the suggestion unit can apply specific suggestion algorithms for each of meals and accommodations. For example, the suggestion unit can apply specific suggestion algorithms for each of meals and accommodations. In this way, by applying different suggestion algorithms depending on the category of the travel plan, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input category data of the travel plan into AI, and the AI ​​can apply different suggestion algorithms.

[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, optimizes the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract a pattern for improving the accuracy of the suggestion from the user's past suggestion results. For example, the suggestion unit can extract a pattern for improving the accuracy of the suggestion from the user's past suggestion results. Furthermore, the suggestion unit can adjust the level of detail of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs the user's past suggestion result data into AI, which can improve the accuracy of the suggestion.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the travel plan. For example, the suggestion unit can prioritize the most recent travel plan. For example, the suggestion unit can prioritize the most recent travel plan. Furthermore, the suggestion unit can lower the priority of older travel plans when making a proposal. For example, the suggestion unit can lower the priority of older travel plans when making a proposal. Furthermore, the suggestion unit can adjust the schedule of the proposal based on the time of submission of the travel plan. For example, the suggestion unit can adjust the schedule of the proposal based on the time of submission of the travel plan. In this way, more appropriate proposals can be provided by determining the priority of the proposal based on the time of submission of the travel plan. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the time of submission of the travel plan into AI, which can then determine the priority of the proposal.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the travel plans. For example, the suggestion unit can prioritize proposals for highly relevant travel plans. For example, the suggestion unit can prioritize proposals for highly relevant travel plans. Furthermore, the suggestion unit can lower the priority of less relevant travel plans when making a proposal. For example, the suggestion unit can lower the priority of less relevant travel plans when making a proposal. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the travel plans. For example, the suggestion unit can adjust the order of proposals based on the relevance of the travel plans. As a result, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of travel plans into AI, which can adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a proposal that avoids technical terminology. For example, if the user does not have technical expertise, the suggestion unit can provide a proposal that avoids technical terminology. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. As a result, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terminology in the proposal.

[0053] During response, the response unit can adjust the level of detail of the countermeasures based on the importance of the unexpected event. For example, the response unit can provide detailed countermeasures for highly important events. For example, the response unit can provide detailed countermeasures for highly important events. Furthermore, the response unit can provide simplified countermeasures for less important events. For example, the response unit can provide simplified countermeasures for less important events. Furthermore, the response unit can determine the priority of the countermeasures based on the importance of the event. For example, the response unit can determine the priority of the countermeasures based on the importance of the event. As a result, by adjusting the level of detail of the countermeasures based on the importance of the unexpected event, more appropriate countermeasures can be provided. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit can input incident importance data into AI, which can adjust the level of detail of the countermeasures.

[0054] The response unit can apply different response algorithms depending on the category of the incident when responding. For example, the response unit can apply a weather-specific response algorithm to a sudden change in weather. For example, the response unit can apply a weather-specific response algorithm to a sudden change in weather. The response unit can also apply a transportation-specific response algorithm to a transportation delay. For example, the response unit can apply a transportation-specific response algorithm to a transportation delay. The response unit can also apply an accommodation-specific response algorithm to a problem at an accommodation. For example, the response unit can apply an accommodation-specific response algorithm to a problem at an accommodation. In this way, by applying different response algorithms depending on the category of the incident, more appropriate responses can be provided. Some or all of the above-mentioned processing in the response unit may be performed using, or without, AI. For example, the response unit can input incident category data into AI, and the AI ​​can apply different response algorithms.

[0055] When responding, the response unit can improve the accuracy of the response measures by referring to the user's past response results. The response unit, for example, optimizes a response algorithm based on the user's past response results. For example, the response unit can optimize the response algorithm based on the user's past response results. The response unit can also extract patterns for improving the accuracy of the response measures from the user's past response results. For example, the response unit can extract patterns for improving the accuracy of the response measures from the user's past response results. Furthermore, the response unit can adjust the level of detail of the response measures by referring to the user's past response results. For example, the response unit can adjust the level of detail of the response measures by referring to the user's past response results. In this way, the accuracy of the response measures can be improved by referring to the user's past response results. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past response result data into AI, which can improve the accuracy of the response measures.

[0056] When responding to an event, the response unit can determine the priority of countermeasures based on the time of occurrence of the event. For example, the response unit can prioritize providing countermeasures for the most recent event. For example, the response unit can prioritize providing countermeasures for the most recent event. Furthermore, the response unit can lower the priority of countermeasures for older events. For example, the response unit can lower the priority of countermeasures for older events. Furthermore, the response unit can adjust the schedule of countermeasures depending on the time of event occurrence. For example, the response unit can adjust the schedule of countermeasures depending on the time of event occurrence. In this way, by determining the priority of countermeasures based on the time of event occurrence, more appropriate countermeasures can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input data on the time of event occurrence into AI, and the AI ​​can determine the priority of countermeasures.

[0057] The response unit can adjust the order of countermeasures based on the relevance of events during response. For example, the response unit can prioritize providing countermeasures for highly relevant events. For example, the response unit can prioritize providing countermeasures for highly relevant events. Furthermore, the response unit can lower the priority of providing countermeasures for less relevant events. For example, the response unit can lower the priority of providing countermeasures for less relevant events. Furthermore, the response unit can adjust the order of countermeasures based on the relevance of events. For example, the response unit can adjust the order of countermeasures based on the relevance of events. In this way, by adjusting the order of countermeasures based on the relevance of events, more appropriate countermeasures can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input event relevance data into AI, and the AI ​​can adjust the order of countermeasures.

