System

Abstract

To provide a system for efficiently performing a range of jobs from planning of a journey to reservation and to navigation.SOLUTION: A system according to an embodiment of the present invention comprises a reception unit, a generation unit, a proposition unit, a reservation unit, and a navigation unit. The reception unit receives basic information from a user. The generation unit analyzes the information received by the reception unit and creates an efficient itinerary table. The proposition unit proposes stop-by destinations other than desired sightseeing spots on the basis of the itinerary table created by the generation unit. The reservation unit inquires about vacancies for which reservation is required and makes a reservation. The navigation unit performs navigation in linkage with a map application.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology makes it difficult to plan trips efficiently, and reservations and navigation in particular can be time-consuming.

[0005] The system according to the embodiment aims to efficiently carry out a process from planning a trip to booking and navigation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a proposal unit, a reservation unit, and a navigation unit. The reception unit accepts basic information from a user. The generation unit analyzes the information accepted by the reception unit and creates an efficient itinerary. The proposal unit proposes stopovers other than the desired tourist spots based on the itinerary created by the generation unit. The reservation unit checks availability of items that require reservations and makes reservations. The navigation unit performs navigation in conjunction with a map application. Effect of the Invention

[0007] The system according to the embodiment can efficiently carry out a process from planning a trip to booking and navigation. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A travel planning system according to an embodiment of the present invention is a system that provides an optimal itinerary by allowing a user to input basic information such as the schedule, transportation (airline, bullet train, car, etc.), accommodation, desired sightseeing spots, and what to eat. In this system, the user inputs basic information such as the schedule, transportation, accommodation, desired sightseeing spots, and what to eat. For example, the user inputs "Travel from March 1st to March 5th, transportation is bullet train, accommodation is Tokyo, desired sightseeing spot is sightseeing spot A, and what to eat is food B." This information is input to AI. Next, AI analyzes the input information and creates an optimal itinerary. Based on information such as the schedule, transportation, accommodation, desired sightseeing spot, and what to eat, AI calculates an efficient route and creates a schedule for each day. For example, on March 1st, you visit sightseeing spot A, and on March 2nd, you visit restaurant C to eat food B. Furthermore, if there is room in the itinerary, AI suggests recommended stops other than the desired sightseeing spots. For example, after visiting sightseeing spot A, it suggests visiting nearby sightseeing spot D. In this way, a fulfilling travel plan is provided for the user. In addition, for things that require reservations, such as restaurants and experiences, the AI ​​checks availability. For example, it checks the availability of restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if a reservation is available. This allows the user to complete the reservation without any hassle. Finally, on the day, it links with a map app to provide navigation. For example, when a user heads to tourist spot A, the map app displays the route from the user's current location to tourist spot A and performs navigation. This allows the user to reach the destination without getting lost. This mechanism allows the user to easily create the optimal travel plan and enjoy a fulfilling trip. As a result, the travel planning system can streamline the user's travel planning and provide a fulfilling travel experience.

[0029] The travel planning system according to the embodiment includes a reception unit, a generation unit, a proposal unit, a reservation unit, and a navigation unit. The reception unit receives basic information from a user. The basic information includes, for example, a schedule, a means of transportation (air, bullet train, car, etc.), a place to stay, a desired sightseeing spot, and a desired food, but is not limited to such examples. For example, the user inputs "Travel from March 1 to March 5, a means of transportation is a bullet train, a place to stay is Tokyo, a desired sightseeing spot is tourist spot A, and a desired food is food B" into the reception unit. This information is input to the AI. The generation unit uses the generation AI to analyze the information received by the reception unit and create an optimal itinerary. The generation unit calculates an efficient route based on information such as the schedule, a means of transportation, a place to stay, a desired sightseeing spot, and a desired food, and creates a schedule for each day. For example, the generation AI uses a text generation AI (for example, LLM) to calculate an efficient route and create a schedule for each day. The generation unit can also use the multimodal generation AI to calculate efficient routes and create a schedule for each day. The suggestion unit suggests recommended stopovers other than the desired tourist spot based on the itinerary created by the generation unit. For example, the suggestion unit suggests visiting a nearby tourist spot D after visiting tourist spot A. The suggestion unit can also use the generation AI to suggest stopovers based on the user's preferences. The reservation unit inquires about availability and makes reservations for things that require reservations, such as restaurants and experiences. For example, the reservation unit checks the availability of restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if a reservation is possible. The reservation unit can also use the generation AI to search multiple reservation sites across multiple sites and provide optimal reservation options. The navigation unit performs navigation in conjunction with a map app. For example, when a user heads to tourist spot A, the map app displays a route from the user's current location to tourist spot A and performs navigation. The navigation unit can also use the generation AI to estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions. As a result, the travel planning system according to the embodiment can improve the efficiency of the user's travel planning and provide a fulfilling travel experience.

[0030] The reception unit receives basic information from the user. The basic information includes, for example, a schedule, a means of transportation (air, bullet train, car, etc.), a place of accommodation, a desired sightseeing spot, and a food to eat, but is not limited to such examples. For example, the user inputs "Travel from March 1 to March 5, a means of transportation is a bullet train, a place of accommodation is Tokyo, a desired sightseeing spot is tourist spot A, and a food to eat is food B" into the reception unit. This information is input to the AI. The reception unit provides an interface to accurately receive the information input by the user and properly process it within the system. For example, when a user inputs information through a website or a mobile app, the reception unit provides an intuitive and easy-to-use form so that the user can input all the necessary information. Furthermore, the reception unit checks the consistency of the input information, and if there is missing or contradictory information, it provides appropriate feedback to the user. For example, if there is an overlap in the schedule or if there is a contradiction between the means of transportation and the place of accommodation, an error message is displayed to the user to prompt the user to make corrections. The reception unit also has a function to save the user's input contents in real time and to allow the user to resume input even if it is interrupted midway. This allows the user to take their time to carefully plan their trip. Furthermore, the reception unit can store the user's past travel history and preferences, and refer to them when planning their next trip. For example, personalized suggestions can be made to the user based on information such as tourist spots visited in the past, accommodations, and food eaten. This allows the reception unit to flexibly respond to the user's needs, improving the efficiency of trip planning and satisfaction.

