system

Abstract

The system according to this embodiment aims to enable users to create travel plans quickly and enjoyably. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, and a reservation unit. The analysis unit analyzes the user's interests and usage history. The proposal unit proposes a travel plan based on the results analyzed by the analysis unit. The reservation unit makes a reservation based on the plan proposed by the proposal unit.

Description

Technical Field

[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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time to consider a travel plan and it is difficult for a user to plan a trip enjoyably.

[0005] The system according to the embodiment aims to enable a user to create a travel plan quickly and enjoyably.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and a reservation unit. The analysis unit analyzes the user's interests and usage history. The proposal unit proposes a travel plan based on the results analyzed by the analysis unit. The reservation unit makes a reservation based on the plan proposed by the proposal unit.

Effects of the Invention

[0007] The system according to this embodiment can enable users to create travel plans quickly and enjoyably. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 travel plan suggestion system according to an embodiment of the present invention is a system that analyzes a user's interests and usage history, proposes an optimal travel plan, and makes reservations. The travel plan suggestion system uses AI to analyze the user's interests and usage history of the electronic payment system and proposes an optimal travel plan. Next, the AI, which has learned the knowledge and experience of professional guides, provides a travel plan, and the user can enjoy the trip while listening to explanations from an avatar guide. In addition, the system can also make reservations for services through the cooperation of the generating AI with other AI systems. This mechanism makes it possible to create a travel plan quickly and enjoyably and to use it at a discount. First, the AI ​​analyzes the user's interests and usage history of the electronic payment system. At this time, data on places the user has visited and services used in the past is collected and analyzed by the AI ​​to understand the user's interests and preferences. For example, by collecting data on tourist destinations the user has visited and restaurants they have used in the past and analyzing it, the AI ​​can propose an optimal travel plan to the user. Next, the AI, which has learned the knowledge and experience of professional guides, provides a travel plan. For example, the AI ​​can suggest sightseeing spots and recommended routes based on the knowledge of professional guides. As a result, the user can enjoy the trip while listening to explanations from an avatar guide. Furthermore, by integrating the generating AI with other AI systems, service reservations can also be made. For example, reservations for restaurants and accommodations suggested by the AI ​​can be automatically made through the integration of the generating AI with other AI systems. This allows users to prepare for their trips without hassle. This system enables users to quickly and enjoyably create travel plans and take advantage of great deals. Users can intuitively create travel plans and enjoy their trips without complex operations. In addition, by utilizing the deals suggested by the AI, users can maximize their travel budget. For example, they can save on travel expenses by using coupons and points suggested by the AI. In this way, the AI-powered travel plan creation and reservation function enables users to quickly and enjoyably create travel plans and take advantage of great deals. Please take advantage of the AI ​​suggestion and reservation function and enjoy a wonderful trip.This allows the travel plan suggestion system to propose and book the most suitable travel plan based on the user's interests and usage history.

[0029] The travel plan suggestion system according to this embodiment comprises an analysis unit, a suggestion unit, and a reservation unit. The analysis unit analyzes the user's interests and usage history. For example, the analysis unit collects the user's past travel history and reservation history, and the AI ​​analyzes it to understand the user's interests and preferences. For example, the analysis unit collects data on tourist destinations the user has visited and restaurants they have used in the past, and the AI ​​analyzes it to suggest the optimal travel plan for the user. The suggestion unit has an AI that has learned the knowledge of professional guides to suggest a travel plan. For example, the suggestion unit has an AI that, based on the knowledge of professional guides, suggests the highlights of tourist destinations and recommended routes. For example, the suggestion unit has an AI that suggests the highlights of tourist destinations, and the user can enjoy the trip while listening to explanations from an avatar guide. The reservation unit makes reservations based on the plan suggested by the suggestion unit. For example, the reservation unit can automatically make reservations for suggested restaurants and accommodations by having a generating AI and other AI systems work together. For example, the reservation unit can automatically make reservations for restaurants and accommodations suggested by the AI ​​through the cooperation of a generating AI and other AI systems. As a result, the travel plan suggestion system according to the embodiment can suggest the optimal travel plan based on the user's interests and usage history, and make reservations.

[0030] The analytics department analyzes user interests and usage history. Specifically, it collects users' past travel and booking history, and uses AI to analyze it to understand user interests and preferences. For example, it collects data on tourist destinations and restaurants users have visited in the past, and the AI ​​analyzes this data to suggest the most suitable travel plan for the user. The AI ​​uses natural language processing technology to analyze user reviews and comments to identify activities and tourist destinations that users are particularly interested in. It also analyzes users' social media posts and photos to gain a more detailed understanding of user preferences and interests. Furthermore, the AI ​​analyzes users' past travel patterns and seasonal travel trends to predict their next travel destination. For example, if a user has visited a beach resort in the summer in the past, it can suggest other beach resorts as their next summer travel destination. In this way, the analytics department can comprehensively analyze diverse user data and understand users' interests and preferences with high accuracy.