[0058] When responding, the response unit can adjust the use of technical terminology in the countermeasure according to the user's level of expertise. For example, if the user has technical expertise, the response unit can provide a countermeasure that uses a lot of technical terminology. For example, if the user has technical expertise, the response unit can provide a countermeasure that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the response unit can provide a countermeasure that avoids technical terminology. For example, if the user does not have technical expertise, the response unit can provide a countermeasure that avoids technical terminology. Furthermore, the response unit can adjust the way the countermeasure is expressed according to the user's level of expertise. For example, the response unit can adjust the way the countermeasure is expressed according to the user's level of expertise. This allows for adjusting the use of technical terminology in the countermeasure according to the user's level of expertise, thereby providing a more appropriate countermeasure. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without AI. For example, the response unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terminology in the countermeasure.

[0059] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. For example, the learning unit can optimize the learning algorithm based on past learning data. The learning unit can also extract patterns for improving the accuracy of learning from past learning data. For example, the learning unit can extract patterns for improving the accuracy of learning from past learning data. Furthermore, the learning unit can adjust the level of detail of learning by referring to past learning data. For example, the learning unit can adjust the level of detail of learning by referring to past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI, which can optimize the learning algorithm.

[0060] During learning, the learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. For example, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. For example, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. In this way, by analyzing fluctuations in the user's travel history, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's travel history data into AI, and the AI ​​can adjust the update frequency of the learning data.

[0061] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. The learning unit, for example, optimizes the learning algorithm based on user feedback. For example, the learning unit can optimize the learning algorithm based on user feedback. The learning unit can also extract patterns for improving learning accuracy from user feedback. For example, the learning unit can extract patterns for improving learning accuracy from user feedback. Furthermore, the learning unit can adjust the level of detail of learning by referring to user feedback. For example, the learning unit can adjust the level of detail of learning by referring to user feedback. In this way, by reflecting user feedback, the learning algorithm can be appropriately adjusted and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into AI, which can adjust the learning algorithm.

[0062] During learning, the learning unit can weight the learning data based on the time of submission of the travel history. For example, the learning unit can assign a higher weight to the most recent travel history. For example, the learning unit can assign a higher weight to the most recent travel history. The learning unit can also assign a lower weight to older travel history. For example, the learning unit can assign a lower weight to older travel history. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time of submission of the travel history. For example, the learning unit can adjust the weighting of the learning data depending on the time of submission of the travel history. This enables more appropriate learning by weighting the learning data based on the time of submission of the travel history. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time of submission of the travel history to AI, and the AI ​​can weight the learning data.

[0063] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. The learning unit, for example, optimizes the learning algorithm based on user feedback. For example, the learning unit can optimize the learning algorithm based on user feedback. The learning unit can also extract patterns for improving learning accuracy from user feedback. For example, the learning unit can extract patterns for improving learning accuracy from user feedback. Furthermore, the learning unit can adjust the level of detail of learning by referring to user feedback. For example, the learning unit can adjust the level of detail of learning by referring to user feedback. In this way, by reflecting user feedback, the learning algorithm can be appropriately adjusted and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into AI, which can adjust the learning algorithm.

[0064] During learning, the learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. For example, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. For example, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. In this way, by analyzing fluctuations in the user's travel history, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's travel history data into AI, and the AI ​​can adjust the update frequency of the learning data.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can introduce new perspectives into the travel plan suggestions based on the user's past travel history. For example, the analysis unit can suggest unvisited tourist spots near tourist spots the user has visited in the past. The analysis unit can also suggest new activities related to activities the user has participated in in the past that were particularly highly rated. Furthermore, the analysis unit can suggest new similar options based on information about meals and accommodations that the user has previously preferred. This makes it possible to suggest travel plans from a new perspective by utilizing the user's past travel history. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past travel history data into AI, which then suggests travel plans incorporating new perspectives.

[0067] The suggestion unit can customize travel plan suggestions based on the user's current interests. For example, it can prioritize suggesting information about theme parks in which the user is currently interested. It can also prioritize suggesting information about cultural events and festivals in which the user is currently interested. It can also prioritize suggesting information about dining and accommodations in which the user is currently interested. This allows the travel plan to be customized based on the user's current interests, thereby providing more relevant suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's current interest data into AI, which can then customize the travel plan.

[0068] The response unit can optimize response measures for unexpected situations based on the user's past response history. For example, the response unit can suggest the optimal response measures based on how the user responded to sudden changes in weather in the past. The response unit can also suggest the optimal response measures based on how the user responded to transportation delays in the past. Furthermore, the response unit can suggest the optimal response measures based on how the user responded to accommodation troubles in the past. In this way, the response measures for unexpected situations can be optimized by utilizing the user's past response history. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past response history data into AI, which can then optimize the response measures.

[0069] The learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history changes frequently, the update frequency of the learning data can be increased. Also, if the user's travel history is stable, the update frequency of the learning data can be decreased. Furthermore, the update schedule of the learning data can be adjusted according to fluctuations in the user's travel history. In this way, by analyzing fluctuations in the user's travel history, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's travel history data into AI, which can adjust the update frequency of the learning data.

[0070] The collection unit can analyze the user's social media activities and collect related information. For example, it can collect information on places where the user has checked in on social media. It can also analyze the content of the user's social media posts and collect information on related tourist spots and activities. It can also refer to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which then collects the related information.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects at least one of the user's preferences or past travel experiences, favorite activities, and tourist destinations. For example, information such as tourist destinations visited by the user, activities participated in by the user, and favorite meals and accommodations can be collected. Related information based on the user's preferences and needs can also be collected. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses AI to analyze the collected information and propose the best travel plan for the user. Step 3: The suggestion unit proposes travel plans based on the information analyzed by the analysis unit. For example, it proposes optimal travel plans based on information such as the user's preferences, past travel experiences, favorite activities, and tourist destinations, taking into account budget, schedule, and special requests. Step 4: The response department collects local information and proposes countermeasures for unexpected situations or sudden requests during the trip. For example, AI is used to collect local information and propose optimal countermeasures for unexpected situations such as sudden changes in weather or transportation delays. Step 5: The learning unit learns from the user's travel experience and reflects it in the next travel plan. For example, AI is used to record the user's favorite tourist spots and activities and reflect them in the next travel plan.