[0031] The generation unit uses the generation AI to analyze the information received by the reception unit and create an optimal itinerary. The generation unit calculates an efficient route based on information such as the schedule, transportation means, accommodation, desired sightseeing spots, and what to eat, and creates a schedule for each day. The generation AI uses a text generation AI (e.g., LLM) to calculate an efficient route and create a schedule for each day. The generation unit can also use a multimodal generation AI to calculate an efficient route and create a schedule for each day. The generation unit utilizes multiple data sources to generate an optimal itinerary based on the user's input information. For example, the generation unit obtains information such as transportation schedules, business hours of tourist spots, and restaurant reservation status in real time, and integrates this information to create an optimal schedule. The generation AI analyzes these data and proposes an itinerary that best suits the user's wishes. For example, when visiting tourist spot A, the least crowded time period is selected so that sightseeing can be enjoyed efficiently. The generation unit can also create a personalized itinerary taking into account the user's preferences and past travel history. For example, it will suggest new tourist spots and restaurants that the user might be interested in based on information about tourist spots visited in the past and food eaten. Furthermore, the generation unit can modify and improve the itinerary based on user feedback. For example, if a user wants to add a specific tourist spot or change the schedule, the generation AI will respond quickly and regenerate the optimal itinerary. This allows the generation unit to respond flexibly to user needs, resulting in more efficient travel planning and improved satisfaction.

[0032] The suggestion unit suggests recommended stopovers other than the desired tourist spot based on the itinerary created by the generation unit. For example, the suggestion unit suggests visiting a nearby tourist spot D after visiting tourist spot A. The suggestion unit can also suggest stopovers based on the user's preferences using the generation AI. The suggestion unit suggests tourist spots and activities that the user may be interested in based on the user's input information and past travel history. For example, if the user is interested in historical buildings, the suggestion unit suggests historical tourist spots in the area. Also, if the user wants to enjoy nature, the suggestion unit can suggest nearby parks and nature reserves. The suggestion unit uses the generation AI to analyze the user's preferences and interests and suggest optimal stopovers. For example, if the user wants to eat a specific dish, the suggestion unit can suggest restaurants and markets where the dish can be enjoyed. Furthermore, the suggestion unit suggests efficient stopovers taking into account the user's schedule and means of transportation. For example, the suggestion unit makes suggestions taking into account the means of transportation and travel time so that the user can move smoothly from tourist spot A to tourist spot D. This allows the user to enjoy sightseeing efficiently without wasting time. The suggestion unit can continuously improve the accuracy and effectiveness of the suggestions based on the user's feedback. For example, the suggestion unit can collect the user's impressions and evaluations after visiting the suggested tourist spots and reflect them in the next suggestions. This enables the suggestion unit to flexibly respond to the user's preferences, resulting in more efficient travel planning and improved satisfaction.

[0033] The reservation unit checks availability and makes reservations for restaurants and experiences that require reservations. For example, the reservation unit checks the availability of restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if a reservation is possible. The reservation unit can also use the generation AI to search multiple reservation sites across multiple reservation sites and provide optimal reservation options. The reservation unit works with multiple reservation sites and services to efficiently make reservations for restaurants and activities that users want. For example, it obtains availability in real time from restaurant reservation sites, experience activity reservation sites, hotel reservation sites, etc., and provides optimal reservation options. The generation AI analyzes this data and automatically makes a reservation that best suits the user's wishes. For example, if a user wants to make a reservation at a restaurant during a specific time period, it prioritizes searching for restaurants that have availability during that time period and makes a reservation. The reservation unit also makes efficient reservations by taking into account the user's schedule and means of transportation. For example, if a user wants to eat at restaurant C after visiting tourist attraction A, it suggests the optimal reservation time by taking into account the travel time from tourist attraction A to restaurant C. Furthermore, the reservation unit can continuously improve the accuracy and effectiveness of reservations based on user feedback. For example, it can collect ratings of restaurants and activities reserved by users and reflect them in the next reservation. This allows the reservation unit to flexibly respond to users' needs, making travel planning more efficient and increasing satisfaction.

[0034] The navigation unit performs navigation in conjunction with a map application. For example, when a user heads to a tourist spot A, the map application displays a route from the current location to tourist spot A and performs navigation. The navigation unit can also use the generation AI to estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions. The navigation unit provides the optimal route in real time so that the user can reach the destination smoothly. For example, the optimal route is calculated and guided to the user, taking into account traffic congestion, construction information, weather information, etc. The generation AI analyzes these data and provides the optimal route from the user's current location to the destination. In addition, the navigation unit can estimate the user's emotions and adjust the navigation display method. For example, if the user is tired, more concise and easy-to-understand guidance is provided, and if the user is relaxed, detailed guidance is provided. Furthermore, the navigation unit can provide personalized navigation taking into account the user's preferences and past movement history. For example, the navigation unit suggests routes and stops that the user is likely to like based on information on routes the user has used in the past and places visited. This enables the navigation unit to flexibly respond to the needs of the user, improving the efficiency of travel plans and satisfaction. Furthermore, the navigation unit can continuously improve the accuracy and effectiveness of navigation based on user feedback. For example, the user can rate the route provided and the next navigation can be based on that rating. This allows the navigation unit to provide the user with quick and reliable navigation and enrich the travel experience.

[0035] The suggestion unit can suggest a stopover based on the user's preferences. The suggestion unit identifies the user's preferences based on, for example, the user's past travel history or survey results, and suggests a stopover. For example, the suggestion unit analyzes data on tourist spots and restaurants visited by the user in the past, and suggests a stopover that matches the user's preferences. The suggestion unit can also suggest a stopover based on the interests and concerns answered by the user in the survey. This improves the satisfaction of the trip by suggesting a stopover according to the user's preferences. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's past travel history and survey results into the generation AI, and cause the generation AI to execute a stopover suggestion based on the user's preferences.

[0036] The reservation unit can search multiple reservation sites across the board. The reservation unit cross-searches multiple reservation sites, such as a hotel reservation site, an airline ticket reservation site, and a restaurant reservation site, and provides optimal reservation options. For example, the reservation unit obtains the availability of accommodations, transportation, and restaurants desired by the user from multiple reservation sites and compares and considers them. The reservation unit can also propose optimal reservation options based on the user's budget and desired conditions. This makes it possible to provide optimal reservation options by searching multiple reservation sites. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input data from multiple reservation sites into the generation AI and cause the generation AI to execute the proposal of optimal reservation options.