[0031] The proposal department uses AI, trained on the knowledge of professional guides, to suggest travel plans. Specifically, the AI ​​uses the knowledge of professional guides to suggest sightseeing spots and recommended routes. For example, the AI ​​learns information about the history, culture, and local specialties of tourist destinations and provides users with detailed explanations. Furthermore, the AI ​​creates customized travel plans based on the user's interests. For example, if a user is interested in historical buildings, the AI ​​will suggest a plan centered on historical tourist spots in that area. The AI ​​also supports real-time information updates and can resuggest optimal routes and sightseeing spots in response to changes in weather and traffic conditions. Users can enjoy their trip while listening to explanations from their avatar guide, experiencing it as if they were traveling with a local professional guide. In this way, the proposal department can provide users with high-quality, personalized travel plans and improve their travel satisfaction.

[0032] The reservation department makes reservations based on plans proposed by the suggestion department. Specifically, the generation AI and other AI systems work together to automatically make reservations for suggested restaurants and accommodations. For example, reservations for restaurants and accommodations suggested by the AI ​​can be automatically made through the collaboration of the generation AI and other AI systems. The generation AI considers the user's desired date and time, budget, and special requests to select the optimal reservation option. The reservation department can also search across multiple reservation sites and services to find the best deal. Furthermore, the reservation department automates procedures such as reservation confirmation, modification, and cancellation, significantly reducing the burden on the user. For example, if a user needs to cancel a reservation due to a sudden change in plans, the AI ​​will automatically handle the cancellation process, minimizing the cost of cancellation fees. The reservation department also manages the user's reservation history to ensure smooth reservations for future trips. In this way, the reservation department provides users with a fast and efficient reservation service, allowing them to plan their trips smoothly.

[0033] The analysis unit can analyze a user's past travel history in detail and extract data to propose the optimal travel plan. For example, the analysis unit can collect data on tourist destinations the user has visited in the past, and have AI analyze it to understand the user's interests and preferences. The analysis unit can also collect data on restaurants and accommodations the user has used in the past, and have AI analyze it to propose the optimal travel plan for the user. Furthermore, the analysis unit can extract data related to specific seasons or events from the user's past travel history and propose the optimal travel plan. In this way, by analyzing past travel history in detail, the analysis unit can propose the optimal travel plan for the user. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's past travel history data into a generating AI and have the generating AI perform the extraction of data to propose the optimal travel plan.

[0034] The analysis unit can filter data based on the user's current lifestyle and areas of interest during analysis. For example, the analysis unit can suggest relevant travel plans based on the user's current occupation and lifestyle. It can also suggest interesting tourist destinations and activities based on the user's current hobbies and areas of interest. Furthermore, the analysis unit can suggest appropriate travel plans based on the user's current health status and fitness level. By filtering data based on the user's current lifestyle and areas of interest, it is possible to suggest more relevant travel plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0035] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location during the analysis process. For example, the analysis unit can prioritize suggesting tourist destinations and activities close to the user's current location. Furthermore, the analysis unit can suggest easily accessible travel plans based on the user's geographical location. In addition, the analysis unit can suggest travel plans that optimize transportation methods and travel times, taking the user's geographical location into consideration. This allows for the suggestion of more relevant travel plans by analyzing data while considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location into a generating AI and have the generating AI perform a priority analysis of highly relevant data.

[0036] The analysis unit can analyze users' social media activity and acquire relevant data during the analysis process. For example, the analysis unit can analyze travel photos and posts shared by users on social media and suggest interesting tourist destinations and activities. It can also analyze posts from users' followers and friends on social media and suggest relevant travel plans. Furthermore, the analysis unit can analyze hashtags and keywords used by users on social media and suggest interesting travel plans. In this way, by analyzing users' social media activity, it is possible to suggest more relevant travel plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI acquire relevant data.

[0037] The suggestion section uses an AI that has learned the knowledge of professional guides to propose travel plans. For example, the AI ​​can suggest sightseeing spots and recommended routes based on the knowledge of professional guides. Furthermore, the AI ​​can suggest sightseeing spots, and users can enjoy their trip while listening to explanations from an avatar guide. In addition, the AI ​​can provide information about the history and culture of the sightseeing spots based on the knowledge of professional guides. This allows for the provision of higher-quality travel plans by having an AI based on the knowledge of professional guides propose travel plans. Some or all of the above processing in the suggestion section may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion section can input professional guide knowledge data into a generative AI and have the generative AI propose travel plans.

[0038] The suggestion unit provides the user with an avatar guide's explanation. The suggestion unit can provide the user with an avatar guide's explanation. For example, the suggestion unit can have the avatar guide explain the sights and history of a tourist spot. The suggestion unit can also have the avatar guide answer the user's questions in real time. Furthermore, the suggestion unit can have the avatar guide provide information customized according to the user's interests. In this way, by providing the avatar guide's explanation, the user can enjoy their trip more. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input avatar guide explanation data into a generative AI and have the generative AI perform the provision of the explanation.

[0039] The suggestion unit can propose the optimal plan by considering the congestion levels of tourist destinations and weather information. For example, the suggestion unit can avoid tourist destinations that are expected to be crowded and suggest less crowded times and dates. In addition, based on weather information, the suggestion unit can suggest indoor tourist destinations and activities in case of rain. Furthermore, based on weather information, the suggestion unit can suggest outdoor tourist destinations and activities in case of sunny weather. In this way, by considering the congestion levels of tourist destinations and weather information, it is possible to propose a more appropriate travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information on the congestion levels of tourist destinations and weather information into a generating AI and have the generating AI execute the proposal of the optimal plan.

