system

Abstract

The system according to this embodiment aims to propose the optimal local gourmet travel plan based on the user's food preferences and travel style. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a reservation unit. The collection unit collects information on the user's food preferences and travel style. The analysis unit analyzes the information collected by the collection unit and analyzes the user's preferences. The generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. The reservation unit reserves the travel plan generated by the generation unit.

Description

Technical Field

[0002] , ,

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to propose an optimal local gourmet travel plan based on the user's food preferences and travel styles, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal local gourmet travel plan based on the user's food preferences and travel styles.

Means for Solving the Problems

[0006] It should be noted that there are some tags in the original text that seem to be repeated or have an unclear usage pattern. I have translated them as they are while trying to maintain the overall context. If there are specific requirements or corrections regarding these tags, please let me know.The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a reservation unit. The collection unit collects information about the user's food preferences and travel style. The analysis unit analyzes the information collected by the collection unit to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. The reservation unit reserves the travel plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal local gourmet travel plan based on the user's food preferences and travel style. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The gourmet travel agent system according to an embodiment of the present invention is a system that analyzes a user's food preferences and travel style and proposes an optimal regional gourmet travel plan. This gourmet travel agent system collects information on the user's food preferences and travel style, and the AI ​​analyzes the user's preferences to generate an optimal regional gourmet travel plan. This plan includes visits to undiscovered regional gourmet spots, accommodations, and transportation, and the user can book the proposed plan all at once. For example, the gourmet travel agent system collects information such as the type of cuisine the user likes, past travel destinations, and travel frequency. This information is input into the AI, which uses machine learning to analyze the user's preferences and generate an optimal travel plan. For example, if the user likes Japanese food, the AI ​​will propose undiscovered regional Japanese gourmet spots. Also, if the user prefers a travel style that enjoys nature, the AI ​​will propose accommodations and transportation in areas rich in nature. The generated travel plan includes visits to undiscovered regional gourmet spots, accommodations, and transportation, and includes a list of gourmet spots the user should visit, accommodation booking information, and details of transportation. This allows the user to review and book the travel plan all at once. Furthermore, the AI ​​updates local information in real time, providing the latest information on gourmet spots and accommodations. This allows users to always plan their trips based on the most up-to-date information. This system significantly reduces the time and effort required for travel planning. It also contributes to the revitalization of local economies by discovering undiscovered gourmet spots in rural areas. For example, when users visit the suggested gourmet spots, the use of local restaurants and accommodations increases, stimulating the local economy. This gourmet travel agent system is extremely useful for individuals who want to enjoy gourmet travel, travelers who want to discover hidden gems in rural areas, and users who seek efficient travel planning. For example, if a busy business person wants to plan a trip efficiently in a short period of time, they can use this gourmet travel agent system to create an optimal travel plan in a short amount of time. In addition, by continuously improving the service using user feedback, more personalized travel suggestions become possible.For example, based on feedback provided by users after their trip, the AI ​​further optimizes their next travel plan. In this way, by utilizing the gourmet travel agent system, users can discover new local cuisine and contribute to the revitalization of local economies. Embark on a journey to discover new cuisine and rediscover the charm of local areas. Why not start an adventure to encounter unknown flavors with a gourmet travel agent? The gourmet travel agent system proposes the optimal local gourmet travel plan based on the user's food preferences and travel style, and handles the booking all in one place.

[0029] The gourmet travel agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a reservation unit. The collection unit collects information about the user's food preferences and travel style. For example, the collection unit collects information such as the type of cuisine the user likes, past travel destinations, and travel frequency. For example, if the user likes Japanese food, the collection unit can collect information about Japanese food. The collection unit can also collect information about past travel destinations the user has visited. Furthermore, the collection unit can collect information about the user's travel frequency. The analysis unit analyzes the information collected by the collection unit to analyze the user's preferences. For example, the analysis unit uses machine learning to analyze the user's preferences. For example, the analysis unit can use deep learning to analyze the user's preferences with high accuracy. The analysis unit can also use support vector machines to analyze the user's preferences. Furthermore, the analysis unit can also use clustering to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. For example, the generation unit generates a travel plan that includes visits to undiscovered local gourmet spots, accommodations, and transportation. The generation unit can, for example, generate a list of gourmet spots that the user should visit. It can also generate accommodation reservation information. Furthermore, it can generate details of transportation. The reservation unit makes reservations for the travel plans generated by the generation unit. For example, the reservation unit can make reservations for the proposed travel plans all at once. For example, the reservation unit can make reservations by integrating multiple reservation sites. It can also make reservations using a dedicated reservation system. Furthermore, the reservation unit can customize the reservation method according to the user's preferences. As a result, the gourmet travel agent system according to this embodiment can propose the optimal local gourmet travel plan based on the user's food preferences and travel style, and handle the entire reservation process.

[0030] The data collection unit gathers information about users' food preferences and travel styles. Specifically, it collects detailed information such as the types of cuisine users prefer, past travel destinations, travel frequency, travel purpose, budget, and whether or not they travel with companions. For example, if a user prefers Japanese food, the unit collects information such as their favorite Japanese dishes (sushi, tempura, udon, etc.), famous Japanese restaurants they have visited in the past, and their participation history in Japanese food-related events and festivals. Regarding past travel destinations, the unit collects detailed information such as the time of visit, length of stay, and the tourist attractions and gourmet spots visited. Furthermore, regarding the user's travel frequency, it collects information such as the number of trips per year, the season of travel, and the length of trips. This information is stored in a database as a user profile and used for analysis by the analysis unit. The data collection unit can collect information not only from information entered by users but also from external sources such as social media, blogs, and review sites. For example, it collects photos of meals and travel impressions posted by users on social media, as well as ratings and comments on review sites, to more accurately understand the user's preferences and travel style. Furthermore, the data collection unit can analyze the user's behavioral history and search history to identify places and dishes that the user is interested in but has not yet visited. This allows the data collection unit to comprehensively collect diverse user information, enabling highly accurate analysis by the analysis unit.

[0031] The analysis unit analyzes the information collected by the data collection unit to analyze user preferences. The analysis utilizes advanced technologies such as machine learning, deep learning, support vector machines, and clustering. Specifically, machine learning algorithms are used to learn the user's past behavior and preference patterns to predict future preferences. Deep learning allows for highly accurate analysis of user preferences and identification of subtle differences. For example, even if a user likes Japanese food, the analysis can determine in detail whether they particularly like sushi, tempura, or Japanese food from a specific region. Support vector machines are used to classify user preferences and identify user groups with similar preferences. Clustering divides user preferences into multiple categories and proposes the optimal travel plan for each category. The analysis unit combines these technologies to provide foundational data for generating optimal travel plans by comprehensively analyzing user preferences. Furthermore, the analysis unit can analyze collected information in real time, enabling rapid responses to changes in user preferences and trends. For example, if a user develops an interest in a new type of cuisine, this information is immediately reflected, and a travel plan based on the latest preferences is proposed. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and seasonal fluctuations, which can then be used to suggest future travel plans. This allows the analysis unit to analyze user preferences with high accuracy and consistently provide the optimal travel plan.