[0037] The generation unit can calculate an efficient route based on the information of the schedule, the means of transportation, the accommodation, the desired sightseeing spots, and the food to be eaten, and create a schedule for each day. The generation unit uses a generation AI to calculate an efficient route based on the information of the schedule, the means of transportation, the accommodation, the desired sightseeing spots, and the food to be eaten, and creates a schedule for each day. For example, the generation unit analyzes the information of the schedule, the means of transportation, the accommodation, the desired sightseeing spots, and the food to be eaten input by the user, and calculates an efficient route. The generation unit creates a schedule for each day, for example, taking into consideration minimizing travel time and the optimal order of sightseeing spots. The generation unit can also adjust the schedule based on the user's preferences using the generation AI. For example, if the user desires a relaxing trip, the generation unit creates a relaxed schedule. In addition, if the user desires an efficient trip, the generation unit can also create a schedule that emphasizes the shortest route. As a result, the travel plan can be optimized by calculating an efficient route and creating a schedule for each day. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input user input information to the generation AI and cause the generation AI to calculate efficient routes and create schedules.

[0038] The navigation unit can display a route from the current location to the destination and perform navigation. The navigation unit, for example, works in conjunction with a map application to display a route from the current location to the destination and perform navigation. For example, when a user heads to a tourist spot A, the map application displays a route from the current location to the tourist spot A and performs navigation. The navigation unit acquires the user's current location based on GPS information or a manual input by the user, and calculates an optimal route to the destination. The navigation unit can also estimate the user's emotions using a generating AI and adjust the display method of navigation based on the estimated user's emotions. For example, when a user is nervous, the navigation unit provides a display method that is simple and highly visible. In addition, when a user is relaxed, the navigation unit can also provide a display method that includes detailed information. As a result, the route from the current location to the destination is displayed and navigation is performed, allowing the user to reach the destination without getting lost. A part or all of the above-mentioned processing in the navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input information about the user's current location and destination into the generation AI and have the generation AI calculate the optimal route and display the navigation.

[0039] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit automatically displays travel destinations and accommodations that the user frequently inputs in the past as candidates. For example, the reception unit analyzes data on tourist spots and accommodations visited by the user in the past and suggests input items that match the user's preferences. The reception unit can also preferentially suggest input methods (voice, text, etc.) used by the user in the past. For example, if the user used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also suggest input items related to a specific season or event from the user's past travel history. For example, the reception unit analyzes data on tourist spots and events visited by the user in a specific season in the past and suggests related input items. This makes it possible to improve the efficiency of input work by suggesting the optimal input method based on the past travel history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data to the generation AI and cause the generation AI to execute the suggestion of the optimal input method.

[0040] The reception unit can customize input items based on the user's current living situation and areas of interest at the time of input. For example, the reception unit suggests places and events in which the user showed interest in recent social media activities as input items. For example, the reception unit analyzes data on tourist spots and events that the user has "liked" or commented on on social media, and suggests related input items. The reception unit can also display appropriate input items based on the user's current living situation (for example, family trip, business trip, etc.). For example, if the user is planning a family trip, the reception unit suggests tourist spots and activities for families as input items. The reception unit can also customize related input items based on the user's areas of interest (for example, history, nature, gourmet, etc.). For example, if the user is interested in history, the reception unit suggests historical tourist spots and museums as input items. This makes it possible to streamline input work by providing input items according to the user's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data and living situation data into the generation AI and have the generation AI execute suggestions for related input items.

[0041] The reception unit can display input items with high relevance in consideration of the geographical location information of the user at the time of input. The reception unit, for example, displays tourist spots and restaurants close to the user's current location as input items with priority. For example, the reception unit acquires the user's current location based on GPS information or address information, and suggests nearby tourist spots and restaurants as input items. In addition, when the user is staying in a specific area, the reception unit can also suggest events and activities related to the area as input items. For example, when the user is staying in a specific area, the reception unit acquires information on events and activities held in the area and suggests them as input items. In addition, the reception unit can display optimal input items based on the means of transportation from the user's current location. For example, when the user travels by car, the reception unit suggests tourist spots and restaurants that are easily accessible by car as input items. Thereby, by providing input items based on the geographical location information, the input work can be made more efficient. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to suggest highly relevant input items.

[0042] The reception unit can analyze the user's social media activities at the time of input and suggest related input items. The reception unit, for example, suggests tourist spots and restaurants that the user has "liked" or commented on on social media as input items. For example, the reception unit analyzes data on tourist spots and restaurants that the user has "liked" or commented on on social media and suggests related input items. The reception unit can also suggest input items with reference to places visited by the user's followers and friends. For example, the reception unit analyzes data on tourist spots and restaurants visited by the user's followers and friends and suggests related input items. The reception unit can also suggest related input items based on articles and photos shared by the user on social media. For example, the reception unit analyzes data on tourist spots and events shared by the user on social media and suggests related input items. This makes it possible to provide input items based on social media activities, thereby making input work more efficient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of related input items.

[0043] The generation unit can adjust the level of detail of the itinerary based on the importance of the trip at the time of generation. The generation unit adjusts the level of detail of the itinerary based on the importance of the trip at the time of generation using the generation AI. For example, the generation unit analyzes the purpose and importance of the trip input by the user and adjusts the level of detail of the itinerary based on the purpose and importance of the trip. For example, the generation unit generates an itinerary including a detailed schedule and contact information in the case of an important business trip. In addition, the generation unit can also generate an itinerary including a relaxed schedule and family-oriented activities in the case of a family trip. Furthermore, the generation unit can generate an itinerary including an efficient travel route and major tourist attractions in the case of a short trip. This makes it possible to improve the accuracy of travel plans by providing a detailed itinerary according to the importance of the trip. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input information to the generation AI and cause the generation AI to adjust the level of detail of the itinerary based on the importance of the trip.