[0040] The suggestion unit can customize its suggestions based on the user's past travel plan evaluations. For example, it can prioritize suggesting tourist destinations and activities that the user has previously given high ratings to. It can also avoid suggesting tourist destinations and activities that the user has previously given low ratings to. Furthermore, based on the user's past travel plan evaluations, the suggestion unit can suggest new tourist destinations and activities that might interest the user. In this way, by customizing the suggestions based on the user's past travel plan evaluations, it can propose more appropriate travel plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past travel plan evaluation data into a generating AI and have the generating AI perform the customization of the suggestions.

[0041] The suggestion unit can propose the most suitable tourist destinations by considering the user's geographical location. For example, the suggestion unit can prioritize suggesting tourist destinations and activities close to the user's current location. It can also propose easily accessible travel plans based on the user's geographical location. Furthermore, the suggestion unit can propose travel plans that optimize transportation methods and travel times by considering the user's geographical location. This allows for the suggestion of more appropriate tourist destinations by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI propose the most suitable tourist destinations.

[0042] The suggestion unit can analyze the user's social media activity and suggest relevant tourist destinations and activities when making suggestions. For example, the suggestion unit can analyze travel photos and posts shared by the user on social media and suggest interesting tourist destinations and activities. It can also analyze posts from the user's followers and friends on social media and suggest relevant travel plans. Furthermore, the suggestion unit can analyze hashtags and keywords used by the user on social media and suggest interesting travel plans. In this way, by analyzing the user's social media activity, it is possible to suggest more relevant travel plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant tourist destinations and activities.

[0043] The reservation department makes reservations in cooperation with a generation AI and other AI systems. The reservation department can make reservations in cooperation with a generation AI and other AI systems. For example, the reservation department can automatically make reservations for restaurants and accommodations suggested by the generation AI in cooperation with other AI systems. The reservation department can also automatically make reservations for activities suggested by the generation AI in cooperation with other AI systems. Furthermore, the reservation department can automatically make reservations for transportation suggested by the generation AI in cooperation with other AI systems. This improves the efficiency of reservations by having the generation AI and other AI systems work together. Some or all of the above processes in the reservation department may be performed using a generation AI, for example, or without a generation AI. For example, the reservation department can input reservation data suggested by a generation AI into another AI system and have the other AI system execute the reservation.

[0044] The reservation department makes reservations for services. The reservation department can make reservations for services. For example, the reservation department can make reservations for accommodations. The reservation department can also make reservations for transportation. Furthermore, the reservation department can also make reservations for activities. This allows users to prepare for their trip without hassle by making service reservations. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input service reservation data into a generating AI and have the generating AI execute the reservation.

[0045] The reservation department can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department may prioritize suggesting reservation methods that the user has used in the past (online, telephone, etc.). The reservation department can also suggest reservation methods tailored to specific time slots or dates based on the user's past reservation history. Furthermore, the reservation department can analyze the user's past reservation history and suggest the most efficient reservation method. This allows for the selection of a more appropriate reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's past reservation history data into a generating AI and have the generating AI select the optimal reservation method.

[0046] The reservation unit can suggest the optimal reservation timing when a reservation is made, taking into account the user's current schedule. For example, the reservation unit can refer to the user's calendar information to suggest the optimal reservation timing. The reservation unit can also make a reservation during an available time slot based on the user's current schedule. Furthermore, the reservation unit can suggest the optimal reservation timing in accordance with the user's schedule. This allows for the suggestion of a more appropriate reservation timing by considering the user's current schedule. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not. For example, the reservation unit can input the user's schedule data into a generating AI and have the generating AI suggest the optimal reservation timing.

[0047] The reservation department can suggest the most suitable reservation destination when a reservation is made, taking into account the user's geographical location. For example, the reservation department can prioritize suggesting restaurants or accommodations close to the user's current location. It can also suggest easily accessible reservation destinations based on the user's geographical location. Furthermore, the reservation department can suggest reservation destinations that optimize transportation and travel time, taking into account the user's geographical location. This allows for the suggestion of more appropriate reservation destinations by considering the user's geographical location. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's geographical location information into a generating AI and have the generating AI suggest the most suitable reservation destination.

[0048] The booking department can analyze a user's social media activity during the booking process and suggest relevant booking options. For example, it can analyze travel photos and posts shared by the user on social media and suggest restaurants and accommodations that might interest them. It can also analyze posts from the user's followers and friends on social media and suggest relevant booking options. Furthermore, it can analyze hashtags and keywords used by the user on social media and suggest booking options that might interest them. This allows for the suggestion of more relevant booking options by analyzing the user's social media activity. Some or all of the above processes in the booking department may be performed using AI, for example, or not. For example, the booking department can input the user's social media activity data into a generating AI and have the generating AI suggest relevant booking options.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analytics department can consider a user's current health status when analyzing their interests and usage history. For example, if a user enters their health checkup results, the analytics department can use that data to suggest a health-conscious travel plan. Specifically, if a user has high blood pressure, it can suggest restaurants offering low-salt meals or relaxing hot spring resorts. If a user feels they are not getting enough exercise, it can suggest a plan that includes activities such as hiking or cycling. Furthermore, if a user is feeling stressed, it can suggest a plan that includes relaxation or mindfulness sessions. This allows the system to provide the optimal travel plan tailored to the user's health condition.