[0073] (Example 2) A travel assistant system according to an embodiment of the present invention understands a user's preferences and needs and proposes an optimal, personalized travel plan based on their past travel experiences, favorite activities, and tourist destinations. The travel assistant system collects information about the user's preferences, past travel experiences, favorite activities, and tourist destinations, and uses AI to analyze the information and propose an optimal travel plan. It also responds to unexpected situations and sudden requests during the trip by utilizing local information and AI functions. Furthermore, the system learns from the user's travel experiences and incorporates this information into its next travel plan, enabling a more enjoyable trip. For example, the travel assistant system collects information about the user's previously visited tourist destinations, activities they have participated in, and preferred meals and accommodations. Then, AI analyzes the collected information and proposes an optimal travel plan for the user. For example, it proposes similar tourist destinations and activities based on the user's previously visited tourist destinations and activities they have participated in. It also takes into account the user's budget, schedule, and special requests. Furthermore, the AI ​​collects local information and proposes optimal countermeasures for unexpected situations and sudden requests during the trip. For example, the AI ​​collects local information and proposes optimal countermeasures for unexpected situations such as sudden changes in weather or transportation delays. In addition, the AI ​​can respond to sudden requests based on local information. It also learns from the user's travel experience and reflects this in its next travel plan. For example, it can record the user's favorite tourist spots and activities and reflect them in its next travel plan. This allows the travel assistant system to understand the user's preferences and needs and not only propose personalized travel plans, but also respond to unexpected situations and sudden requests during the trip and reflect these in its next travel plan, making for a more enjoyable trip. In this way, the travel assistant system learns from the user's travel experience and reflects these in its next travel plan, making for a more enjoyable trip. For example, by proposing the next travel plan based on tourist spots the user has visited and activities they have participated in in the past, it can provide the user with a more attractive trip. Furthermore, by quickly responding to unexpected situations and sudden requests during the trip, it can improve the user's travel experience.

[0074] A travel assistant system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a response unit, and a learning unit. The collection unit collects at least one of information on a user's preferences or past travel experiences, favorite activities, and tourist destinations. The collection unit collects information such as tourist destinations visited by the user in the past, activities participated in by the user, and favorite meals and accommodations. The collection unit can also collect related information based on the user's preferences and needs. For example, the collection unit can collect information on related tourist destinations and activities based on tourist destinations visited by the user in the past and activities participated in by the user. The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, propose an optimal travel plan to the user based on the collected information. The analysis unit can analyze the collected information using AI and propose an optimal travel plan based on the user's preferences and needs. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. The proposal unit proposes an optimal travel plan based on information such as the user's preferences, past travel experiences, favorite activities, and tourist destinations. The proposal unit can also take into account the user's budget, schedule, and special requests. The response unit collects local information and proposes countermeasures in response to unexpected situations or sudden requests during the trip. The response unit collects local information and proposes optimal countermeasures in response to unexpected situations, such as sudden changes in weather or transportation delays. The response unit can use AI to collect local information and propose optimal countermeasures. The learning unit learns through the user's travel experience and reflects it in the next travel plan. The learning unit, for example, records the user's favorite tourist spots and activities and reflects them in the next travel plan. The learning unit can use AI to learn from the user's travel experience and reflect it in the next travel plan. As a result, the travel assistant system according to the embodiment can propose an individual travel plan based on the user's preferences and needs and respond to unexpected situations during the trip.

[0075] The collection unit can collect at least one piece of information about tourist spots visited by the user in the past, activities participated in by the user, and preferred meals or accommodations. The collection unit, for example, collects information about tourist spots visited by the user in the past. For example, the collection unit can collect information about cities, natural landscapes, historical buildings, etc. visited by the user in the past. The collection unit can also collect information about activities the user participated in in the past. For example, the collection unit can collect information about sports, sightseeing, shopping, etc. participated in by the user in the past. The collection unit can also collect information about the user's preferred meals and accommodations. For example, the collection unit can collect information about the user's preferred meals, such as Japanese food, Western food, or vegetarian food. The collection unit can also collect information about the user's preferred accommodations, such as hotels, guesthouses, and resorts. By collecting information based on the user's past travel experiences, a more personalized travel plan can be proposed. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input information about tourist spots visited by the user in the past into AI, which can then collect information about related tourist spots.

[0076] The analysis unit can propose a travel plan to the user based on the collected information. The analysis unit, for example, proposes an optimal travel plan to the user based on the collected information. For example, the analysis unit can propose similar tourist spots and activities based on tourist spots the user has previously visited or activities they have participated in. The analysis unit can also propose a travel plan that takes into account the user's budget, schedule, and special requests based on the user's preferences and needs. For example, the analysis unit can propose an optimal travel plan based on the user's budget. The analysis unit can also adjust the travel plan to match the user's schedule. Furthermore, the analysis unit can customize the travel plan based on the user's special requests. In this way, by analyzing the collected information, the optimal travel plan can be proposed to the user. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected information into AI, which then proposes an optimal travel plan.

[0077] The suggestion unit can suggest a travel plan based on the user's budget, schedule, and special requests. The suggestion unit, for example, suggests a travel plan based on the user's budget. For example, the suggestion unit can suggest an optimal travel plan based on the user's budget. The suggestion unit can also suggest a travel plan based on the user's schedule. For example, the suggestion unit can adjust the travel plan to match the user's schedule. The suggestion unit can also suggest a travel plan based on the user's special requests. For example, the suggestion unit can customize the travel plan according to the user's special requests. This makes it possible to suggest a more appropriate travel plan by taking the user's budget, schedule, and special requests into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's budget, schedule, and special requests into AI, which can then suggest an optimal travel plan.