[0044] The generation unit can apply different generation algorithms according to the category of the trip at the time of generation. The generation unit uses the generation AI to apply different generation algorithms according to the category of the trip at the time of generation. For example, the generation unit analyzes the category of the trip (business trip, sightseeing trip, family trip, etc.) input by the user and applies the corresponding generation algorithm. For example, in the case of a business trip, the generation unit applies an algorithm that prioritizes schedules for meetings and business negotiations. In addition, in the case of a sightseeing trip, the generation unit can also apply an algorithm that prioritizes tourist destinations and activities. Furthermore, in the case of a family trip, the generation unit can also apply an algorithm that prioritizes activities and accommodations for families. This makes it possible to improve the accuracy of the trip plan by providing an optimal itinerary according to the category of the trip. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's input information to the generation AI and cause the generation AI to apply the generation algorithm according to the category of the trip.

[0045] The generation unit can determine the priority of the itinerary based on the submission time of the trip at the time of generation. The generation unit uses the generation AI to determine the priority of the itinerary based on the submission time of the trip at the time of generation. For example, the generation unit applies an algorithm for rapid generation when the user submits the itinerary just before the trip. The generation unit can also generate an itinerary including a detailed plan when the user submits the itinerary several months before the trip. Furthermore, the generation unit can also generate an itinerary by allocating appropriate resources according to the submission time of the trip. This makes it possible to provide an efficient travel plan by setting the priority according to the submission time. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's submission time information to the generation AI and cause the generation AI to execute the determination of the priority of the itinerary based on the submission time.

[0046] The generation unit can adjust the order of the itinerary based on the relevance of the trip at the time of generation. The generation unit adjusts the order of the itinerary based on the relevance of the trip at the time of generation using the generation AI. For example, the generation unit analyzes the purpose and relevance of the trip input by the user and adjusts the order of the itinerary based on the purpose and relevance. The generation unit, for example, prioritizes major tourist attractions and places them at the beginning of the itinerary. The generation unit can also arrange related tourist attractions consecutively based on the user's interest. Furthermore, the generation unit can also arrange tourist attractions and activities in an optimal order according to the purpose of the trip. As a result, an efficient travel plan can be provided by providing an itinerary in an order based on the relevance. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input information to the generation AI and cause the generation AI to adjust the order of the itinerary based on the relevance of the trip.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the stop-off destination at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the stop-off destination at the time of suggestion using the generation AI. For example, the suggestion unit analyzes the importance of the stop-off destination input by the user and adjusts the level of detail of the proposal based on the importance. For example, in the case of an important tourist destination, the suggestion unit makes a proposal including detailed information and an access method. In addition, in the case of a small tourist destination, the suggestion unit can also make a proposal including concise information and an access method. Furthermore, the suggestion unit can make a proposal with an appropriate level of detail according to the importance of the stop-off destination. This makes it possible to improve the satisfaction of the trip by providing a detailed proposal according to the importance of the stop-off destination. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to adjust the level of detail of the proposal based on the importance of the stop-off destination.

[0048] The suggestion unit can apply different proposal algorithms according to the category of the stopover at the time of suggestion. The suggestion unit uses the generation AI to apply different proposal algorithms according to the category of the stopover at the time of suggestion. For example, the suggestion unit analyzes the category of the stopover input by the user (historical tourist destination, natural scenery, gourmet spot, etc.) and applies the corresponding proposal algorithm. For example, in the case of a historical tourist destination, the suggestion unit makes a proposal including historical background and attractions. In addition, in the case of a natural scenery, the suggestion unit can also make a proposal including scenic points and hiking trails. Furthermore, in the case of a gourmet spot, the suggestion unit can also make a proposal including popular menus and reservation methods. In this way, the satisfaction of the trip can be improved by providing an optimal proposal according to the category of the stopover. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to apply the proposal algorithm according to the category of the stopover.

[0049] The suggestion unit can make a suggestion taking into consideration the geographical distribution of the stopovers when making a suggestion. The suggestion unit makes a suggestion taking into consideration the geographical distribution of the stopovers when making a suggestion using the generation AI. For example, the suggestion unit analyzes the geographical distribution of the stopovers input by the user and makes a suggestion based on the analysis. The suggestion unit, for example, suggests nearby tourist spots together to enable efficient travel. In addition, when visiting geographically distant tourist spots, the suggestion unit can also make a suggestion taking into consideration the means of transportation and time. Furthermore, the suggestion unit can make a suggestion in an optimal order based on the geographical distribution of the stopovers. As a result, an efficient travel plan can be provided by providing a suggestion based on the geographical distribution. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to execute a suggestion based on the geographical distribution of the stopovers.

[0050] The suggestion unit can improve the accuracy of the suggestion by referring to related literature of the stop-off destination at the time of suggestion. The suggestion unit uses the generation AI to improve the accuracy of the suggestion by referring to related literature of the stop-off destination at the time of suggestion. For example, the suggestion unit refers to guidebooks and reviews related to the stop-off destination input by the user and makes a suggestion based on the guidebooks and reviews. The suggestion unit, for example, analyzes guidebooks and reviews related to the stop-off destination and reflects related information in the suggestion. The suggestion unit can also make a suggestion by referring to historical literature related to the stop-off destination. Furthermore, the suggestion unit can also make a suggestion by referring to the latest information on the stop-off destination. This makes it possible to improve the accuracy of the suggestion by providing a suggestion based on related literature. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to execute a suggestion based on related literature of the stop-off destination.

[0051] The reservation unit can select the optimal reservation method by analyzing the user's past reservation history at the time of reservation. The reservation unit uses the generation AI to analyze the user's past reservation history at the time of reservation and select the optimal reservation method. For example, the reservation unit preferentially displays reservation sites that the user has frequently used in the past. The reservation unit can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. Furthermore, the reservation unit can also suggest a reservation method suitable for a specific time period or season from the user's past reservation history. This makes it possible to streamline reservation work by providing an optimal reservation method based on the past reservation history. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into the generation AI and cause the generation AI to select the optimal reservation method.

[0052] The reservation unit can customize the reservation means based on the user's current living situation at the time of reservation. The reservation unit uses the generation AI to customize the reservation means based on the user's current living situation at the time of reservation. For example, if the user is on a family trip, the reservation unit can preferentially suggest family-oriented accommodations and activities. Also, if the user is on a business trip, the reservation unit can preferentially suggest business-oriented accommodations and conference rooms. Furthermore, the reservation unit can also suggest the optimal reservation means based on the user's current living situation (e.g., budget, time constraints, etc.). This makes it possible to streamline reservation work by providing a reservation means according to the user's living situation. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into the generation AI and cause the generation AI to execute a suggestion of the optimal reservation means.