[0032] The generation unit generates the optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. Specifically, it generates a travel plan that includes visits to undiscovered local gourmet spots, accommodations, and transportation, tailored to the user's preferences. For example, if the user prefers Japanese food, it will suggest a plan to visit famous Japanese restaurants and hidden local gems. It can also generate a plan where the user selects a region they have never visited before and enjoys the local specialties and seasonal cuisine. The generation unit also pays attention to accommodation selection, suggesting accommodations that match the user's budget and preferences. For example, it selects accommodations that meet the user's needs, such as luxury ryokans (traditional Japanese inns), hot spring resorts, or guesthouses that utilize local characteristics. Furthermore, it also suggests the most suitable mode of transportation, taking into consideration the user's convenience. For example, it selects transportation methods that match the user's travel style, such as trains, buses, or rental cars. The generation unit combines these elements to generate the optimal travel plan for the user. In addition, the generation unit makes improvements to enhance the accuracy and satisfaction of the travel plan based on user feedback. For example, by analyzing the feedback provided by users after their trip and reflecting it in the next travel plan, it can provide a more satisfying plan. This allows the generation unit to create optimal travel plans tailored to the user's preferences and needs, providing them with an attractive travel experience.

[0033] The booking department books the travel plans generated by the generation department. Specifically, it can book proposed travel plans all at once. For example, it can integrate multiple booking sites to book accommodations, transportation, and restaurants all at once. The booking department can also customize booking methods to suit user preferences using a dedicated booking system. For example, if a user prefers a particular booking site, it will prioritize using that site for bookings. Furthermore, the booking department can propose the optimal booking plan according to the user's budget and schedule. For example, it can utilize early booking discounts and special campaigns to provide the user with the most advantageous plan. In addition, the booking department can flexibly handle changes and cancellations of bookings. For example, if a user's plans change, it will quickly modify the booking details and propose the most suitable plan again. The booking department also makes improvements to enhance the usability of the booking system and the quality of service based on user feedback. As a result, the booking department can provide users with a convenient and reliable booking service and smoothly support them from planning to executing their trip.

[0034] The gourmet travel agent system further includes an update unit that updates local information in real time. The update unit can update local information in real time. For example, the update unit can update information in seconds. It can also update information in minutes. Furthermore, the update unit can update information in hours. This allows users to create travel plans based on the latest information by updating local information in real time. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input local information into a generative AI, which can then analyze and update the information.

[0035] The gourmet travel agent system further includes a feedback collection unit that collects user feedback. The feedback collection unit can collect user feedback. For example, the feedback collection unit can collect satisfaction ratings. The feedback collection unit can also collect comments. Furthermore, the feedback collection unit can also collect suggestions for improvement. This allows the service to be continuously improved by collecting user feedback. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input user feedback into a generative AI, which can then analyze and collect the feedback.

[0036] The data collection unit can collect information such as the user's favorite types of food, past travel destinations, and travel frequency. For example, the data collection unit can collect information on the user's favorite types of food, such as Japanese, Western, or Chinese cuisine. The data collection unit can also collect information on past travel destinations, such as the name of the destination, the time of visit, and the length of stay. Furthermore, the data collection unit can collect information on the user's travel frequency, such as whether they travel several times a year or once a month. By collecting detailed information on the user's food preferences and travel style, a more accurate travel plan can be generated. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user information into a generative AI, which can then analyze and collect the information.

[0037] The analysis unit can analyze user preferences using machine learning. The analysis unit can analyze user preferences with high accuracy using, for example, deep learning. The analysis unit can also analyze user preferences using support vector machines. Furthermore, the analysis unit can analyze user preferences using clustering. This allows for high-accuracy analysis of user preferences using machine learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user information into a generative AI, which can then analyze the information to determine user preferences.

[0038] The generation unit can generate travel plans that include visits to undiscovered local gourmet spots, accommodations, and transportation. For example, the generation unit can generate a travel plan that includes visits to undiscovered local gourmet spots. For example, the generation unit can generate a list of gourmet spots that the user should visit. The generation unit can also generate accommodation reservation information. Furthermore, the generation unit can generate details of transportation. This allows the user to be offered a new experience by generating travel plans that include undiscovered local gourmet spots. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user information into a generation AI, and the generation AI can analyze the information and generate a travel plan.

[0039] The booking department can book proposed travel plans all at once. The booking department can, for example, integrate multiple booking sites to make reservations. The booking department can also, for example, integrate multiple booking sites to make reservations. Furthermore, the booking department can make reservations using a dedicated booking system. In addition, the booking department can customize the booking method according to the user's preferences. This significantly reduces the user's effort by allowing them to book proposed travel plans all at once. Some or all of the above processing in the booking department may be performed using, for example, a generative AI, or not using a generative AI. For example, the booking department can input user information into a generative AI, which can then analyze the information and make reservations.

[0040] The improvement unit can improve the service based on the feedback collected by the feedback collection unit. For example, the improvement unit can improve the user interface. The improvement unit can also add new features. Furthermore, the improvement unit can make improvements to enhance the service performance. This makes it possible to provide more personalized travel suggestions by improving the service based on the collected feedback. Some or all of the above processing in the improvement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the improvement unit can input user feedback into a generative AI, which can then analyze the information to improve the service.

[0041] The data collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the data collection unit can generate relevant questions based on data of destinations the user has visited in the past. The data collection unit can also collect information at an appropriate time, taking into account the user's past travel frequency. Furthermore, the data collection unit can analyze the user's past travel style and suggest the optimal information collection method. This allows for the selection of the optimal information collection method by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the user's past travel history data into a generating AI, which can then analyze the information and select the optimal information collection method.

[0042] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can provide a form for the user to input their current living situation and filter the information. The data collection unit can also prioritize displaying relevant questions based on the user's areas of interest. Furthermore, the data collection unit can select an appropriate data collection method according to the user's living situation. This allows for the collection of more relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's living situation data into a generative AI, which can then analyze and filter the information.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information about nearby restaurants based on the user's current location. The data collection unit can also prioritize the collection of relevant information by considering the user's past visited locations. Furthermore, the data collection unit can select the optimal information collection method based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then analyze the information and prioritize the collection of highly relevant information.

[0044] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the user's social media posts and collect information about their food preferences. The data collection unit can also collect relevant information based on the user's social media followers. Furthermore, the data collection unit can analyze the user's social media activity history and select the optimal data collection method. This allows for the effective collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI, which can then analyze the information and collect relevant information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's preferences during the analysis. For example, if the user has a strong interest in a particular dish, the analysis unit can provide detailed analysis results about that dish. The analysis unit can also provide detailed analysis results about a travel style if the user has a strong interest in that style. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the importance of the user's preferences. This allows the analysis unit to provide detailed information that is important to the user by adjusting the level of detail of the analysis according to the importance of the user's preferences. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user preference importance data into a generative AI, and the generative AI can analyze the information and adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the user's travel style during analysis. For example, if the user prefers an active travel style, the analysis unit can apply an analysis algorithm related to activities. The analysis unit can also apply a relaxation analysis algorithm if the user prefers a relaxed travel style. Furthermore, the analysis unit can select the optimal analysis algorithm according to the user's travel style. This allows for the provision of more appropriate analysis results by applying the optimal analysis algorithm according to the user's travel style. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's travel style data into a generative AI, which can analyze the information and apply the optimal analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the user's past travel history during the analysis process. For example, the analysis unit can prioritize providing relevant analysis results based on data of destinations the user has visited in the past. The analysis unit can also provide appropriate analysis results by considering the user's past travel frequency. Furthermore, the analysis unit can analyze the user's past travel style and provide optimal analysis results. This allows the analysis unit to prioritize providing highly relevant analysis results by determining the priority of analysis based on the user's past travel history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past travel history data into a generating AI, which can then analyze the information and determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, if a user has a strong interest in a particular dish, the analysis unit can prioritize providing analysis results related to that dish. The analysis unit can also prioritize providing analysis results related to a travel style if the user has a strong interest in that style. Furthermore, the analysis unit can adjust the order of analysis results according to user relevance. This allows the system to prioritize providing information that is important to the user by adjusting the order of analysis according to user relevance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user relevance data into a generative AI, which can then analyze the information and adjust the order of analysis.