[0078] The response unit can collect local information and propose countermeasures for unexpected situations such as sudden changes in weather and transportation delays. The response unit, for example, collects local information for sudden changes in weather and proposes countermeasures. For example, the response unit can collect local weather information for sudden changes in weather and propose optimal countermeasures. The response unit can also collect local information for transportation delays and propose countermeasures. For example, the response unit can collect local traffic information and propose optimal countermeasures for transportation delays. Furthermore, the response unit can collect local information for other unexpected situations and propose countermeasures. For example, the response unit can collect local accommodation information for problems with accommodation and propose optimal countermeasures. This allows for rapid response to unexpected situations. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input local information into AI, which then proposes optimal countermeasures.

[0079] The learning unit can learn from the user's travel experience and reflect that in the next travel plan. The learning unit, for example, learns from the user's travel experience and reflects that in the next travel plan. For example, the learning unit can record the user's favorite tourist spots and activities and reflect them in the next travel plan. The learning unit can also customize the next travel plan based on the user's travel experience. For example, the learning unit can suggest the next travel plan based on tourist spots the user has visited and activities they have participated in in the past. Furthermore, the learning unit can improve the next travel plan based on user feedback. For example, the learning unit can collect user feedback and optimize the next travel plan based on that feedback. In this way, by learning the user's travel experience, the next travel plan becomes more appropriate. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the user's travel experience data into AI, which can then optimize the next travel plan.

[0080] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect information during a time when the user is able to relax. For example, the collection unit can estimate the user's emotions and collect information during a time when the user is able to relax. Furthermore, if the user is excited, the collection unit can immediately start collecting information and provide the information in real time. For example, the collection unit can estimate the user's emotions and provide the information in real time when the user is excited. Furthermore, if the user is tired, the collection unit can collect information after the user has rested so that the user can receive the information in a refreshed state. For example, the collection unit can estimate the user's emotions and collect information after the user has rested when the user is tired. This allows more appropriate information to be collected by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized 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. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI, which may then adjust the timing of information collection.

[0081] The collection unit can analyze the user's past travel history and select an information collection method. The collection unit, for example, analyzes the user's past travel history and selects the optimal information collection method. For example, the collection unit can prioritize collecting information on tourist spots the user has visited in the past. The collection unit can also collect information on similar activities based on information on activities the user has participated in in the past. For example, the collection unit can collect information on related activities based on information on activities the user has participated in in the past. The collection unit can also collect related information based on information on meals and accommodations the user has previously preferred. For example, the collection unit can collect related information based on information on meals and accommodations the user has previously preferred. This allows for analyzing the user's past travel history to select a more appropriate information collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past travel history data into AI, which can select the optimal information collection method.

[0082] When collecting information, the collection unit can filter the information based on the user's current interests. For example, the collection unit can prioritize collecting information on tourist destinations in which the user is currently interested. For example, the collection unit can prioritize collecting information on tourist destinations in which the user is currently interested. The collection unit can also prioritize collecting information on activities in which the user is currently interested. For example, the collection unit can prioritize collecting information on activities in which the user is currently interested. Furthermore, the collection unit can also prioritize collecting information on restaurants and accommodations in which the user is currently interested. For example, the collection unit can prioritize collecting information on restaurants and accommodations in which the user is currently interested. This allows more relevant information to be collected by filtering information based on the user's current interests. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current interests into AI, which can then filter the information.

[0083] When collecting information, the collection unit can select a collection means according to the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can collect information using text analysis technology. For example, when the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect information using image recognition technology. For example, when the user uses image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into AI, which can select the optimal collection means.

[0084] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed information. For example, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that emphasizes the main points. For example, when the user is in a hurry, the collection unit can prioritize collecting information that emphasizes the main points. Furthermore, when the user is excited, the collection unit can prioritize collecting visually appealing information. For example, when the user is excited, the collection unit can prioritize collecting visually appealing information. This allows more appropriate information to be collected by determining the priority of information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI, which can then determine the priority of the information.

[0085] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. The collection unit, for example, prioritizes collecting information about tourist attractions near the user's current location. For example, the collection unit can prioritize collecting information about tourist attractions near the user's current location. The collection unit can also prioritize collecting information about activities near the user's current location. For example, the collection unit can prioritize collecting information about activities near the user's current location. The collection unit can also prioritize collecting information about restaurants and accommodations near the user's current location. For example, the collection unit can prioritize collecting information about restaurants and accommodations near the user's current location. This allows highly relevant information to be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can then preferentially collect highly relevant information.

[0086] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information on places where the user has checked in on social media. For example, the collection unit can collect information on places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect information on related tourist spots and activities. For example, the collection unit can analyze the content of the user's social media posts and collect information on related tourist spots and activities. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit can collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can collect related information.