[0051] The suggestion function can customize suggestions based on the user's past travel plan ratings. For example, it can prioritize suggesting tourist destinations and activities that the user has previously given high ratings to. It can also avoid suggesting tourist destinations and activities that the user has previously given low ratings to. Furthermore, it can suggest new tourist destinations and activities that might interest the user based on their past travel plan ratings. This allows the system to provide an optimal travel plan that reflects the user's past evaluations.

[0052] The proposal department can suggest the most suitable plan when making a proposal, taking into account the crowd situation at tourist destinations and weather information. For example, it can avoid tourist destinations that are expected to be crowded and suggest less crowded times and dates. Also, based on weather information, it can suggest indoor tourist destinations and activities in case of rain. Furthermore, based on weather information, it can suggest outdoor tourist destinations and activities in case of sunny weather. In this way, it can provide the optimal travel plan that takes into account the crowd situation at tourist destinations and weather information.

[0053] The reservation system can select the most suitable reservation method by referring to the user's past reservation history. For example, it can prioritize suggesting reservation methods the user has used in the past (online, telephone, etc.). It can also suggest reservation methods tailored to specific time slots or dates based on the user's past reservation history. Furthermore, it can analyze the user's past reservation history and suggest the most efficient reservation method. This allows the system to provide the most optimal reservation method based on the user's past reservation history.

[0054] The reservation system can suggest the most suitable reservation options by considering the user's geographical location during the reservation process. For example, it can prioritize suggesting restaurants and accommodations close to the user's current location. It can also suggest easily accessible reservation options based on the user's geographical location. Furthermore, it can suggest reservation options that optimize transportation and travel time, taking the user's geographical location into consideration. This allows the system to provide the most suitable reservation options, taking the user's geographical location into account.

[0055] The booking department can analyze a user's social media activity during the booking process and suggest relevant booking options. For example, it can analyze travel photos and posts shared by the user on social media to suggest restaurants and accommodations that might interest them. It can also analyze posts from the user's followers and friends on social media to suggest relevant booking options. Furthermore, it can analyze hashtags and keywords used by the user on social media to suggest booking options that might interest them. This allows the system to provide the most suitable booking options based on an analysis of the user's social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analytics department analyzes user interests and usage history. Specifically, it collects users' past travel and booking history, and uses AI to analyze it to understand user interests and preferences. For example, it collects data on tourist destinations visited and restaurants used by users in the past, and uses AI to analyze it. Step 2: The proposal department proposes travel plans based on the results analyzed by the analysis department. Specifically, an AI that has learned the knowledge of professional guides suggests sightseeing spots and recommended routes. For example, users can enjoy their trip while listening to explanations from an avatar guide. Step 3: The reservation department makes reservations based on the plans proposed by the proposal department. Specifically, the generation AI and other AI systems work together to automatically make reservations for the proposed restaurants and accommodations. For example, reservations for restaurants and accommodations proposed by the AI ​​can be made automatically through the collaboration of the generation AI and other AI systems.

[0058] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system that analyzes a user's interests and usage history, proposes an optimal travel plan, and makes reservations. The travel plan suggestion system uses AI to analyze the user's interests and usage history of the electronic payment system and proposes an optimal travel plan. Next, the AI, which has learned the knowledge and experience of professional guides, provides a travel plan, and the user can enjoy the trip while listening to explanations from an avatar guide. In addition, the system can also make reservations for services through the cooperation of the generating AI with other AI systems. This mechanism makes it possible to create a travel plan quickly and enjoyably and to use it at a discount. First, the AI ​​analyzes the user's interests and usage history of the electronic payment system. At this time, data on places the user has visited and services used in the past is collected and analyzed by the AI ​​to understand the user's interests and preferences. For example, by collecting data on tourist destinations the user has visited and restaurants they have used in the past and analyzing it, the AI ​​can propose an optimal travel plan to the user. Next, the AI, which has learned the knowledge and experience of professional guides, provides a travel plan. For example, the AI ​​can suggest sightseeing spots and recommended routes based on the knowledge of professional guides. As a result, the user can enjoy the trip while listening to explanations from an avatar guide. Furthermore, by integrating the generating AI with other AI systems, service reservations can also be made. For example, reservations for restaurants and accommodations suggested by the AI ​​can be automatically made through the integration of the generating AI with other AI systems. This allows users to prepare for their trips without hassle. This system enables users to quickly and enjoyably create travel plans and take advantage of great deals. Users can intuitively create travel plans and enjoy their trips without complex operations. In addition, by utilizing the deals suggested by the AI, users can maximize their travel budget. For example, they can save on travel expenses by using coupons and points suggested by the AI. In this way, the AI-powered travel plan creation and reservation function enables users to quickly and enjoyably create travel plans and take advantage of great deals. Please take advantage of the AI ​​suggestion and reservation function and enjoy a wonderful trip.This allows the travel plan suggestion system to propose and book the most suitable travel plan based on the user's interests and usage history.