[0053] The reservation unit can select the optimal reservation method in consideration of the geographical location information of the user at the time of reservation. The reservation unit uses the generation AI to select the optimal reservation method in consideration of the geographical location information of the user at the time of reservation. For example, the reservation unit preferentially reserves accommodations and restaurants close to the user's current location. In addition, if the user is staying in a specific area, the reservation unit can also preferentially reserve activities and events related to that area. Furthermore, the reservation unit can also suggest the optimal reservation method based on the geographical location information of the user. This makes it possible to streamline reservation work by providing the optimal reservation method based on the geographical location information. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal reservation method.

[0054] The reservation unit can analyze the user's social media activity at the time of reservation and suggest a means of reservation. The reservation unit uses the generation AI to analyze the user's social media activity at the time of reservation and suggest a means of reservation. For example, the reservation unit preferentially reserves restaurants and accommodations that the user has "liked" or commented on on social media. The reservation unit can also suggest a means of reservation with reference to places visited by the user's followers and friends. Furthermore, the reservation unit can also suggest related reservation means based on articles and photos shared by the user on social media. This makes it possible to streamline reservation work by providing a reservation means based on social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data into the generation AI and have the generation AI execute a suggestion of the optimal reservation means.

[0055] The navigation unit can suggest an optimal route by referring to the user's past movement history during navigation. The navigation unit uses the generation AI to suggest an optimal route by referring to the user's past movement history during navigation. For example, the navigation unit suggests an optimal route based on routes used by the user in the past. The navigation unit can also suggest a route that avoids congestion from the user's past movement history. Furthermore, the navigation unit can analyze the user's past movement history and suggest the most efficient route. This makes it possible to improve the accuracy of navigation by providing an optimal route based on the past movement history. Some or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's past movement history data to the generation AI and cause the generation AI to execute the proposal of an optimal route.

[0056] The navigation unit can customize the navigation means based on the user's current living situation during navigation. The navigation unit uses the generation AI to customize the navigation means based on the user's current living situation during navigation. For example, if the user is on a family trip, the navigation unit can preferentially suggest routes and activities for families. Also, if the user is on a business trip, the navigation unit can preferentially suggest routes and meeting places for business. Furthermore, the navigation unit can also suggest the optimal navigation means based on the user's current living situation (e.g., budget, time constraints, etc.). This makes it possible to improve the accuracy of navigation by providing a navigation means according to the user's living situation. Some or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's living situation data to the generation AI and cause the generation AI to execute the suggestion of the optimal navigation means.

[0057] The navigation unit can propose an optimal route during navigation, taking into account the geographical location information of the user. The navigation unit uses the generation AI to propose an optimal route during navigation, taking into account the geographical location information of the user. For example, the navigation unit preferentially includes tourist attractions and restaurants close to the user's current location in the route. In addition, if the user is staying in a specific area, the navigation unit can also preferentially include tourist attractions and activities related to the area in the route. Furthermore, the navigation unit can also propose an optimal route based on the geographical location information of the user. This makes it possible to improve the accuracy of navigation by providing an optimal route based on the geographical location information. Part or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the proposal of an optimal route.

[0058] The navigation unit can analyze the user's social media activities and suggest navigation means during navigation. The navigation unit uses the generation AI to analyze the user's social media activities and suggest navigation means during navigation. For example, the navigation unit preferentially includes in the route tourist attractions and restaurants that the user has "liked" or commented on on social media. The navigation unit can also suggest navigation means with reference to places visited by the user's followers and friends. Furthermore, the navigation unit can also suggest related navigation means based on articles and photos shared by the user on social media. This makes it possible to improve the accuracy of navigation by providing navigation means based on social media activities. Some or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a suggestion of an optimal navigation means.

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

[0060] The travel planning system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, blood pressure, number of steps, etc.) and supports health management during the trip. For example, the health management unit may suggest a break if the user's heart rate becomes high. The health management unit may also suggest a place to relax if the user's number of steps exceeds a certain standard. Furthermore, the health management unit may suggest meals and activities during the trip based on the user's health condition. This allows the user to enjoy the trip while maintaining their health.

[0061] The reservation unit can analyze the user's past reservation history and suggest the optimal reservation method. For example, it can preferentially display reservation sites that the user has used frequently in the past. It can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. Furthermore, it can also suggest reservation methods suitable for specific time periods or seasons based on the user's past reservation history. This makes it possible to streamline reservation work by providing the optimal reservation method based on the past reservation history.

[0062] The navigation unit can suggest an optimal route by referring to the user's past movement history. For example, the optimal route can be suggested based on routes the user has used in the past. In addition, a route that avoids congestion can be suggested based on the user's past movement history. Furthermore, the navigation unit can analyze the user's past movement history and suggest the most efficient route. This can improve the accuracy of navigation by providing an optimal route based on the user's past movement history.

[0063] The reception unit can customize input items based on the user's current living situation and areas of interest. For example, places and events that the user showed interest in in recent social media activities are suggested as input items. Appropriate input items can also be displayed based on the user's current living situation (e.g., family trip, business trip, etc.). Furthermore, related input items can also be customized based on the user's areas of interest (e.g., history, nature, gourmet, etc.). This can make input work more efficient by providing input items according to the user's living situation and areas of interest.

[0064] The reservation unit can select the optimal reservation method in consideration of the user's geographical location information. For example, the reservation unit can give priority to reserving accommodations and restaurants close to the user's current location. Also, if the user is staying in a specific area, the reservation unit can give priority to reserving activities and events related to that area. Furthermore, the reservation unit can suggest the optimal reservation method based on the user's geographical location information. This can make reservation work more efficient by providing the optimal reservation method based on the geographical location information.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit accepts basic information from the user. Basic information includes the schedule, means of transportation (air, bullet train, car, etc.), accommodation, desired sightseeing spots, and desired food. For example, the user might input "Traveling from March 1st to March 5th, means of transportation is the bullet train, accommodation is Tokyo, desired sightseeing spot is tourist spot A, and desired food is dish B." This information is input into the AI. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and create an optimal itinerary. The generation unit calculates efficient routes based on information such as the schedule, means of transportation, accommodation, desired sightseeing spots, and desired foods, and creates a schedule for each day. For example, the generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to calculate efficient routes and create a schedule for each day. Step 3: The suggestion unit suggests recommended stopovers other than the desired tourist spots based on the itinerary created by the generation unit. For example, it suggests visiting a nearby tourist spot D after visiting tourist spot A. The suggestion unit can also use the generation AI to suggest stopovers based on the user's preferences. Step 4: The reservation department checks availability and makes reservations for restaurants, experiences, and other things that require reservations. For example, it checks availability at restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if it is available. The reservation department can also use generative AI to search multiple reservation sites across the board and provide the best reservation options. Step 5: The navigation unit performs navigation in conjunction with the map application. For example, when the user heads to tourist spot A, the map application displays the route from the current location to tourist spot A and performs navigation. The navigation unit can also use the generation AI to estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions.