[0087] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can prioritize collecting information sources that the user has previously rated highly. For example, the collection unit can prioritize collecting information sources that the user has previously rated highly. The collection unit can also collect information while avoiding information sources that the user has previously rated poorly. For example, the collection unit can collect information while avoiding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. For example, the collection unit can optimize the collection method based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI, which can customize the collection method.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide a more concise analysis result. For example, when the user is in a hurry, the analysis unit can provide a more concise analysis result. Furthermore, when the user is excited, the analysis unit can provide a more visually appealing analysis result. For example, when the user is excited, the analysis unit can provide a more visually appealing analysis result. Thus, by adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the AI, which can then adjust how the analysis is presented.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance. For example, the analysis unit can perform a detailed analysis of information with high importance. Furthermore, the analysis unit can perform a simplified analysis of information with low importance. For example, the analysis unit can perform a simplified analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit can determine the priority of the analysis according to the importance of the information. As a result, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to AI, and the AI ​​can adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a tourist destination-specific analysis algorithm to tourist destination information. For example, the analysis unit can apply a tourist destination-specific analysis algorithm to tourist destination information. The analysis unit can also apply an activity-specific analysis algorithm to activity information. For example, the analysis unit can apply an activity-specific analysis algorithm to activity information. The analysis unit can also apply specific analysis algorithms to meal and accommodation information. For example, the analysis unit can apply specific analysis algorithms to meal and accommodation information. This allows for applying different analysis algorithms depending on the category of information, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information category data into AI, and the AI ​​can apply different analysis algorithms.

[0091] 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, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also extract patterns for improving the accuracy of the analysis from the user's past analysis results. For example, the analysis unit can extract patterns for improving the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. This allows for adjusting the length of the analysis based on the user's emotions to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the AI, which can then adjust the length of the analysis.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit can prioritize analysis of the latest information. For example, the analysis unit can prioritize analysis of the latest information. Furthermore, the analysis unit can lower the priority of analysis of older information. For example, the analysis unit can lower the priority of analysis of older information. Furthermore, the analysis unit can adjust the analysis schedule according to the time of submission of information. For example, the analysis unit can adjust the analysis schedule according to the time of submission of information. In this way, by determining the priority of analysis based on the time of submission of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information into AI, and the AI ​​can determine the priority of analysis.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize analysis of highly relevant information. For example, the analysis unit can prioritize analysis of highly relevant information. Furthermore, the analysis unit can lower the priority of analysis of less relevant information. For example, the analysis unit can lower the priority of analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can adjust the order of analysis based on the relevance of the information. As a result, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the information into AI, and the AI ​​can adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terms in the analysis.

[0096] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide suggestions that are concise when the user is in a hurry. For example, the suggestion unit can provide suggestions that are concise when the user is in a hurry. Furthermore, the suggestion unit can provide visually appealing suggestions when the user is excited. For example, the suggestion unit can provide visually appealing suggestions when the user is excited. This allows for adjusting the way the suggestions are expressed based on the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the AI, which can then adjust the way the suggestion is expressed.

[0097] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel plan when making a suggestion. For example, the suggestion unit can make a detailed suggestion for a highly important travel plan. For example, the suggestion unit can make a detailed suggestion for a highly important travel plan. Furthermore, the suggestion unit can make a simplified suggestion for a less important travel plan. For example, the suggestion unit can make a simplified suggestion for a less important travel plan. Furthermore, the suggestion unit can determine the priority of the suggestion based on the importance of the travel plan. For example, the suggestion unit can determine the priority of the suggestion based on the importance of the travel plan. As a result, by adjusting the level of detail of the suggestion based on the importance of the travel plan, more appropriate suggestions can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input importance data of the travel plan into AI, which can adjust the level of detail of the suggestion.

[0098] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the travel plan. For example, the suggestion unit can apply a suggestion algorithm specific to a tourist destination when suggesting tourist destinations. For example, the suggestion unit can apply a suggestion algorithm specific to a tourist destination when suggesting tourist destinations. Furthermore, the suggestion unit can apply a suggestion algorithm specific to an activity when suggesting activities. For example, the suggestion unit can apply a suggestion algorithm specific to an activity when suggesting activities. Furthermore, the suggestion unit can apply specific suggestion algorithms for each of meals and accommodations. For example, the suggestion unit can apply specific suggestion algorithms for each of meals and accommodations. In this way, by applying different suggestion algorithms depending on the category of the travel plan, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input category data of the travel plan into AI, and the AI ​​can apply different suggestion algorithms.

[0099] 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, optimizes the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit can optimize the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract a pattern for improving the accuracy of the suggestion from the user's past suggestion results. For example, the suggestion unit can extract a pattern for improving the accuracy of the suggestion from the user's past suggestion results. Furthermore, the suggestion unit can adjust the level of detail of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs the user's past suggestion result data into AI, which can improve the accuracy of the suggestion.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, the suggestion unit can provide detailed suggestions if the user is relaxed. Furthermore, the suggestion unit can also provide visually appealing suggestions if the user is excited. For example, the suggestion unit can provide visually appealing suggestions if the user is excited. This allows for adjusting the length of the suggestions based on the user's emotions to provide more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can adjust the length of the suggestions.

[0101] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the travel plan. For example, the suggestion unit can prioritize the most recent travel plan. For example, the suggestion unit can prioritize the most recent travel plan. Furthermore, the suggestion unit can lower the priority of older travel plans when making a proposal. For example, the suggestion unit can lower the priority of older travel plans when making a proposal. Furthermore, the suggestion unit can adjust the schedule of the proposal based on the time of submission of the travel plan. For example, the suggestion unit can adjust the schedule of the proposal based on the time of submission of the travel plan. In this way, more appropriate proposals can be provided by determining the priority of the proposal based on the time of submission of the travel plan. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the time of submission of the travel plan into AI, which can then determine the priority of the proposal.

[0102] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the travel plans. For example, the suggestion unit can prioritize proposals for highly relevant travel plans. For example, the suggestion unit can prioritize proposals for highly relevant travel plans. Furthermore, the suggestion unit can lower the priority of less relevant travel plans when making a proposal. For example, the suggestion unit can lower the priority of less relevant travel plans when making a proposal. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the travel plans. For example, the suggestion unit can adjust the order of proposals based on the relevance of the travel plans. As a result, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of travel plans into AI, which can adjust the order of proposals.