[0049] The generation unit can adjust the level of detail in the plan based on the importance of the user's preferences during generation. For example, if the user has a strong interest in a particular dish, the generation unit can provide a detailed travel plan related to that dish. The generation unit can also provide a detailed travel plan related to a travel style if the user has a strong interest in that style. Furthermore, the generation unit can adjust the level of detail in the travel plan according to the importance of the user's preferences. This allows the generation unit to provide detailed information that is important to the user by adjusting the level of detail in the plan according to the importance of the user's preferences. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user preference importance data into a generation AI, and the generation AI can analyze the information and adjust the level of detail in the plan.

[0050] The generation unit can apply different generation algorithms depending on the user's travel style during generation. For example, if the user prefers an active travel style, the generation unit can apply an activity-related generation algorithm. The generation unit can also apply a relaxation-related generation algorithm if the user prefers a relaxed travel style. Furthermore, the generation unit can select the optimal generation algorithm according to the user's travel style. This allows for the provision of more appropriate travel plans by applying the optimal generation algorithm according to the user's travel style. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's travel style data into a generation AI, which can analyze the information and apply the optimal generation algorithm.

[0051] The generation unit can determine the priority of travel plans based on the user's past travel history during generation. For example, the generation unit can prioritize providing relevant travel plans based on data of destinations the user has visited in the past. The generation unit can also provide appropriate travel plans by considering the user's past travel frequency. Furthermore, the generation unit can analyze the user's past travel style and provide the optimal travel plan. This allows the generation unit to prioritize highly relevant travel plans by determining the priority of plans based on the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past travel history data into a generation AI, which can then analyze the information to determine the priority of plans.

[0052] The generation unit can adjust the order of plans based on user relevance during generation. For example, if a user has a strong interest in a particular dish, the generation unit can prioritize providing travel plans related to that dish. The generation unit can also prioritize providing travel plans related to a travel style if a user has a strong interest in that style. Furthermore, the generation unit can adjust the order of travel plans according to user relevance. This allows for the priority provision of information important to the user by adjusting the order of plans according to user relevance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user relevance data into a generation AI, which can analyze the information and adjust the order of plans.

[0053] The reservation unit can analyze the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation unit can suggest the optimal reservation method based on the reservation method the user has used in the past. The reservation unit can also suggest the most frequently used reservation method based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history to select the most efficient reservation method. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above processing in the reservation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the reservation unit can input the user's past reservation history data into a generation AI, and the generation AI can analyze the information to select the optimal reservation method.

[0054] The reservation unit can customize the reservation process based on the user's current lifestyle. For example, if the user is busy, the reservation unit can provide a simple reservation process. The reservation unit can also provide a detailed reservation process if the user is relaxed. Furthermore, the reservation unit can customize the optimal reservation process according to the user's lifestyle. This allows the reservation unit to provide the user with the most suitable reservation process by customizing the reservation process based on the user's current lifestyle. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input user lifestyle data into a generative AI, which can then analyze the information and customize the reservation process.

[0055] The reservation unit can select the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, the reservation unit can prioritize booking nearby accommodations and transportation based on the user's current location. The reservation unit can also suggest relevant reservation methods by considering the user's past visits. Furthermore, the reservation unit can select the optimal reservation method based on the user's geographical location information. This allows the system to select the optimal reservation method by considering the user's geographical location information. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input the user's geographical location information into a generative AI, which can then analyze the information and select the optimal reservation method.

[0056] The reservation unit can analyze the user's social media activity and suggest reservation methods at the time of reservation. For example, the reservation unit can analyze the user's social media posts and suggest relevant reservation methods. The reservation unit can also suggest the optimal reservation method based on the user's social media followers. Furthermore, the reservation unit can analyze the user's social media activity history and select the optimal reservation method. In this way, the optimal reservation method can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input the user's social media data into a generative AI, which can then analyze the information and suggest reservation methods.

[0057] The update unit can select the optimal update algorithm by referring to past update data during the update process. For example, the update unit can select the optimal update algorithm based on past update data. The update unit can also prioritize the selection of frequently used update algorithms from past update data. Furthermore, the update unit can analyze past update data and select the most efficient update algorithm. This allows the optimal update algorithm to be selected by referring to past update data. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input past update data into a generative AI, which can then analyze the information and select the optimal update algorithm.

[0058] The update unit can select the optimal update method when updating, taking into account the user's geographical location information. For example, the update unit can prioritize updating nearby information based on the user's current location. The update unit can also prioritize updating relevant information by taking into account the user's past visited locations. Furthermore, the update unit can select the optimal update method based on the user's geographical location information. This allows the optimal update method to be selected by taking into account the user's geographical location information. Some or all of the above processing in the update unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the update unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the information and select the optimal update method.

[0059] The feedback collection unit can analyze the user's past feedback history to select the optimal collection method when collecting feedback. For example, the feedback collection unit can propose the optimal collection method based on the feedback the user has provided in the past. The feedback collection unit can also prioritize and propose frequently used collection methods based on the user's past feedback history. Furthermore, the feedback collection unit can analyze the user's past feedback history to select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's past feedback history data into a generative AI, which can then analyze the information and select the optimal collection method.

[0060] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting feedback on nearby information based on the user's current location. The feedback collection unit can also prioritize collecting relevant feedback by considering the user's past visited locations. Furthermore, the feedback collection unit can select the optimal feedback collection method based on the user's geographical location information. This allows for the selection of the optimal feedback collection method by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's geographical location information into a generative AI, which can then analyze the information and select the optimal feedback collection method.

[0061] The improvement unit can select the optimal improvement algorithm by referring to past improvement data during the improvement process. For example, the improvement unit can select the optimal improvement algorithm based on past improvement data. The improvement unit can also prioritize the selection of frequently used improvement algorithms from past improvement data. Furthermore, the improvement unit can analyze past improvement data and select the most efficient improvement algorithm. In this way, the optimal improvement algorithm can be selected by referring to past improvement data. Some or all of the above processing in the improvement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the improvement unit can input past improvement data into a generative AI, and the generative AI can analyze the information and select the optimal improvement algorithm.

[0062] The improvement unit can select the optimal improvement method when making improvements, taking into account the user's geographical location information. For example, the improvement unit can prioritize improvement suggestions related to nearby information based on the user's current location. The improvement unit can also prioritize improvement suggestions related to the user's past visited locations, taking into account the user's past visits. Furthermore, the improvement unit can select the optimal improvement method based on the user's geographical location information. This allows the optimal improvement method to be selected by considering the user's geographical location information. Some or all of the above processing in the improvement unit may be performed using, for example, a generating AI, or without a generating AI. For example, the improvement unit can input the user's geographical location information into a generating AI, which can then analyze the information and select the optimal improvement method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The gourmet travel agent system can suggest travel plans tailored to specific seasons and events based on the user's food preferences and travel style. For example, the data collection unit can collect information about the dishes and events that the user likes in a particular season. The analysis unit uses the collected information to identify the seasons and events the user prefers and generates an optimal travel plan. The generation unit can, for example, generate plans for Japanese gourmet spots tailored to cherry blossom season or food stalls tailored to summer festivals. This allows users to enjoy special travel experiences tailored to the season and events.

[0065] The gourmet travel agent system can customize travel plans based on the user's health status and dietary restrictions. The data collection unit can collect data on the user's allergies and health status. The analysis unit performs analysis based on the collected information, taking into account the user's health status and dietary restrictions. The generation unit can generate travel plans that include, for example, gluten-free or vegan-friendly gourmet spots. This allows users to enjoy their trips with peace of mind.