[0067] (Example 2) A travel planning system according to an embodiment of the present invention is a system that provides an optimal itinerary by allowing a user to input basic information such as the schedule, transportation (airline, bullet train, car, etc.), accommodation, desired sightseeing spots, and what to eat. In this system, the user inputs basic information such as the schedule, transportation, accommodation, desired sightseeing spots, and what to eat. For example, the user inputs "Travel from March 1st to March 5th, transportation is bullet train, accommodation is Tokyo, desired sightseeing spot is sightseeing spot A, and what to eat is food B." This information is input to AI. Next, AI analyzes the input information and creates an optimal itinerary. Based on information such as the schedule, transportation, accommodation, desired sightseeing spot, and what to eat, AI calculates an efficient route and creates a schedule for each day. For example, on March 1st, you visit sightseeing spot A, and on March 2nd, you visit restaurant C to eat food B. Furthermore, if there is room in the itinerary, AI suggests recommended stops other than the desired sightseeing spots. For example, after visiting sightseeing spot A, it suggests visiting nearby sightseeing spot D. In this way, a fulfilling travel plan is provided for the user. In addition, for things that require reservations, such as restaurants and experiences, the AI ​​checks availability. For example, it checks the availability of restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if a reservation is available. This allows the user to complete the reservation without any hassle. Finally, on the day, it links with a map app to provide navigation. For example, when a user heads to tourist spot A, the map app displays the route from the user's current location to tourist spot A and performs navigation. This allows the user to reach the destination without getting lost. This mechanism allows the user to easily create the optimal travel plan and enjoy a fulfilling trip. As a result, the travel planning system can streamline the user's travel planning and provide a fulfilling travel experience.

[0068] The travel planning system according to the embodiment includes a reception unit, a generation unit, a proposal unit, a reservation unit, and a navigation unit. The reception unit receives basic information from a user. The basic information includes, for example, a schedule, a means of transportation (air, bullet train, car, etc.), a place to stay, a desired sightseeing spot, and a desired food, but is not limited to such examples. For example, the user inputs "Travel from March 1 to March 5, a means of transportation is a bullet train, a place to stay is Tokyo, a desired sightseeing spot is tourist spot A, and a desired food is food B" into the reception unit. This information is input to the AI. The generation unit uses the generation AI to analyze the information received by the reception unit and create an optimal itinerary. The generation unit calculates an efficient route based on information such as the schedule, a means of transportation, a place to stay, a desired sightseeing spot, and a desired food, and creates a schedule for each day. For example, the generation AI uses a text generation AI (for example, LLM) to calculate an efficient route and create a schedule for each day. The generation unit can also use the multimodal generation AI to calculate efficient routes and create a schedule for each day. The suggestion unit suggests recommended stopovers other than the desired tourist spot based on the itinerary created by the generation unit. For example, the suggestion unit suggests visiting a nearby tourist spot D after visiting tourist spot A. The suggestion unit can also use the generation AI to suggest stopovers based on the user's preferences. The reservation unit inquires about availability and makes reservations for things that require reservations, such as restaurants and experiences. For example, the reservation unit checks the availability of restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if a reservation is possible. The reservation unit can also use the generation AI to search multiple reservation sites across multiple sites and provide optimal reservation options. The navigation unit performs navigation in conjunction with a map app. For example, when a user heads to tourist spot A, the map app displays a route from the user's current location to tourist spot A and performs navigation. The navigation unit can also use the generation AI to estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions. As a result, the travel planning system according to the embodiment can improve the efficiency of the user's travel planning and provide a fulfilling travel experience.

[0069] The reception unit receives basic information from the user. The basic information includes, for example, a schedule, a means of transportation (air, bullet train, car, etc.), a place of accommodation, a desired sightseeing spot, and a food to eat, but is not limited to such examples. For example, the user inputs "Travel from March 1 to March 5, a means of transportation is a bullet train, a place of accommodation is Tokyo, a desired sightseeing spot is tourist spot A, and a food to eat is food B" into the reception unit. This information is input to the AI. The reception unit provides an interface to accurately receive the information input by the user and properly process it within the system. For example, when a user inputs information through a website or a mobile app, the reception unit provides an intuitive and easy-to-use form so that the user can input all the necessary information. Furthermore, the reception unit checks the consistency of the input information, and if there is missing or contradictory information, it provides appropriate feedback to the user. For example, if there is an overlap in the schedule or if there is a contradiction between the means of transportation and the place of accommodation, an error message is displayed to the user to prompt the user to make corrections. The reception unit also has a function to save the user's input contents in real time and to allow the user to resume input even if it is interrupted midway. This allows the user to take their time to carefully plan their trip. Furthermore, the reception unit can store the user's past travel history and preferences, and refer to them when planning their next trip. For example, personalized suggestions can be made to the user based on information such as tourist spots visited in the past, accommodations, and food eaten. This allows the reception unit to flexibly respond to the user's needs, improving the efficiency of trip planning and satisfaction.