[0103] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. For example, if the user has technical expertise, the suggestion unit can provide a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a proposal that avoids technical terminology. For example, if the user does not have technical expertise, the suggestion unit can provide a proposal that avoids technical terminology. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. As a result, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate proposals can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terminology in the proposal.

[0104] The response unit can estimate the user's emotions and adjust the way in which the countermeasure is expressed based on the estimated user's emotions. For example, if the user is nervous, the response unit can provide the countermeasure in a calm expression. For example, if the user is nervous, the response unit can provide the countermeasure in a calm expression. Furthermore, if the user is relaxed, the response unit can provide a detailed countermeasure. For example, if the user is relaxed, the response unit can provide a detailed countermeasure. Furthermore, if the user is in a hurry, the response unit can provide a countermeasure that focuses on the main points. For example, if the user is in a hurry, the response unit can provide a countermeasure that focuses on the main points. In this way, by adjusting the way in which the countermeasure is expressed based on the user's emotions, more appropriate countermeasures can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the user's emotional data into the AI, which can then adjust the way the response is expressed.

[0105] During response, the response unit can adjust the level of detail of the countermeasures based on the importance of the unexpected event. For example, the response unit can provide detailed countermeasures for highly important events. For example, the response unit can provide detailed countermeasures for highly important events. Furthermore, the response unit can provide simplified countermeasures for less important events. For example, the response unit can provide simplified countermeasures for less important events. Furthermore, the response unit can determine the priority of the countermeasures based on the importance of the event. For example, the response unit can determine the priority of the countermeasures based on the importance of the event. As a result, by adjusting the level of detail of the countermeasures based on the importance of the unexpected event, more appropriate countermeasures can be provided. Some or all of the above-described processing in the response unit may be performed using, or without, AI. For example, the response unit can input incident importance data into AI, which can adjust the level of detail of the countermeasures.

[0106] The response unit can apply different response algorithms depending on the category of the incident when responding. For example, the response unit can apply a weather-specific response algorithm to a sudden change in weather. For example, the response unit can apply a weather-specific response algorithm to a sudden change in weather. The response unit can also apply a transportation-specific response algorithm to a transportation delay. For example, the response unit can apply a transportation-specific response algorithm to a transportation delay. The response unit can also apply an accommodation-specific response algorithm to a problem at an accommodation. For example, the response unit can apply an accommodation-specific response algorithm to a problem at an accommodation. In this way, by applying different response algorithms depending on the category of the incident, more appropriate responses can be provided. Some or all of the above-mentioned processing in the response unit may be performed using, or without, AI. For example, the response unit can input incident category data into AI, and the AI ​​can apply different response algorithms.

[0107] When responding, the response unit can improve the accuracy of the response measures by referring to the user's past response results. The response unit, for example, optimizes a response algorithm based on the user's past response results. For example, the response unit can optimize the response algorithm based on the user's past response results. The response unit can also extract patterns for improving the accuracy of the response measures from the user's past response results. For example, the response unit can extract patterns for improving the accuracy of the response measures from the user's past response results. Furthermore, the response unit can adjust the level of detail of the response measures by referring to the user's past response results. For example, the response unit can adjust the level of detail of the response measures by referring to the user's past response results. In this way, the accuracy of the response measures can be improved by referring to the user's past response results. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past response result data into AI, which can improve the accuracy of the response measures.

[0108] The response unit can estimate the user's emotion and adjust the length of the countermeasure based on the estimated user's emotion. For example, if the user is in a hurry, the response unit can provide a short and to-the-point countermeasure. For example, if the user is in a hurry, the response unit can provide a short and to-the-point countermeasure. The response unit can also provide a detailed countermeasure if the user is relaxed. For example, if the user is relaxed, the response unit can provide a detailed countermeasure. Furthermore, if the user is excited, the response unit can provide a visually appealing countermeasure. For example, if the user is excited, the response unit can provide a visually appealing countermeasure. This allows for adjusting the length of the countermeasure based on the user's emotion to provide a more appropriate countermeasure. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the user's emotional data into the AI, which can then adjust the length of the response measures.

[0109] When responding to an event, the response unit can determine the priority of countermeasures based on the time of occurrence of the event. For example, the response unit can prioritize providing countermeasures for the most recent event. For example, the response unit can prioritize providing countermeasures for the most recent event. Furthermore, the response unit can lower the priority of countermeasures for older events. For example, the response unit can lower the priority of countermeasures for older events. Furthermore, the response unit can adjust the schedule of countermeasures depending on the time of event occurrence. For example, the response unit can adjust the schedule of countermeasures depending on the time of event occurrence. In this way, by determining the priority of countermeasures based on the time of event occurrence, more appropriate countermeasures can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input data on the time of event occurrence into AI, and the AI ​​can determine the priority of countermeasures.

[0110] The response unit can adjust the order of countermeasures based on the relevance of events during response. For example, the response unit can prioritize providing countermeasures for highly relevant events. For example, the response unit can prioritize providing countermeasures for highly relevant events. Furthermore, the response unit can lower the priority of providing countermeasures for less relevant events. For example, the response unit can lower the priority of providing countermeasures for less relevant events. Furthermore, the response unit can adjust the order of countermeasures based on the relevance of events. For example, the response unit can adjust the order of countermeasures based on the relevance of events. In this way, by adjusting the order of countermeasures based on the relevance of events, more appropriate countermeasures can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input event relevance data into AI, and the AI ​​can adjust the order of countermeasures.