[0066] The gourmet travel agent system can analyze a user's past travel history and propose special travel plans for repeat customers. The data collection unit gathers the user's past travel history data, and the analysis unit analyzes this data to identify the user's preferences and tendencies. The generation unit can generate travel plans that include, for example, new gourmet spots in areas the user has visited before, or special offers for repeat customers. This allows users to relive cherished past memories while making new discoveries.

[0067] The gourmet travel agent system can analyze users' social media activity and propose travel plans based on trends. The data collection unit can collect data on users' social media posts and who they follow. The analysis unit identifies trends and topics of interest to users based on the collected data. The generation unit can generate travel plans that include, for example, currently trending gourmet spots and popular events. This allows users to enjoy travel based on the latest trends.

[0068] The gourmet travel agent system can provide real-time local information by taking into account the user's geographical location. The data collection unit gathers data about the user's current location, and the analysis unit uses this data to identify information relevant to the user's current location. The generation unit can, for example, provide the latest gourmet spots and event information in the area the user is currently in. This allows users to enjoy a more fulfilling travel experience based on the latest local information.

[0069] The gourmet travel agent system can analyze users' past feedback history and improve services based on that feedback. The data collection unit collects past user feedback data, and the analysis unit uses that data to identify user requests and areas for improvement. The improvement unit can, for example, propose improvements to address problems previously pointed out by users. It can also add new features in response to user requests. By improving services based on user feedback, the system can provide a more satisfying service.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects information about the user's food preferences and travel style. For example, it collects information such as the type of cuisine the user likes, past travel destinations, and travel frequency. If the user likes Japanese food, the data collection unit can collect information about Japanese food. It can also collect information about past travel destinations and travel frequency. Step 2: The analysis unit analyzes the information collected by the data collection unit to analyze user preferences. The analysis unit can analyze user preferences with high accuracy using methods such as machine learning, deep learning, support vector machines, and clustering. Step 3: The generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. The generation unit generates a travel plan that includes visits to undiscovered local gourmet spots, accommodations, and transportation. For example, it can generate a list of gourmet spots that the user should visit, accommodation reservation information, and details of transportation. Step 4: The booking unit books the travel plans generated by the generation unit. The booking unit can book the proposed travel plans all at once. For example, it can book by integrating multiple booking sites or by using a dedicated booking system. It can also customize the booking method according to the user's preferences.

[0072] (Example of form 2) The gourmet travel agent system according to an embodiment of the present invention is a system that analyzes a user's food preferences and travel style and proposes an optimal regional gourmet travel plan. This gourmet travel agent system collects information on the user's food preferences and travel style, and the AI ​​analyzes the user's preferences to generate an optimal regional gourmet travel plan. This plan includes visits to undiscovered regional gourmet spots, accommodations, and transportation, and the user can book the proposed plan all at once. For example, the gourmet travel agent system collects information such as the type of cuisine the user likes, past travel destinations, and travel frequency. This information is input into the AI, which uses machine learning to analyze the user's preferences and generate an optimal travel plan. For example, if the user likes Japanese food, the AI ​​will propose undiscovered regional Japanese gourmet spots. Also, if the user prefers a travel style that enjoys nature, the AI ​​will propose accommodations and transportation in areas rich in nature. The generated travel plan includes visits to undiscovered regional gourmet spots, accommodations, and transportation, and includes a list of gourmet spots the user should visit, accommodation booking information, and details of transportation. This allows the user to review and book the travel plan all at once. Furthermore, the AI ​​updates local information in real time, providing the latest information on gourmet spots and accommodations. This allows users to always plan their trips based on the most up-to-date information. This system significantly reduces the time and effort required for travel planning. It also contributes to the revitalization of local economies by discovering undiscovered gourmet spots in rural areas. For example, when users visit the suggested gourmet spots, the use of local restaurants and accommodations increases, stimulating the local economy. This gourmet travel agent system is extremely useful for individuals who want to enjoy gourmet travel, travelers who want to discover hidden gems in rural areas, and users who seek efficient travel planning. For example, if a busy business person wants to plan a trip efficiently in a short period of time, they can use this gourmet travel agent system to create an optimal travel plan in a short amount of time. In addition, by continuously improving the service using user feedback, more personalized travel suggestions become possible.For example, based on feedback provided by users after their trip, the AI ​​further optimizes their next travel plan. In this way, by utilizing the gourmet travel agent system, users can discover new local cuisine and contribute to the revitalization of local economies. Embark on a journey to discover new cuisine and rediscover the charm of local areas. Why not start an adventure to encounter unknown flavors with a gourmet travel agent? The gourmet travel agent system proposes the optimal local gourmet travel plan based on the user's food preferences and travel style, and handles the booking all in one place.

[0073] The gourmet travel agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a reservation unit. The collection unit collects information about the user's food preferences and travel style. For example, the collection unit collects information such as the type of cuisine the user likes, past travel destinations, and travel frequency. For example, if the user likes Japanese food, the collection unit can collect information about Japanese food. The collection unit can also collect information about past travel destinations the user has visited. Furthermore, the collection unit can collect information about the user's travel frequency. The analysis unit analyzes the information collected by the collection unit to analyze the user's preferences. For example, the analysis unit uses machine learning to analyze the user's preferences. For example, the analysis unit can use deep learning to analyze the user's preferences with high accuracy. The analysis unit can also use support vector machines to analyze the user's preferences. Furthermore, the analysis unit can also use clustering to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. For example, the generation unit generates a travel plan that includes visits to undiscovered local gourmet spots, accommodations, and transportation. The generation unit can, for example, generate a list of gourmet spots that the user should visit. It can also generate accommodation reservation information. Furthermore, it can generate details of transportation. The reservation unit makes reservations for the travel plans generated by the generation unit. For example, the reservation unit can make reservations for the proposed travel plans all at once. For example, the reservation unit can make reservations by integrating multiple reservation sites. It can also make reservations using a dedicated reservation system. Furthermore, the reservation unit can customize the reservation method according to the user's preferences. As a result, the gourmet travel agent system according to this embodiment can propose the optimal local gourmet travel plan based on the user's food preferences and travel style, and handle the entire reservation process.

[0074] The data collection unit gathers information about users' food preferences and travel styles. Specifically, it collects detailed information such as the types of cuisine users prefer, past travel destinations, travel frequency, travel purpose, budget, and whether or not they travel with companions. For example, if a user prefers Japanese food, the unit collects information such as their favorite Japanese dishes (sushi, tempura, udon, etc.), famous Japanese restaurants they have visited in the past, and their participation history in Japanese food-related events and festivals. Regarding past travel destinations, the unit collects detailed information such as the time of visit, length of stay, and the tourist attractions and gourmet spots visited. Furthermore, regarding the user's travel frequency, it collects information such as the number of trips per year, the season of travel, and the length of trips. This information is stored in a database as a user profile and used for analysis by the analysis unit. The data collection unit can collect information not only from information entered by users but also from external sources such as social media, blogs, and review sites. For example, it collects photos of meals and travel impressions posted by users on social media, as well as ratings and comments on review sites, to more accurately understand the user's preferences and travel style. Furthermore, the data collection unit can analyze the user's behavioral history and search history to identify places and dishes that the user is interested in but has not yet visited. This allows the data collection unit to comprehensively collect diverse user information, enabling highly accurate analysis by the analysis unit.