[0059] The travel plan suggestion system according to this embodiment comprises an analysis unit, a suggestion unit, and a reservation unit. The analysis unit analyzes the user's interests and usage history. For example, the analysis unit collects the user's past travel history and reservation history, and the AI ​​analyzes it to understand the user's interests and preferences. For example, the analysis unit collects data on tourist destinations the user has visited and restaurants they have used in the past, and the AI ​​analyzes it to suggest the optimal travel plan for the user. The suggestion unit has an AI that has learned the knowledge of professional guides to suggest a travel plan. For example, the suggestion unit has an AI that, based on the knowledge of professional guides, suggests the highlights of tourist destinations and recommended routes. For example, the suggestion unit has an AI that suggests the highlights of tourist destinations, and the user can enjoy the trip while listening to explanations from an avatar guide. The reservation unit makes reservations based on the plan suggested by the suggestion unit. For example, the reservation unit can automatically make reservations for suggested restaurants and accommodations by having a generating AI and other AI systems work together. For example, the reservation unit can automatically make reservations for restaurants and accommodations suggested by the AI ​​through the cooperation of a generating AI and other AI systems. As a result, the travel plan suggestion system according to the embodiment can suggest the optimal travel plan based on the user's interests and usage history, and make reservations.

[0060] The analytics department analyzes user interests and usage history. Specifically, it collects users' past travel and booking history, and uses AI to analyze it to understand user interests and preferences. For example, it collects data on tourist destinations and restaurants users have visited in the past, and the AI ​​analyzes this data to suggest the most suitable travel plan for the user. The AI ​​uses natural language processing technology to analyze user reviews and comments to identify activities and tourist destinations that users are particularly interested in. It also analyzes users' social media posts and photos to gain a more detailed understanding of user preferences and interests. Furthermore, the AI ​​analyzes users' past travel patterns and seasonal travel trends to predict their next travel destination. For example, if a user has visited a beach resort in the summer in the past, it can suggest other beach resorts as their next summer travel destination. In this way, the analytics department can comprehensively analyze diverse user data and understand users' interests and preferences with high accuracy.

[0061] The proposal department uses AI, trained on the knowledge of professional guides, to suggest travel plans. Specifically, the AI ​​uses the knowledge of professional guides to suggest sightseeing spots and recommended routes. For example, the AI ​​learns information about the history, culture, and local specialties of tourist destinations and provides users with detailed explanations. Furthermore, the AI ​​creates customized travel plans based on the user's interests. For example, if a user is interested in historical buildings, the AI ​​will suggest a plan centered on historical tourist spots in that area. The AI ​​also supports real-time information updates and can resuggest optimal routes and sightseeing spots in response to changes in weather and traffic conditions. Users can enjoy their trip while listening to explanations from their avatar guide, experiencing it as if they were traveling with a local professional guide. In this way, the proposal department can provide users with high-quality, personalized travel plans and improve their travel satisfaction.

[0062] The reservation department makes reservations based on plans proposed by the suggestion department. Specifically, the generation AI and other AI systems work together to automatically make reservations for suggested restaurants and accommodations. For example, reservations for restaurants and accommodations suggested by the AI ​​can be automatically made through the collaboration of the generation AI and other AI systems. The generation AI considers the user's desired date and time, budget, and special requests to select the optimal reservation option. The reservation department can also search across multiple reservation sites and services to find the best deal. Furthermore, the reservation department automates procedures such as reservation confirmation, modification, and cancellation, significantly reducing the burden on the user. For example, if a user needs to cancel a reservation due to a sudden change in plans, the AI ​​will automatically handle the cancellation process, minimizing the cost of cancellation fees. The reservation department also manages the user's reservation history to ensure smooth reservations for future trips. In this way, the reservation department provides users with a fast and efficient reservation service, allowing them to plan their trips smoothly.

[0063] The analysis unit can estimate the user's emotions and adjust the analysis method of interests and usage history based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple interface and minimize input steps. If the user is relaxed, the analysis unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the analysis unit can prioritize voice input to allow for quick input of origin and destination. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0064] The analysis unit can analyze a user's past travel history in detail and extract data to propose the optimal travel plan. For example, the analysis unit can collect data on tourist destinations the user has visited in the past, and have AI analyze it to understand the user's interests and preferences. The analysis unit can also collect data on restaurants and accommodations the user has used in the past, and have AI analyze it to propose the optimal travel plan for the user. Furthermore, the analysis unit can extract data related to specific seasons or events from the user's past travel history and propose the optimal travel plan. In this way, by analyzing past travel history in detail, the analysis unit can propose the optimal travel plan for the user. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's past travel history data into a generating AI and have the generating AI perform the extraction of data to propose the optimal travel plan.

[0065] The analysis unit can filter data based on the user's current lifestyle and areas of interest during analysis. For example, the analysis unit can suggest relevant travel plans based on the user's current occupation and lifestyle. It can also suggest interesting tourist destinations and activities based on the user's current hobbies and areas of interest. Furthermore, the analysis unit can suggest appropriate travel plans based on the user's current health status and fitness level. By filtering data based on the user's current lifestyle and areas of interest, it is possible to suggest more relevant travel plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0066] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize suggesting exciting activities. If the user is relaxed, the analysis unit can also prioritize suggesting relaxing tourist destinations and activities. Furthermore, if the user is stressed, the analysis unit can prioritize suggesting travel plans that help relieve stress. By prioritizing the analysis results according to the user's emotions, a more appropriate travel plan can be suggested. 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 AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location during the analysis process. For example, the analysis unit can prioritize suggesting tourist destinations and activities close to the user's current location. Furthermore, the analysis unit can suggest easily accessible travel plans based on the user's geographical location. In addition, the analysis unit can suggest travel plans that optimize transportation methods and travel times, taking the user's geographical location into consideration. This allows for the suggestion of more relevant travel plans by analyzing data while considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location into a generating AI and have the generating AI perform a priority analysis of highly relevant data.