[0111] When responding, the response unit can adjust the use of technical terminology in the countermeasure according to the user's level of expertise. For example, if the user has technical expertise, the response unit can provide a countermeasure that uses a lot of technical terminology. For example, if the user has technical expertise, the response unit can provide a countermeasure that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the response unit can provide a countermeasure that avoids technical terminology. For example, if the user does not have technical expertise, the response unit can provide a countermeasure that avoids technical terminology. Furthermore, the response unit can adjust the way the countermeasure is expressed according to the user's level of expertise. For example, the response unit can adjust the way the countermeasure is expressed according to the user's level of expertise. This allows for adjusting the use of technical terminology in the countermeasure according to the user's level of expertise, thereby providing a more appropriate countermeasure. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without AI. For example, the response unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terminology in the countermeasure.

[0112] The learning unit can estimate the user's emotions and select training data based on the estimated user's emotions. For example, when the user is relaxed, the learning unit selects detailed training data. For example, when the user is relaxed, the learning unit can select detailed training data. Furthermore, when the user is in a hurry, the learning unit can select training data that emphasizes the main points. For example, when the user is in a hurry, the learning unit can select training data that emphasizes the main points. Furthermore, when the user is excited, the learning unit can select visually appealing training data. For example, when the user is excited, the learning unit can select visually appealing training data. This enables more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input user emotional data into the AI, which can then select the learning data.

[0113] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. For example, the learning unit can optimize the learning algorithm based on past learning data. The learning unit can also extract patterns for improving the accuracy of learning from past learning data. For example, the learning unit can extract patterns for improving the accuracy of learning from past learning data. Furthermore, the learning unit can adjust the level of detail of learning by referring to past learning data. For example, the learning unit can adjust the level of detail of learning by referring to past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI, which can optimize the learning algorithm.

[0114] During learning, the learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. For example, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. For example, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. In this way, by analyzing fluctuations in the user's travel history, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's travel history data into AI, and the AI ​​can adjust the update frequency of the learning data.

[0115] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. The learning unit, for example, optimizes the learning algorithm based on user feedback. For example, the learning unit can optimize the learning algorithm based on user feedback. The learning unit can also extract patterns for improving learning accuracy from user feedback. For example, the learning unit can extract patterns for improving learning accuracy from user feedback. Furthermore, the learning unit can adjust the level of detail of learning by referring to user feedback. For example, the learning unit can adjust the level of detail of learning by referring to user feedback. In this way, by reflecting user feedback, the learning algorithm can be appropriately adjusted and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into AI, which can adjust the learning algorithm.

[0116] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can perform learning more frequently when the user is relaxed. For example, the learning unit can perform learning more frequently when the user is relaxed. The learning unit can also reduce the frequency of learning when the user is in a hurry. For example, the learning unit can reduce the frequency of learning when the user is in a hurry. Furthermore, the learning unit can select visually appealing learning data when the user is excited. For example, the learning unit can select visually appealing learning data when the user is excited. This enables more appropriate learning by adjusting the frequency of learning based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, or may be performed without an AI. For example, the learning unit can input the user's emotion data into an AI, which can adjust the frequency of learning.

[0117] During learning, the learning unit can weight the learning data based on the time of submission of the travel history. For example, the learning unit can assign a higher weight to the most recent travel history. For example, the learning unit can assign a higher weight to the most recent travel history. The learning unit can also assign a lower weight to older travel history. For example, the learning unit can assign a lower weight to older travel history. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time of submission of the travel history. For example, the learning unit can adjust the weighting of the learning data depending on the time of submission of the travel history. This enables more appropriate learning by weighting the learning data based on the time of submission of the travel history. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time of submission of the travel history to AI, and the AI ​​can weight the learning data.

[0118] During learning, the learning unit can adjust the learning algorithm by reflecting user feedback. The learning unit, for example, optimizes the learning algorithm based on user feedback. For example, the learning unit can optimize the learning algorithm based on user feedback. The learning unit can also extract patterns for improving learning accuracy from user feedback. For example, the learning unit can extract patterns for improving learning accuracy from user feedback. Furthermore, the learning unit can adjust the level of detail of learning by referring to user feedback. For example, the learning unit can adjust the level of detail of learning by referring to user feedback. In this way, by reflecting user feedback, the learning algorithm can be appropriately adjusted and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into AI, which can adjust the learning algorithm.

[0119] During learning, the learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. For example, if the user's travel history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. For example, if the user's travel history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. For example, the learning unit can adjust the update schedule of the learning data in accordance with fluctuations in the user's travel history. In this way, by analyzing fluctuations in the user's travel history, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's travel history data into AI, and the AI ​​can adjust the update frequency of the learning data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, response unit, and learning unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the user's preferences and past travel experiences using the camera 42 and microphone 38B 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 collected information to suggest an optimal travel plan to the user. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and presents the travel plan to the user based on the analysis results. The response unit collects local information via the communication I / F 44 of the smart device 14 and suggests optimal responses via the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's travel experiences and reflects them in the next travel plan. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, response unit, and learning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the user's preferences and past travel experiences using the camera 42 and 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 collected information to suggest an optimal travel plan to the user. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and presents the travel plan to the user based on the analysis results. The response unit collects local information via the communication I / F 44 of the smart glasses 214 and suggests optimal responses via the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's travel experiences and reflects them in the next travel plan. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, response unit, and learning unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect the user's preferences and past travel experiences using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to suggest an optimal travel plan to the user. The suggestion unit is realized, for example, by the control unit 46A of the headset terminal 314 and presents the travel plan to the user based on the analysis results. The response unit collects local information via the communication I / F 44 of the headset terminal 314 and suggests optimal responses via the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's travel experiences and reflects them in the next travel plan. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, response unit, and learning unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the user's preferences and past travel experiences using the camera 42 and 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 collected information to suggest an optimal travel plan to the user. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and presents the travel plan to the user based on the analysis results. The response unit collects local information via the communication I / F 44 of the robot 414 and suggests optimal responses via the specific processing unit 290 of the data processing device 12. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's travel experiences and reflects them in the next travel plan.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of information with high importance. Furthermore, if the user is relaxed, the analysis unit can perform detailed analysis. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the analysis priority based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then determine the analysis priority.