[0075] The analysis unit analyzes the information collected by the data collection unit to analyze user preferences. The analysis utilizes advanced technologies such as machine learning, deep learning, support vector machines, and clustering. Specifically, machine learning algorithms are used to learn the user's past behavior and preference patterns to predict future preferences. Deep learning allows for highly accurate analysis of user preferences and identification of subtle differences. For example, even if a user likes Japanese food, the analysis can determine in detail whether they particularly like sushi, tempura, or Japanese food from a specific region. Support vector machines are used to classify user preferences and identify user groups with similar preferences. Clustering divides user preferences into multiple categories and proposes the optimal travel plan for each category. The analysis unit combines these technologies to provide foundational data for generating optimal travel plans by comprehensively analyzing user preferences. Furthermore, the analysis unit can analyze collected information in real time, enabling rapid responses to changes in user preferences and trends. For example, if a user develops an interest in a new type of cuisine, this information is immediately reflected, and a travel plan based on the latest preferences is proposed. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and seasonal fluctuations, which can then be used to suggest future travel plans. This allows the analysis unit to analyze user preferences with high accuracy and consistently provide the optimal travel plan.

[0076] The generation unit generates the optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. Specifically, it generates a travel plan that includes visits to undiscovered local gourmet spots, accommodations, and transportation, tailored to the user's preferences. For example, if the user prefers Japanese food, it will suggest a plan to visit famous Japanese restaurants and hidden local gems. It can also generate a plan where the user selects a region they have never visited before and enjoys the local specialties and seasonal cuisine. The generation unit also pays attention to accommodation selection, suggesting accommodations that match the user's budget and preferences. For example, it selects accommodations that meet the user's needs, such as luxury ryokans (traditional Japanese inns), hot spring resorts, or guesthouses that utilize local characteristics. Furthermore, it also suggests the most suitable mode of transportation, taking into consideration the user's convenience. For example, it selects transportation methods that match the user's travel style, such as trains, buses, or rental cars. The generation unit combines these elements to generate the optimal travel plan for the user. In addition, the generation unit makes improvements to enhance the accuracy and satisfaction of the travel plan based on user feedback. For example, by analyzing the feedback provided by users after their trip and reflecting it in the next travel plan, it can provide a more satisfying plan. This allows the generation unit to create optimal travel plans tailored to the user's preferences and needs, providing them with an attractive travel experience.

[0077] The booking department books the travel plans generated by the generation department. Specifically, it can book proposed travel plans all at once. For example, it can integrate multiple booking sites to book accommodations, transportation, and restaurants all at once. The booking department can also customize booking methods to suit user preferences using a dedicated booking system. For example, if a user prefers a particular booking site, it will prioritize using that site for bookings. Furthermore, the booking department can propose the optimal booking plan according to the user's budget and schedule. For example, it can utilize early booking discounts and special campaigns to provide the user with the most advantageous plan. In addition, the booking department can flexibly handle changes and cancellations of bookings. For example, if a user's plans change, it will quickly modify the booking details and propose the most suitable plan again. The booking department also makes improvements to enhance the usability of the booking system and the quality of service based on user feedback. As a result, the booking department can provide users with a convenient and reliable booking service and smoothly support them from planning to executing their trip.

[0078] The gourmet travel agent system further includes an update unit that updates local information in real time. The update unit can update local information in real time. For example, the update unit can update information in seconds. It can also update information in minutes. Furthermore, the update unit can update information in hours. This allows users to create travel plans based on the latest information by updating local information in real time. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input local information into a generative AI, which can then analyze and update the information.

[0079] The gourmet travel agent system further includes a feedback collection unit that collects user feedback. The feedback collection unit can collect user feedback. For example, the feedback collection unit can collect satisfaction ratings. The feedback collection unit can also collect comments. Furthermore, the feedback collection unit can also collect suggestions for improvement. This allows the service to be continuously improved by collecting user feedback. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input user feedback into a generative AI, which can then analyze and collect the feedback.

[0080] The data collection unit can collect information such as the user's favorite types of food, past travel destinations, and travel frequency. For example, the data collection unit can collect information on the user's favorite types of food, such as Japanese, Western, or Chinese cuisine. The data collection unit can also collect information on past travel destinations, such as the name of the destination, the time of visit, and the length of stay. Furthermore, the data collection unit can collect information on the user's travel frequency, such as whether they travel several times a year or once a month. By collecting detailed information on the user's food preferences and travel style, a more accurate travel plan can be generated. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user information into a generative AI, which can then analyze and collect the information.

[0081] The analysis unit can analyze user preferences using machine learning. The analysis unit can analyze user preferences with high accuracy using, for example, deep learning. The analysis unit can also analyze user preferences using support vector machines. Furthermore, the analysis unit can analyze user preferences using clustering. This allows for high-accuracy analysis of user preferences using machine learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user information into a generative AI, which can then analyze the information to determine user preferences.

[0082] The generation unit can generate travel plans that include visits to undiscovered local gourmet spots, accommodations, and transportation. For example, the generation unit can generate a travel plan that includes visits to undiscovered local gourmet spots. For example, the generation unit can generate a list of gourmet spots that the user should visit. The generation unit can also generate accommodation reservation information. Furthermore, the generation unit can generate details of transportation. This allows the user to be offered a new experience by generating travel plans that include undiscovered local gourmet spots. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user information into a generation AI, and the generation AI can analyze the information and generate a travel plan.

[0083] The booking department can book proposed travel plans all at once. The booking department can, for example, integrate multiple booking sites to make reservations. The booking department can also, for example, integrate multiple booking sites to make reservations. Furthermore, the booking department can make reservations using a dedicated booking system. In addition, the booking department can customize the booking method according to the user's preferences. This significantly reduces the user's effort by allowing them to book proposed travel plans all at once. Some or all of the above processing in the booking department may be performed using, for example, a generative AI, or not using a generative AI. For example, the booking department can input user information into a generative AI, which can then analyze the information and make reservations.

[0084] The improvement unit can improve the service based on the feedback collected by the feedback collection unit. For example, the improvement unit can improve the user interface. The improvement unit can also add new features. Furthermore, the improvement unit can make improvements to enhance the service performance. This makes it possible to provide more personalized travel suggestions by improving the service based on the collected feedback. Some or all of the above processing in the improvement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the improvement unit can input user feedback into a generative AI, which can then analyze the information to improve the service.

[0085] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can send a questionnaire about their food preferences and travel style. The data collection unit can also collect information in the form of short questions if the user is busy. Furthermore, if the user is excited, the data collection unit can collect information in an interactive format. This allows for more effective information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input the user's emotion data into a generative AI, which can analyze the information and adjust the timing of information collection.

[0086] The data collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the data collection unit can generate relevant questions based on data of destinations the user has visited in the past. The data collection unit can also collect information at an appropriate time, taking into account the user's past travel frequency. Furthermore, the data collection unit can analyze the user's past travel style and suggest the optimal information collection method. This allows for the selection of the optimal information collection method by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the user's past travel history data into a generating AI, which can then analyze the information and select the optimal information collection method.

[0087] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can provide a form for the user to input their current living situation and filter the information. The data collection unit can also prioritize displaying relevant questions based on the user's areas of interest. Furthermore, the data collection unit can select an appropriate data collection method according to the user's living situation. This allows for the collection of more relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's living situation data into a generative AI, which can then analyze and filter the information.

[0088] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information. The data collection unit can also collect detailed information if the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect information in an interactive format. This allows for the priority of collecting important information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the information and determine its priority.

[0089] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information about nearby restaurants based on the user's current location. The data collection unit can also prioritize the collection of relevant information by considering the user's past visited locations. Furthermore, the data collection unit can select the optimal information collection method based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then analyze the information and prioritize the collection of highly relevant information.