[0068] The analysis unit can analyze users' social media activity and acquire relevant data during the analysis process. For example, the analysis unit can analyze travel photos and posts shared by users on social media and suggest interesting tourist destinations and activities. It can also analyze posts from users' followers and friends on social media and suggest relevant travel plans. Furthermore, the analysis unit can analyze hashtags and keywords used by users on social media and suggest interesting travel plans. In this way, by analyzing users' social media activity, it is possible to suggest more relevant travel plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input user social media activity data into a generating AI and have the generating AI acquire relevant data.

[0069] The suggestion section uses an AI that has learned the knowledge of professional guides to propose travel plans. For example, the AI ​​can suggest sightseeing spots and recommended routes based on the knowledge of professional guides. Furthermore, the AI ​​can suggest sightseeing spots, and users can enjoy their trip while listening to explanations from an avatar guide. In addition, the AI ​​can provide information about the history and culture of the sightseeing spots based on the knowledge of professional guides. This allows for the provision of higher-quality travel plans by having an AI based on the knowledge of professional guides propose travel plans. Some or all of the above processing in the suggestion section may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion section can input professional guide knowledge data into a generative AI and have the generative AI propose travel plans.

[0070] The suggestion unit provides the user with an avatar guide's explanation. The suggestion unit can provide the user with an avatar guide's explanation. For example, the suggestion unit can have the avatar guide explain the sights and history of a tourist spot. The suggestion unit can also have the avatar guide answer the user's questions in real time. Furthermore, the suggestion unit can have the avatar guide provide information customized according to the user's interests. In this way, by providing the avatar guide's explanation, the user can enjoy their trip more. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input avatar guide explanation data into a generative AI and have the generative AI perform the provision of the explanation.

[0071] The suggestion unit can estimate the user's emotions and adjust how it suggests travel plans based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the suggestion unit can also suggest a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the suggestion unit can suggest a travel plan with visually stimulating effects. By adjusting the suggestion method according to the user's emotions, a more appropriate travel plan can be suggested. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The suggestion unit can propose the optimal plan by considering the congestion levels of tourist destinations and weather information. For example, the suggestion unit can avoid tourist destinations that are expected to be crowded and suggest less crowded times and dates. In addition, based on weather information, the suggestion unit can suggest indoor tourist destinations and activities in case of rain. Furthermore, based on weather information, the suggestion unit can suggest outdoor tourist destinations and activities in case of sunny weather. In this way, by considering the congestion levels of tourist destinations and weather information, it is possible to propose a more appropriate travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information on the congestion levels of tourist destinations and weather information into a generating AI and have the generating AI execute the proposal of the optimal plan.

[0073] The suggestion unit can customize its suggestions based on the user's past travel plan evaluations. For example, it can prioritize suggesting tourist destinations and activities that the user has previously given high ratings to. It can also avoid suggesting tourist destinations and activities that the user has previously given low ratings to. Furthermore, based on the user's past travel plan evaluations, the suggestion unit can suggest new tourist destinations and activities that might interest the user. In this way, by customizing the suggestions based on the user's past travel plan evaluations, it can propose more appropriate travel plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past travel plan evaluation data into a generating AI and have the generating AI perform the customization of the suggestions.

[0074] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is excited, the suggestion unit will prioritize suggesting exciting activities. If the user is relaxed, the suggestion unit can also prioritize suggesting relaxing tourist destinations and activities. Furthermore, if the user is stressed, the suggestion unit can prioritize suggesting travel plans that help relieve stress. By prioritizing suggestions according to the user's emotions, a more appropriate travel plan can be suggested. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The suggestion unit can propose the most suitable tourist destinations by considering the user's geographical location. For example, the suggestion unit can prioritize suggesting tourist destinations and activities close to the user's current location. It can also propose easily accessible travel plans based on the user's geographical location. Furthermore, the suggestion unit can propose travel plans that optimize transportation methods and travel times by considering the user's geographical location. This allows for the suggestion of more appropriate tourist destinations by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI propose the most suitable tourist destinations.

[0076] The suggestion unit can analyze the user's social media activity and suggest relevant tourist destinations and activities when making suggestions. For example, the suggestion unit can analyze travel photos and posts shared by the user on social media and suggest interesting tourist destinations and activities. It can also analyze posts from the user's followers and friends on social media and suggest relevant travel plans. Furthermore, the suggestion unit can analyze hashtags and keywords used by the user on social media and suggest interesting travel plans. In this way, by analyzing the user's social media activity, it is possible to suggest more relevant travel plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant tourist destinations and activities.

[0077] The reservation department makes reservations in cooperation with a generation AI and other AI systems. The reservation department can make reservations in cooperation with a generation AI and other AI systems. For example, the reservation department can automatically make reservations for restaurants and accommodations suggested by the generation AI in cooperation with other AI systems. The reservation department can also automatically make reservations for activities suggested by the generation AI in cooperation with other AI systems. Furthermore, the reservation department can automatically make reservations for transportation suggested by the generation AI in cooperation with other AI systems. This improves the efficiency of reservations by having the generation AI and other AI systems work together. Some or all of the above processes in the reservation department may be performed using a generation AI, for example, or without a generation AI. For example, the reservation department can input reservation data suggested by a generation AI into another AI system and have the other AI system execute the reservation.