[0122] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user emotions. For example, if the user is relaxed, a detailed travel plan can be suggested. If the user is in a hurry, a concise suggestion that focuses on the main points can be made. Furthermore, if the user is excited, a visually appealing suggestion can be provided. By adjusting the content of the suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without using an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can then adjust the content of the suggestions.

[0123] The response unit can estimate the user's emotions and adjust the level of detail of the countermeasures based on the estimated user emotions. For example, if the user is nervous, concise and to the point countermeasures can be provided. If the user is relaxed, detailed countermeasures can be provided. Furthermore, if the user is excited, visually appealing countermeasures can be provided. By adjusting the level of detail of the countermeasures based on the user's emotions, more appropriate countermeasures can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the response unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the response unit can input the user's emotion data into an AI, which can then adjust the level of detail of the countermeasures.

[0124] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, detailed training data can be selected. If the user is in a hurry, training data that focuses on the main points can be selected. Furthermore, if the user is excited, visually appealing training data can be selected. This allows for more appropriate learning by selecting training data based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Examples of generative AI include text generation AI (e.g., LLM) and multimodal generation AI, but are not limited to these examples. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into an AI, which then selects the training data.

[0125] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, information collection can be performed during a time when the user is able to relax. Furthermore, if the user is excited, information collection can be started immediately and information can be provided in real time. Furthermore, if the user is tired, information collection can be performed after the user has rested, allowing the user to receive information in a refreshed state. This allows for more appropriate information to be collected by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which can then adjust the timing of information collection.

[0126] The analysis unit can introduce new perspectives into the travel plan suggestions based on the user's past travel history. For example, the analysis unit can suggest unvisited tourist spots near tourist spots the user has visited in the past. The analysis unit can also suggest new activities related to activities the user has participated in in the past that were particularly highly rated. Furthermore, the analysis unit can suggest new similar options based on information about meals and accommodations that the user has previously preferred. This makes it possible to suggest travel plans from a new perspective by utilizing the user's past travel history. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past travel history data into AI, which then suggests travel plans incorporating new perspectives.

[0127] The suggestion unit can customize travel plan suggestions based on the user's current interests. For example, it can prioritize suggesting information about theme parks in which the user is currently interested. It can also prioritize suggesting information about cultural events and festivals in which the user is currently interested. It can also prioritize suggesting information about dining and accommodations in which the user is currently interested. This allows the travel plan to be customized based on the user's current interests, thereby providing more relevant suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's current interest data into AI, which can then customize the travel plan.

[0128] The response unit can optimize response measures for unexpected situations based on the user's past response history. For example, the response unit can suggest the optimal response measures based on how the user responded to sudden changes in weather in the past. The response unit can also suggest the optimal response measures based on how the user responded to transportation delays in the past. Furthermore, the response unit can suggest the optimal response measures based on how the user responded to accommodation troubles in the past. In this way, the response measures for unexpected situations can be optimized by utilizing the user's past response history. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past response history data into AI, which can then optimize the response measures.

[0129] The learning unit can analyze fluctuations in the user's travel history and adjust the update frequency of the learning data. For example, if the user's travel history changes frequently, the update frequency of the learning data can be increased. Also, if the user's travel history is stable, the update frequency of the learning data can be decreased. Furthermore, the update schedule of the learning data can be adjusted according to fluctuations in the user's travel history. In this way, by analyzing fluctuations in the user's travel history, the update frequency of the learning data can be appropriately adjusted. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's travel history data into AI, which can adjust the update frequency of the learning data.

[0130] The collection unit can analyze the user's social media activities and collect related information. For example, it can collect information on places where the user has checked in on social media. It can also analyze the content of the user's social media posts and collect information on related tourist spots and activities. It can also refer to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI, which then collects the related information.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects at least one of the user's preferences or past travel experiences, favorite activities, and tourist destinations. For example, information such as tourist destinations visited by the user, activities participated in by the user, and favorite meals and accommodations can be collected. Related information based on the user's preferences and needs can also be collected. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses AI to analyze the collected information and propose the best travel plan for the user. Step 3: The suggestion unit proposes travel plans based on the information analyzed by the analysis unit. For example, it proposes optimal travel plans based on information such as the user's preferences, past travel experiences, favorite activities, and tourist destinations, taking into account budget, schedule, and special requests. Step 4: The response department collects local information and proposes countermeasures for unexpected situations or sudden requests during the trip. For example, AI is used to collect local information and propose optimal countermeasures for unexpected situations such as sudden changes in weather or transportation delays. Step 5: The learning unit learns from the user's travel experience and reflects it in the next travel plan. For example, AI is used to record the user's favorite tourist spots and activities and reflect them in the next travel plan.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 collection unit for collecting at least one of information on the user's preferences or past travel experiences, favorite activities, and tourist destinations; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that proposes a travel plan based on the information analyzed by the analysis unit; The response department gathers local information and proposes countermeasures for unexpected situations and sudden requests during travel. A learning unit that learns from the user's travel experience and reflects the learning in the user's next travel plan. A system characterized by:

2. The collecting unit Collect information about at least one of the following: tourist attractions visited by the user, activities participated in, and preferred dining or accommodations.

2. The system of claim 1.

3. The analysis unit Based on the collected information, the app suggests travel plans to users.

2. The system of claim 1.

4. The proposal unit Based on your budget, schedule and special requests 2. The system of claim 1.

5. The corresponding part is Collecting local information and proposing countermeasures for unexpected situations such as sudden changes in weather and transportation delays 2. The system of claim 1.

6. The learning unit Learn from your travel experiences and plan your next trip 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past travel history and select the method of information collection 2. The system of claim 1.

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