[0090] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the user's social media posts and collect information about their food preferences. The data collection unit can also collect relevant information based on the user's social media followers. Furthermore, the data collection unit can analyze the user's social media activity history and select the optimal data collection method. This allows for the effective collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI, which can then analyze the information and collect relevant information.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze the information and adjust the presentation of the analysis.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's preferences during the analysis. For example, if the user has a strong interest in a particular dish, the analysis unit can provide detailed analysis results about that dish. The analysis unit can also provide detailed analysis results about a travel style if the user has a strong interest in that style. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the importance of the user's preferences. This allows the analysis unit to provide detailed information that is important to the user by adjusting the level of detail of the analysis according to the importance of the user's preferences. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user preference importance data into a generative AI, and the generative AI can analyze the information and adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the user's travel style during analysis. For example, if the user prefers an active travel style, the analysis unit can apply an analysis algorithm related to activities. The analysis unit can also apply a relaxation analysis algorithm if the user prefers a relaxed travel style. Furthermore, the analysis unit can select the optimal analysis algorithm according to the user's travel style. This allows for the provision of more appropriate analysis results by applying the optimal analysis algorithm according to the user's travel style. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's travel style data into a generative AI, which can analyze the information and apply the optimal analysis algorithm.

[0094] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of the optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can analyze the information and adjust the length of the analysis.

[0095] The analysis unit can determine the priority of analysis based on the user's past travel history during the analysis process. For example, the analysis unit can prioritize providing relevant analysis results based on data of destinations the user has visited in the past. The analysis unit can also provide appropriate analysis results by considering the user's past travel frequency. Furthermore, the analysis unit can analyze the user's past travel style and provide optimal analysis results. This allows the analysis unit to prioritize providing highly relevant analysis results by determining the priority of analysis based on the user's past travel history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past travel history data into a generating AI, which can then analyze the information and determine the priority of analysis.

[0096] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, if a user has a strong interest in a particular dish, the analysis unit can prioritize providing analysis results related to that dish. The analysis unit can also prioritize providing analysis results related to a travel style if the user has a strong interest in that style. Furthermore, the analysis unit can adjust the order of analysis results according to user relevance. This allows the system to prioritize providing information that is important to the user by adjusting the order of analysis according to user relevance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user relevance data into a generative AI, which can then analyze the information and adjust the order of analysis.

[0097] The generation unit can estimate the user's emotions and adjust the way the generated plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed travel plan. It can also provide a concise travel plan that gets straight to the point if the user is in a hurry. Furthermore, if the user is excited, the generation unit can provide a visually appealing travel plan. By adjusting the presentation of the plan according to the user's emotions, it is possible to provide a plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can analyze the information and adjust the presentation of the plan.

[0098] The generation unit can adjust the level of detail in the plan based on the importance of the user's preferences during generation. For example, if the user has a strong interest in a particular dish, the generation unit can provide a detailed travel plan related to that dish. The generation unit can also provide a detailed travel plan related to a travel style if the user has a strong interest in that style. Furthermore, the generation unit can adjust the level of detail in the travel plan according to the importance of the user's preferences. This allows the generation unit to provide detailed information that is important to the user by adjusting the level of detail in the plan according to the importance of the user's preferences. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user preference importance data into a generation AI, and the generation AI can analyze the information and adjust the level of detail in the plan.

[0099] The generation unit can apply different generation algorithms depending on the user's travel style during generation. For example, if the user prefers an active travel style, the generation unit can apply an activity-related generation algorithm. The generation unit can also apply a relaxation-related generation algorithm if the user prefers a relaxed travel style. Furthermore, the generation unit can select the optimal generation algorithm according to the user's travel style. This allows for the provision of more appropriate travel plans by applying the optimal generation algorithm according to the user's travel style. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's travel style data into a generation AI, which can analyze the information and apply the optimal generation algorithm.

[0100] The generation unit can estimate the user's emotions and adjust the length of the generated plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide a short, concise travel plan. The generation unit can also provide a detailed travel plan if the user is relaxed. Furthermore, if the user is excited, the generation unit can provide a visually appealing travel plan. By adjusting the length of the plan according to the user's emotions, the generation unit can provide a travel plan of the optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can analyze the information and adjust the length of the plan.

[0101] The generation unit can determine the priority of travel plans based on the user's past travel history during generation. For example, the generation unit can prioritize providing relevant travel plans based on data of destinations the user has visited in the past. The generation unit can also provide appropriate travel plans by considering the user's past travel frequency. Furthermore, the generation unit can analyze the user's past travel style and provide the optimal travel plan. This allows the generation unit to prioritize highly relevant travel plans by determining the priority of plans based on the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past travel history data into a generation AI, which can then analyze the information to determine the priority of plans.

[0102] The generation unit can adjust the order of plans based on user relevance during generation. For example, if a user has a strong interest in a particular dish, the generation unit can prioritize providing travel plans related to that dish. The generation unit can also prioritize providing travel plans related to a travel style if a user has a strong interest in that style. Furthermore, the generation unit can adjust the order of travel plans according to user relevance. This allows for the priority provision of information important to the user by adjusting the order of plans according to user relevance. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user relevance data into a generation AI, which can analyze the information and adjust the order of plans.

[0103] The reservation unit can estimate the user's emotions and adjust the timing of reservations based on those emotions. For example, if the user is relaxed, the reservation unit can provide detailed reservation options. It can also provide concise reservation options if the user is in a hurry. Furthermore, if the user is excited, the reservation unit can provide visually appealing reservation options. This allows reservations to be made at the optimal time for the user by adjusting the timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reservation unit may be performed using, for example, generative AI, or not. For example, the reservation unit can input user emotion data into a generative AI, which can analyze the information and adjust the timing of reservations.

[0104] The reservation unit can analyze the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation unit can suggest the optimal reservation method based on the reservation method the user has used in the past. The reservation unit can also suggest the most frequently used reservation method based on the user's past reservation history. Furthermore, the reservation unit can analyze the user's past reservation history to select the most efficient reservation method. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above processing in the reservation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the reservation unit can input the user's past reservation history data into a generation AI, and the generation AI can analyze the information to select the optimal reservation method.

[0105] The reservation unit can customize the reservation process based on the user's current lifestyle. For example, if the user is busy, the reservation unit can provide a simple reservation process. The reservation unit can also provide a detailed reservation process if the user is relaxed. Furthermore, the reservation unit can customize the optimal reservation process according to the user's lifestyle. This allows the reservation unit to provide the user with the most suitable reservation process by customizing the reservation process based on the user's current lifestyle. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input user lifestyle data into a generative AI, which can then analyze the information and customize the reservation process.

[0106] The reservation system can estimate the user's emotions and prioritize reservations based on those emotions. For example, if the user is feeling stressed, the reservation system will prioritize important reservations. The reservation system can also provide detailed reservation options if the user is relaxed. Furthermore, if the user is excited, the reservation system can provide visually appealing reservation options. This allows for prioritizing important reservations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using or without a generative AI. For example, the reservation system can input user emotion data into a generative AI, which can then analyze the information to determine reservation priorities.

[0107] The reservation unit can select the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, the reservation unit can prioritize booking nearby accommodations and transportation based on the user's current location. The reservation unit can also suggest relevant reservation methods by considering the user's past visits. Furthermore, the reservation unit can select the optimal reservation method based on the user's geographical location information. This allows the system to select the optimal reservation method by considering the user's geographical location information. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input the user's geographical location information into a generative AI, which can then analyze the information and select the optimal reservation method.

[0108] The reservation unit can analyze the user's social media activity and suggest reservation methods at the time of reservation. For example, the reservation unit can analyze the user's social media posts and suggest relevant reservation methods. The reservation unit can also suggest the optimal reservation method based on the user's social media followers. Furthermore, the reservation unit can analyze the user's social media activity history and select the optimal reservation method. In this way, the optimal reservation method can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reservation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reservation unit can input the user's social media data into a generative AI, which can then analyze the information and suggest reservation methods.