[0078] The reservation department makes reservations for services. The reservation department can make reservations for services. For example, the reservation department can make reservations for accommodations. The reservation department can also make reservations for transportation. Furthermore, the reservation department can also make reservations for activities. This allows users to prepare for their trip without hassle by making service reservations. Some or all of the above processes in the reservation department may be performed using AI, for example, or not using AI. For example, the reservation department can input service reservation data into a generating AI and have the generating AI execute the reservation.

[0079] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if the user is excited, the booking system might prioritize booking exciting activities. If the user is relaxed, it might prioritize booking relaxing tourist destinations or activities. Furthermore, if the user is stressed, it might prioritize booking travel plans that help relieve stress. This allows for more appropriate bookings by prioritizing bookings according to 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 booking system may be performed using AI or not. For example, the booking system could input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reservation department can select the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department may prioritize suggesting reservation methods that the user has used in the past (online, telephone, etc.). The reservation department can also suggest reservation methods tailored to specific time slots or dates based on the user's past reservation history. Furthermore, the reservation department can analyze the user's past reservation history and suggest the most efficient reservation method. This allows for the selection of a more appropriate reservation method by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's past reservation history data into a generating AI and have the generating AI select the optimal reservation method.

[0081] The reservation unit can suggest the optimal reservation timing when a reservation is made, taking into account the user's current schedule. For example, the reservation unit can refer to the user's calendar information to suggest the optimal reservation timing. The reservation unit can also make a reservation during an available time slot based on the user's current schedule. Furthermore, the reservation unit can suggest the optimal reservation timing in accordance with the user's schedule. This allows for the suggestion of a more appropriate reservation timing by considering the user's current schedule. Some or all of the above processes in the reservation unit may be performed using AI, for example, or not. For example, the reservation unit can input the user's schedule data into a generating AI and have the generating AI suggest the optimal reservation timing.

[0082] The reservation unit can estimate the user's emotions and adjust the way reservations are displayed based on the estimated emotions. For example, if the user is nervous, the reservation unit can provide a simple and highly visible display. If the user is relaxed, it can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. By adjusting the way reservations are displayed according to the user's emotions, more appropriate reservation information can be provided. 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 reservation unit may be performed using AI, or not using AI. For example, the reservation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reservation department can suggest the most suitable reservation destination when a reservation is made, taking into account the user's geographical location. For example, the reservation department can prioritize suggesting restaurants or accommodations close to the user's current location. It can also suggest easily accessible reservation destinations based on the user's geographical location. Furthermore, the reservation department can suggest reservation destinations that optimize transportation and travel time, taking into account the user's geographical location. This allows for the suggestion of more appropriate reservation destinations by considering the user's geographical location. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's geographical location information into a generating AI and have the generating AI suggest the most suitable reservation destination.

[0084] The booking department can analyze a user's social media activity during the booking process and suggest relevant booking options. For example, it can analyze travel photos and posts shared by the user on social media and suggest restaurants and accommodations that might interest them. It can also analyze posts from the user's followers and friends on social media and suggest relevant booking options. Furthermore, it can analyze hashtags and keywords used by the user on social media and suggest booking options that might interest them. This allows for the suggestion of more relevant booking options by analyzing the user's social media activity. Some or all of the above processes in the booking department may be performed using AI, for example, or not. For example, the booking department can input the user's social media activity data into a generating AI and have the generating AI suggest relevant booking options.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analytics department can consider a user's current health status when analyzing their interests and usage history. For example, if a user enters their health checkup results, the analytics department can use that data to suggest a health-conscious travel plan. Specifically, if a user has high blood pressure, it can suggest restaurants offering low-salt meals or relaxing hot spring resorts. If a user feels they are not getting enough exercise, it can suggest a plan that includes activities such as hiking or cycling. Furthermore, if a user is feeling stressed, it can suggest a plan that includes relaxation or mindfulness sessions. This allows the system to provide the optimal travel plan tailored to the user's health condition.

[0087] The analytics department can estimate the user's emotions and adjust the suggested travel plans based on those emotions. For example, if the user is excited, it can suggest a plan that includes adventure or extreme sports. If the user is relaxed, it can suggest a plan that includes spas or relaxation facilities. Furthermore, if the user is sad, it can suggest a plan that includes entertainment or visits to art galleries to lift their spirits. This allows the system to provide the optimal travel plan tailored to the user's emotions.

[0088] The suggestion function can customize suggestions based on the user's past travel plan ratings. For example, it can prioritize suggesting tourist destinations and activities that the user has previously given high ratings to. It can also avoid suggesting tourist destinations and activities that the user has previously given low ratings to. Furthermore, it can suggest new tourist destinations and activities that might interest the user based on their past travel plan ratings. This allows the system to provide an optimal travel plan that reflects the user's past evaluations.

[0089] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is excited, it can prioritize suggesting exciting activities. If the user is relaxed, it can prioritize suggesting relaxing tourist destinations and activities. Furthermore, if the user is stressed, it can prioritize suggesting travel plans that help relieve stress. This allows the system to provide the optimal travel plan tailored to the user's emotions.