[0070] The generation unit uses the generation AI to analyze the information received by the reception unit and create an optimal itinerary. The generation unit calculates an efficient route based on information such as the schedule, transportation means, accommodation, desired sightseeing spots, and what to eat, and creates a schedule for each day. The generation AI uses a text generation AI (e.g., LLM) to calculate an efficient route and create a schedule for each day. The generation unit can also use a multimodal generation AI to calculate an efficient route and create a schedule for each day. The generation unit utilizes multiple data sources to generate an optimal itinerary based on the user's input information. For example, the generation unit obtains information such as transportation schedules, business hours of tourist spots, and restaurant reservation status in real time, and integrates this information to create an optimal schedule. The generation AI analyzes these data and proposes an itinerary that best suits the user's wishes. For example, when visiting tourist spot A, the least crowded time period is selected so that sightseeing can be enjoyed efficiently. The generation unit can also create a personalized itinerary taking into account the user's preferences and past travel history. For example, it will suggest new tourist spots and restaurants that the user might be interested in based on information about tourist spots visited in the past and food eaten. Furthermore, the generation unit can modify and improve the itinerary based on user feedback. For example, if a user wants to add a specific tourist spot or change the schedule, the generation AI will respond quickly and regenerate the optimal itinerary. This allows the generation unit to respond flexibly to user needs, resulting in more efficient travel planning and improved satisfaction.

[0071] The suggestion unit suggests recommended stopovers other than the desired tourist spot based on the itinerary created by the generation unit. For example, the suggestion unit suggests visiting a nearby tourist spot D after visiting tourist spot A. The suggestion unit can also suggest stopovers based on the user's preferences using the generation AI. The suggestion unit suggests tourist spots and activities that the user may be interested in based on the user's input information and past travel history. For example, if the user is interested in historical buildings, the suggestion unit suggests historical tourist spots in the area. Also, if the user wants to enjoy nature, the suggestion unit can suggest nearby parks and nature reserves. The suggestion unit uses the generation AI to analyze the user's preferences and interests and suggest optimal stopovers. For example, if the user wants to eat a specific dish, the suggestion unit can suggest restaurants and markets where the dish can be enjoyed. Furthermore, the suggestion unit suggests efficient stopovers taking into account the user's schedule and means of transportation. For example, the suggestion unit makes suggestions taking into account the means of transportation and travel time so that the user can move smoothly from tourist spot A to tourist spot D. This allows the user to enjoy sightseeing efficiently without wasting time. The suggestion unit can continuously improve the accuracy and effectiveness of the suggestions based on the user's feedback. For example, the suggestion unit can collect the user's impressions and evaluations after visiting the suggested tourist spots and reflect them in the next suggestions. This enables the suggestion unit to flexibly respond to the user's preferences, resulting in more efficient travel planning and improved satisfaction.

[0072] The reservation unit checks availability and makes reservations for restaurants and experiences that require reservations. For example, the reservation unit checks the availability of restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if a reservation is possible. The reservation unit can also use the generation AI to search multiple reservation sites across multiple reservation sites and provide optimal reservation options. The reservation unit works with multiple reservation sites and services to efficiently make reservations for restaurants and activities that users want. For example, it obtains availability in real time from restaurant reservation sites, experience activity reservation sites, hotel reservation sites, etc., and provides optimal reservation options. The generation AI analyzes this data and automatically makes a reservation that best suits the user's wishes. For example, if a user wants to make a reservation at a restaurant during a specific time period, it prioritizes searching for restaurants that have availability during that time period and makes a reservation. The reservation unit also makes efficient reservations by taking into account the user's schedule and means of transportation. For example, if a user wants to eat at restaurant C after visiting tourist attraction A, it suggests the optimal reservation time by taking into account the travel time from tourist attraction A to restaurant C. Furthermore, the reservation unit can continuously improve the accuracy and effectiveness of reservations based on user feedback. For example, it can collect ratings of restaurants and activities reserved by users and reflect them in the next reservation. This allows the reservation unit to flexibly respond to users' needs, making travel planning more efficient and increasing satisfaction.

[0073] The navigation unit performs navigation in conjunction with a map application. For example, when a user heads to a tourist spot A, the map application displays a route from the current location to tourist spot A and performs navigation. The navigation unit can also use the generation AI to estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions. The navigation unit provides the optimal route in real time so that the user can reach the destination smoothly. For example, the optimal route is calculated and guided to the user, taking into account traffic congestion, construction information, weather information, etc. The generation AI analyzes these data and provides the optimal route from the user's current location to the destination. In addition, the navigation unit can estimate the user's emotions and adjust the navigation display method. For example, if the user is tired, more concise and easy-to-understand guidance is provided, and if the user is relaxed, detailed guidance is provided. Furthermore, the navigation unit can provide personalized navigation taking into account the user's preferences and past movement history. For example, the navigation unit suggests routes and stops that the user is likely to like based on information on routes the user has used in the past and places visited. This enables the navigation unit to flexibly respond to the needs of the user, improving the efficiency of travel plans and satisfaction. Furthermore, the navigation unit can continuously improve the accuracy and effectiveness of navigation based on user feedback. For example, the user can rate the route provided and the next navigation can be based on that rating. This allows the navigation unit to provide the user with quick and reliable navigation and enrich the travel experience.

[0074] The suggestion unit can suggest a stopover based on the user's preferences. The suggestion unit identifies the user's preferences based on, for example, the user's past travel history or survey results, and suggests a stopover. For example, the suggestion unit analyzes data on tourist spots and restaurants visited by the user in the past, and suggests a stopover that matches the user's preferences. The suggestion unit can also suggest a stopover based on the interests and concerns answered by the user in the survey. This improves the satisfaction of the trip by suggesting a stopover according to the user's preferences. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's past travel history and survey results into the generation AI, and cause the generation AI to execute a stopover suggestion based on the user's preferences.

[0075] The reservation unit can search multiple reservation sites across the board. The reservation unit cross-searches multiple reservation sites, such as a hotel reservation site, an airline ticket reservation site, and a restaurant reservation site, and provides optimal reservation options. For example, the reservation unit obtains the availability of accommodations, transportation, and restaurants desired by the user from multiple reservation sites and compares and considers them. The reservation unit can also propose optimal reservation options based on the user's budget and desired conditions. This makes it possible to provide optimal reservation options by searching multiple reservation sites. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input data from multiple reservation sites into the generation AI and cause the generation AI to execute the proposal of optimal reservation options.