[0109] The update unit can estimate the user's emotions and adjust the timing of information updates based on the estimated emotions. For example, if the user is relaxed, the update unit can provide detailed information updates. The update unit can also provide concise information updates if the user is in a hurry. Furthermore, if the user is excited, the update unit can provide visually appealing information updates. By adjusting the timing of information updates according to the user's emotions, information can be updated at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using a generative AI, or not using a generative AI. For example, the update unit can input user emotion data into a generative AI, which can analyze the information and adjust the timing of information updates.

[0110] The update unit can select the optimal update algorithm by referring to past update data during the update process. For example, the update unit can select the optimal update algorithm based on past update data. The update unit can also prioritize the selection of frequently used update algorithms from past update data. Furthermore, the update unit can analyze past update data and select the most efficient update algorithm. This allows the optimal update algorithm to be selected by referring to past update data. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input past update data into a generative AI, which can then analyze the information and select the optimal update algorithm.

[0111] The update unit can estimate the user's emotions and adjust the frequency of information updates based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize updating important information. The update unit can also provide detailed information updates if the user is relaxed. Furthermore, if the user is excited, the update unit can provide visually appealing information updates. By adjusting the frequency of information updates according to the user's emotions, information can be updated at an optimal frequency for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using a generative AI, or not. For example, the update unit can input user emotion data into a generative AI, which can analyze the information and adjust the frequency of information updates.

[0112] The update unit can select the optimal update method when updating, taking into account the user's geographical location information. For example, the update unit can prioritize updating nearby information based on the user's current location. The update unit can also prioritize updating relevant information by taking into account the user's past visited locations. Furthermore, the update unit can select the optimal update method based on the user's geographical location information. This allows the optimal update method to be selected by taking into account the user's geographical location information. Some or all of the above processing in the update unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the update unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the information and select the optimal update method.

[0113] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit can request detailed feedback. The feedback collection unit can also request concise feedback if the user is in a hurry. Furthermore, if the user is excited, the feedback collection unit can request feedback in an interactive format. This allows feedback to be collected at the optimal time for the user by adjusting the timing of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using a generative AI, or not using a generative AI. For example, the feedback collection unit can input the user's emotion data into a generative AI, which can analyze the information and adjust the timing of feedback collection.

[0114] The feedback collection unit can analyze the user's past feedback history to select the optimal collection method when collecting feedback. For example, the feedback collection unit can propose the optimal collection method based on the feedback the user has provided in the past. The feedback collection unit can also prioritize and propose frequently used collection methods based on the user's past feedback history. Furthermore, the feedback collection unit can analyze the user's past feedback history to select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's past feedback history data into a generative AI, which can then analyze the information and select the optimal collection method.

[0115] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated emotions. For example, if the user is stressed, the feedback collection unit can prioritize collecting important feedback. The feedback collection unit can also request detailed feedback if the user is relaxed. Furthermore, if the user is excited, the feedback collection unit can request feedback in an interactive format. This allows for the priority collection of important feedback by determining the priority of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using a generative AI, or not. For example, the feedback collection unit can input user emotion data into a generative AI, which can analyze the information and determine the priority of feedback collection.

[0116] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting feedback on nearby information based on the user's current location. The feedback collection unit can also prioritize collecting relevant feedback by considering the user's past visited locations. Furthermore, the feedback collection unit can select the optimal feedback collection method based on the user's geographical location information. This allows for the selection of the optimal feedback collection method by considering the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's geographical location information into a generative AI, which can then analyze the information and select the optimal feedback collection method.

[0117] The improvement unit can estimate the user's emotions and adjust the timing of improvements based on the estimated emotions. For example, if the user is relaxed, the improvement unit can provide detailed improvement suggestions. The improvement unit can also provide concise improvement suggestions if the user is in a hurry. Furthermore, if the user is excited, the improvement unit can provide visually appealing improvement suggestions. By adjusting the timing of improvements according to the user's emotions, improvement suggestions can be provided at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using a generative AI, or not using a generative AI. For example, the improvement unit can input user emotion data into a generative AI, which can analyze the information and adjust the timing of improvements.

[0118] The improvement unit can select the optimal improvement algorithm by referring to past improvement data during the improvement process. For example, the improvement unit can select the optimal improvement algorithm based on past improvement data. The improvement unit can also prioritize the selection of frequently used improvement algorithms from past improvement data. Furthermore, the improvement unit can analyze past improvement data and select the most efficient improvement algorithm. In this way, the optimal improvement algorithm can be selected by referring to past improvement data. Some or all of the above processing in the improvement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the improvement unit can input past improvement data into a generative AI, and the generative AI can analyze the information and select the optimal improvement algorithm.

[0119] The improvement unit can estimate the user's emotions and adjust the frequency of improvements based on the estimated emotions. For example, if the user is feeling stressed, the improvement unit will prioritize important improvements. The improvement unit can also provide detailed improvement suggestions if the user is relaxed. Furthermore, if the user is excited, the improvement unit can provide visually appealing improvement suggestions. By adjusting the frequency of improvements according to the user's emotions, improvement suggestions can be provided at the optimal frequency for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using a generative AI, or not using a generative AI. For example, the improvement unit can input user emotion data into a generative AI, which can analyze the information and adjust the frequency of improvements.

[0120] The improvement unit can select the optimal improvement method when making improvements, taking into account the user's geographical location information. For example, the improvement unit can prioritize improvement suggestions related to nearby information based on the user's current location. The improvement unit can also prioritize improvement suggestions related to the user's past visited locations, taking into account the user's past visits. Furthermore, the improvement unit can select the optimal improvement method based on the user's geographical location information. This allows the optimal improvement method to be selected by considering the user's geographical location information. Some or all of the above processing in the improvement unit may be performed using, for example, a generating AI, or without a generating AI. For example, the improvement unit can input the user's geographical location information into a generating AI, which can then analyze the information and select the optimal improvement method.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The gourmet travel agent system can suggest travel plans tailored to specific seasons and events based on the user's food preferences and travel style. For example, the data collection unit can collect information about the dishes and events that the user likes in a particular season. The analysis unit uses the collected information to identify the seasons and events the user prefers and generates an optimal travel plan. The generation unit can, for example, generate plans for Japanese gourmet spots tailored to cherry blossom season or food stalls tailored to summer festivals. This allows users to enjoy special travel experiences tailored to the season and events.

[0123] The gourmet travel agent system can customize travel plans based on the user's health status and dietary restrictions. The data collection unit can collect data on the user's allergies and health status. The analysis unit performs analysis based on the collected information, taking into account the user's health status and dietary restrictions. The generation unit can generate travel plans that include, for example, gluten-free or vegan-friendly gourmet spots. This allows users to enjoy their trips with peace of mind.

[0124] The gourmet travel agent system can estimate the user's emotions and adjust the suggested travel plan based on those emotions. The data collection unit collects user emotion data, and the analysis unit analyzes this data to identify the user's current emotional state. The generation unit, for example, can suggest a relaxing travel plan if the user is feeling stressed, or an active travel plan if the user is excited. This allows the system to provide the optimal travel plan tailored to the user's emotions.

[0125] The gourmet travel agent system can analyze a user's past travel history and propose special travel plans for repeat customers. The data collection unit gathers the user's past travel history data, and the analysis unit analyzes this data to identify the user's preferences and tendencies. The generation unit can generate travel plans that include, for example, new gourmet spots in areas the user has visited before, or special offers for repeat customers. This allows users to relive cherished past memories while making new discoveries.