[0090] The proposal department can suggest the most suitable plan when making a proposal, taking into account the crowd situation at tourist destinations and weather information. For example, it can avoid tourist destinations that are expected to be crowded and suggest less crowded times and dates. Also, based on weather information, it can suggest indoor tourist destinations and activities in case of rain. Furthermore, based on weather information, it can suggest outdoor tourist destinations and activities in case of sunny weather. In this way, it can provide the optimal travel plan that takes into account the crowd situation at tourist destinations and weather information.

[0091] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if a user is excited, it can prioritize booking exciting activities. If a user is relaxed, it can prioritize booking relaxing tourist destinations and activities. Furthermore, if a user is stressed, it can prioritize booking travel plans that help relieve stress. This allows the system to provide optimal bookings tailored to the user's emotions.

[0092] The reservation system can select the most suitable reservation method by referring to the user's past reservation history. For example, it can prioritize suggesting reservation methods the user has used in the past (online, telephone, etc.). It can also suggest reservation methods tailored to specific time slots or dates based on the user's past reservation history. Furthermore, it can analyze the user's past reservation history and suggest the most efficient reservation method. This allows the system to provide the most optimal reservation method based on the user's past reservation history.

[0093] The reservation system can estimate the user's emotions and adjust how reservations are displayed based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. This allows the system to provide the most appropriate reservation information display tailored to the user's emotions.

[0094] The reservation system can suggest the most suitable reservation options by considering the user's geographical location during the reservation process. For example, it can prioritize suggesting restaurants and accommodations close to the user's current location. It can also suggest easily accessible reservation options based on the user's geographical location. Furthermore, it can suggest reservation options that optimize transportation and travel time, taking the user's geographical location into consideration. This allows the system to provide the most suitable reservation options, taking the user's geographical location into account.

[0095] The booking department can analyze a user's social media activity during the booking process and suggest relevant booking options. For example, it can analyze travel photos and posts shared by the user on social media to suggest restaurants and accommodations that might interest them. It can also analyze posts from the user's followers and friends on social media to suggest relevant booking options. Furthermore, it can analyze hashtags and keywords used by the user on social media to suggest booking options that might interest them. This allows the system to provide the most suitable booking options based on an analysis of the user's social media activity.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analytics department analyzes user interests and usage history. Specifically, it collects users' past travel and booking history, and uses AI to analyze it to understand user interests and preferences. For example, it collects data on tourist destinations visited and restaurants used by users in the past, and uses AI to analyze it. Step 2: The proposal department proposes travel plans based on the results analyzed by the analysis department. Specifically, an AI that has learned the knowledge of professional guides suggests sightseeing spots and recommended routes. For example, users can enjoy their trip while listening to explanations from an avatar guide. Step 3: The reservation department makes reservations based on the plans proposed by the proposal department. Specifically, the generation AI and other AI systems work together to automatically make reservations for the proposed restaurants and accommodations. For example, reservations for restaurants and accommodations proposed by the AI ​​can be made automatically through the collaboration of the generation AI and other AI systems.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the analysis unit, proposal unit, and reservation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's interests and usage history. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes a travel plan based on the knowledge of a professional guide. The reservation unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and makes a reservation based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the analysis unit, proposal unit, and reservation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's interests and usage history. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes a travel plan based on the knowledge of a professional guide. The reservation unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and makes a reservation based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the analysis unit, proposal unit, and reservation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's interests and usage history. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and proposes a travel plan based on the knowledge of a professional guide. The reservation unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and makes a reservation based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, proposal unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and analyzes the user's interests and usage history. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes a travel plan based on the knowledge of a professional guide. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and makes a reservation based on the proposed plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The analytics department analyzes user interests and usage history, Based on the results of the analysis conducted by the aforementioned analysis unit, the proposal unit proposes a travel plan. The system includes a reservation unit that makes reservations based on the plan proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis of interests and usage history based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the user's past travel history in detail and extract data to suggest the optimal travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is During analysis, data is filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, AI that has learned from professional guides will suggest travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, Provides users with avatar guide explanations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, The system estimates the user's emotions and adjusts how travel plans are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making a proposal, we will consider factors such as the crowd situation at tourist destinations and weather conditions to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making suggestions, the suggestions are customized based on the user's evaluation of past travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, we propose the most suitable tourist destinations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and propose relevant tourist destinations and activities. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reservation section is, The generation AI and other AI systems work together to make reservations. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reservation section is, Make a reservation for the service The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned reservation section is, When a reservation is made, the system will refer to 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 20) The aforementioned reservation section is, When you make a reservation, we will suggest the best time to book, taking into account your current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, The system estimates the user's emotions and adjusts how reservations are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, When making a reservation, we will suggest the most suitable reservation location considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When you make a reservation, we analyze your social media activity and suggest relevant booking options. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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. The analytics department analyzes user interests and usage history, Based on the results of the analysis conducted by the aforementioned analysis unit, the proposal unit proposes a travel plan. The system includes a reservation unit that makes reservations based on the plan proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit is We estimate user sentiment and adjust the analysis of interests and usage history based on the estimated user sentiment. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the user's past travel history in detail and extract data to suggest the optimal travel plan. The system according to feature 1.

4. The aforementioned analysis unit is During analysis, data is filtered based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned analysis unit is During the analysis, we analyze users' social media activity and obtain relevant data. The system according to feature 1.

8. The aforementioned proposal section is, AI that has learned from professional guides proposes travel plans. The system according to feature 1.

Images