[0126] The gourmet travel agent system can estimate the user's emotions and adjust the communication method of the travel plan based on those estimated emotions. The data collection unit collects user emotion data, and the analysis unit analyzes this data to identify the user's current emotional state. The generation unit can, for example, provide a travel plan with detailed explanations if the user is relaxed, or a concise, to-the-point travel plan if the user is in a hurry. This allows the system to provide a travel plan using the most appropriate communication method according to the user's emotions.

[0127] The gourmet travel agent system can analyze users' social media activity and propose travel plans based on trends. The data collection unit can collect data on users' social media posts and who they follow. The analysis unit identifies trends and topics of interest to users based on the collected data. The generation unit can generate travel plans that include, for example, currently trending gourmet spots and popular events. This allows users to enjoy travel based on the latest trends.

[0128] The gourmet travel agent system can estimate the user's emotions and adjust the method of collecting feedback on the travel plan based on those estimated emotions. The data collection unit collects the user's emotional data, and the analysis unit analyzes this data to identify the user's current emotional state. The feedback collection unit can, for example, request detailed feedback if the user is relaxed, or request concise feedback if the user is in a hurry. This allows the system to provide the optimal feedback collection method tailored to the user's emotions.

[0129] The gourmet travel agent system can provide real-time local information by taking into account the user's geographical location. The data collection unit gathers data about the user's current location, and the analysis unit uses this data to identify information relevant to the user's current location. The generation unit can, for example, provide the latest gourmet spots and event information in the area the user is currently in. This allows users to enjoy a more fulfilling travel experience based on the latest local information.

[0130] The gourmet travel agent system can estimate the user's emotions and adjust the frequency of travel plan updates based on those emotions. The data collection unit collects user emotion data, and the analysis unit analyzes this data to identify the user's current emotional state. The update unit can, for example, prioritize updating important information if the user is feeling stressed. Conversely, if the user is relaxed, it can provide more detailed information updates. This allows the system to provide information at an optimal update frequency tailored to the user's emotions.

[0131] The gourmet travel agent system can analyze users' past feedback history and improve services based on that feedback. The data collection unit collects past user feedback data, and the analysis unit uses that data to identify user requests and areas for improvement. The improvement unit can, for example, propose improvements to address problems previously pointed out by users. It can also add new features in response to user requests. By improving services based on user feedback, the system can provide a more satisfying service.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The data collection unit collects information about the user's food preferences and travel style. For example, it collects information such as the type of cuisine the user likes, past travel destinations, and travel frequency. If the user likes Japanese food, the data collection unit can collect information about Japanese food. It can also collect information about past travel destinations and travel frequency. Step 2: The analysis unit analyzes the information collected by the data collection unit to analyze user preferences. The analysis unit can analyze user preferences with high accuracy using methods such as machine learning, deep learning, support vector machines, and clustering. Step 3: The generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit. The generation unit generates a travel plan that includes visits to undiscovered local gourmet spots, accommodations, and transportation. For example, it can generate a list of gourmet spots that the user should visit, accommodation reservation information, and details of transportation. Step 4: The booking unit books the travel plans generated by the generation unit. The booking unit can book the proposed travel plans all at once. For example, it can book by integrating multiple booking sites or by using a dedicated booking system. It can also customize the booking method according to the user's preferences.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reservation unit, update unit, and feedback collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information about the user's food preferences and travel style by the control unit 46A of the smart device 14. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan by the specific processing unit 290 of the data processing unit 12. The reservation unit reserves the travel plan generated by the control unit 46A of the smart device 14. The update unit updates local information in real time by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reservation unit, update unit, and feedback collection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information about the user's food preferences and travel style by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan by the specific processing unit 290 of the data processing unit 12. The reservation unit reserves the travel plan generated by the control unit 46A of the smart glasses 214. The update unit updates local information in real time by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reservation unit, update unit, and feedback collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information about the user's food preferences and travel style by the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan by the specific processing unit 290 of the data processing unit 12. The reservation unit reserves the travel plan generated by the control unit 46A of the headset terminal 314. The update unit updates local information in real time by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reservation unit, update unit, and feedback collection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information about the user's food preferences and travel style by the control unit 46A of the robot 414. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12 to analyze the user's preferences. The generation unit generates an optimal local gourmet travel plan by the specific processing unit 290 of the data processing unit 12. The reservation unit reserves the travel plan generated by the control unit 46A of the robot 414. The update unit updates local information in real time by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects user feedback by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0187] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] 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.

[0197] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] (Note 1) A collection unit that collects information about users' food preferences and travel styles, An analysis unit analyzes the information collected by the aforementioned collection unit and analyzes user preferences, A generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit, The system includes a reservation unit that reserves the travel plan generated by the generation unit. A system characterized by the following features. (Note 2) It also includes an update unit that updates local information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes a feedback collection unit for gathering user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system collects information such as the user's favorite types of food, past travel destinations, and travel frequency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We use machine learning to analyze user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate travel plans that include visits to undiscovered local gourmet spots, accommodations, and transportation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reservation section is, Book all the suggested travel plans at once. The system described in Appendix 1, characterized by the features described herein. (Note 8) The system further comprises an improvement unit that improves the service based on the feedback collected by the aforementioned feedback collection unit. The system described in Appendix 3, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past travel history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the user's preferred importance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's travel style. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the system prioritizes analysis based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts how the generated plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the level of detail in the plan is adjusted based on the user's preferred importance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, different generation algorithms are applied depending on the user's travel style. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts the length of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During creation, the plan's priority is determined based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the order of plans is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, It estimates the user's emotions and adjusts the timing of reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When making a reservation, the reservation method will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is, It estimates the user's emotions and adjusts the timing of information updates based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned update unit is During updates, the system selects the optimal update algorithm by referring to past update data. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned update unit is It estimates the user's sentiment and adjusts the frequency of information updates based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned update unit is During updates, the system will select the optimal update method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned feedback collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned feedback collection unit is When collecting feedback, the system analyzes the user's past feedback history to select the optimal collection method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback collection based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned improvement unit is, We estimate the user's emotions and adjust the timing of improvements based on those estimated emotions. The system described in Appendix 8, characterized by the features described herein. (Note 42) The aforementioned improvement unit is, When making improvements, the optimal improvement algorithm is selected by referring to past improvement data. The system described in Appendix 8, characterized by the features described herein. (Note 43) The aforementioned improvement unit is, It estimates the user's emotions and adjusts the frequency of improvements based on those estimated emotions. The system described in Appendix 8, characterized by the features described herein. (Note 44) The aforementioned improvement unit is, When making improvements, the optimal improvement method will be selected by considering the user's geographical location information. The system described in Appendix 8, characterized by the features described herein. [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information about users' food preferences and travel styles, An analysis unit analyzes the information collected by the aforementioned collection unit and analyzes user preferences, A generation unit generates an optimal local gourmet travel plan based on the analysis results obtained by the analysis unit, The system includes a reservation unit that reserves the travel plan generated by the generation unit. A system characterized by the following features.

2. It also includes an update unit that updates local information in real time. The system according to feature 1.

3. It also includes a feedback collection unit for gathering user feedback. The system according to feature 1.

4. The aforementioned collection unit is The system collects information such as the user's favorite types of food, past travel destinations, and travel frequency. The system according to feature 1.

5. The aforementioned analysis unit, We use machine learning to analyze user preferences. The system according to feature 1.

6. The generating unit is Generate travel plans that include visits to undiscovered local gourmet spots, accommodations, and transportation. The system according to feature 1.

7. The aforementioned reservation section is, Book all the suggested travel plans at once. The system according to feature 1.

8. The system further comprises an improvement unit that improves the service based on the feedback collected by the aforementioned feedback collection unit. The system according to claim 3.

9. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze the user's past travel history to select the most suitable information gathering method. The system according to feature 1.

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