[0076] The generation unit can calculate an efficient route based on the information of the schedule, the means of transportation, the accommodation, the desired sightseeing spots, and the food to be eaten, and create a schedule for each day. The generation unit uses a generation AI to calculate an efficient route based on the information of the schedule, the means of transportation, the accommodation, the desired sightseeing spots, and the food to be eaten, and creates a schedule for each day. For example, the generation unit analyzes the information of the schedule, the means of transportation, the accommodation, the desired sightseeing spots, and the food to be eaten input by the user, and calculates an efficient route. The generation unit creates a schedule for each day, for example, taking into consideration minimizing travel time and the optimal order of sightseeing spots. The generation unit can also adjust the schedule based on the user's preferences using the generation AI. For example, if the user desires a relaxing trip, the generation unit creates a relaxed schedule. In addition, if the user desires an efficient trip, the generation unit can also create a schedule that emphasizes the shortest route. As a result, the travel plan can be optimized by calculating an efficient route and creating a schedule for each day. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input user input information to the generation AI and cause the generation AI to calculate efficient routes and create schedules.

[0077] The navigation unit can display a route from the current location to the destination and perform navigation. The navigation unit, for example, works in conjunction with a map application to display a route from the current location to the destination and perform navigation. For example, when a user heads to a tourist spot A, the map application displays a route from the current location to the tourist spot A and performs navigation. The navigation unit acquires the user's current location based on GPS information or a manual input by the user, and calculates an optimal route to the destination. The navigation unit can also estimate the user's emotions using a generating AI and adjust the display method of navigation based on the estimated user's emotions. For example, when a user is nervous, the navigation unit provides a display method that is simple and highly visible. In addition, when a user is relaxed, the navigation unit can also provide a display method that includes detailed information. As a result, the route from the current location to the destination is displayed and navigation is performed, allowing the user to reach the destination without getting lost. A part or all of the above-mentioned processing in the navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input information about the user's current location and destination into the generation AI and have the generation AI calculate the optimal route and display the navigation.

[0078] The reception unit can estimate the user's emotion and adjust the input method of the basic information based on the estimated emotion of the user. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to reduce the stress of the input work by providing an input method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0079] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit automatically displays travel destinations and accommodations that the user frequently inputs in the past as candidates. For example, the reception unit analyzes data on tourist spots and accommodations visited by the user in the past and suggests input items that match the user's preferences. The reception unit can also preferentially suggest input methods (voice, text, etc.) used by the user in the past. For example, if the user used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also suggest input items related to a specific season or event from the user's past travel history. For example, the reception unit analyzes data on tourist spots and events visited by the user in a specific season in the past and suggests related input items. This makes it possible to improve the efficiency of input work by suggesting the optimal input method based on the past travel history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data to the generation AI and cause the generation AI to execute the suggestion of the optimal input method.

[0080] The reception unit can customize input items based on the user's current living situation and areas of interest at the time of input. For example, the reception unit suggests places and events in which the user showed interest in recent social media activities as input items. For example, the reception unit analyzes data on tourist spots and events that the user has "liked" or commented on on social media, and suggests related input items. The reception unit can also display appropriate input items based on the user's current living situation (for example, family trip, business trip, etc.). For example, if the user is planning a family trip, the reception unit suggests tourist spots and activities for families as input items. The reception unit can also customize related input items based on the user's areas of interest (for example, history, nature, gourmet, etc.). For example, if the user is interested in history, the reception unit suggests historical tourist spots and museums as input items. This makes it possible to streamline input work by providing input items according to the user's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data and living situation data into the generation AI and have the generation AI execute suggestions for related input items.

[0081] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to reduce the stress of the input work by providing a display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0082] The reception unit can display input items with high relevance in consideration of the geographical location information of the user at the time of input. The reception unit, for example, displays tourist spots and restaurants close to the user's current location as input items with priority. For example, the reception unit acquires the user's current location based on GPS information or address information, and suggests nearby tourist spots and restaurants as input items. In addition, when the user is staying in a specific area, the reception unit can also suggest events and activities related to the area as input items. For example, when the user is staying in a specific area, the reception unit acquires information on events and activities held in the area and suggests them as input items. In addition, the reception unit can display optimal input items based on the means of transportation from the user's current location. For example, when the user travels by car, the reception unit suggests tourist spots and restaurants that are easily accessible by car as input items. Thereby, by providing input items based on the geographical location information, the input work can be made more efficient. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to suggest highly relevant input items.

[0083] The reception unit can analyze the user's social media activities at the time of input and suggest related input items. The reception unit, for example, suggests tourist spots and restaurants that the user has "liked" or commented on on social media as input items. For example, the reception unit analyzes data on tourist spots and restaurants that the user has "liked" or commented on on social media and suggests related input items. The reception unit can also suggest input items with reference to places visited by the user's followers and friends. For example, the reception unit analyzes data on tourist spots and restaurants visited by the user's followers and friends and suggests related input items. The reception unit can also suggest related input items based on articles and photos shared by the user on social media. For example, the reception unit analyzes data on tourist spots and events shared by the user on social media and suggests related input items. This makes it possible to provide input items based on social media activities, thereby making input work more efficient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the suggestion of related input items.

[0084] The generation unit can estimate the user's emotion and adjust the way of expressing the itinerary based on the estimated user's emotion. The generation unit uses the generation AI to estimate the user's emotion and adjust the way of expressing the itinerary based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to improve the satisfaction of the travel plan by providing an itinerary according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0085] The generation unit can adjust the level of detail of the itinerary based on the importance of the trip at the time of generation. The generation unit adjusts the level of detail of the itinerary based on the importance of the trip at the time of generation using the generation AI. For example, the generation unit analyzes the purpose and importance of the trip input by the user and adjusts the level of detail of the itinerary based on the purpose and importance of the trip. For example, the generation unit generates an itinerary including a detailed schedule and contact information in the case of an important business trip. In addition, the generation unit can also generate an itinerary including a relaxed schedule and family-oriented activities in the case of a family trip. Furthermore, the generation unit can generate an itinerary including an efficient travel route and major tourist attractions in the case of a short trip. This makes it possible to improve the accuracy of travel plans by providing a detailed itinerary according to the importance of the trip. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input information to the generation AI and cause the generation AI to adjust the level of detail of the itinerary based on the importance of the trip.

[0086] The generation unit can apply different generation algorithms according to the category of the trip at the time of generation. The generation unit uses the generation AI to apply different generation algorithms according to the category of the trip at the time of generation. For example, the generation unit analyzes the category of the trip (business trip, sightseeing trip, family trip, etc.) input by the user and applies the corresponding generation algorithm. For example, in the case of a business trip, the generation unit applies an algorithm that prioritizes schedules for meetings and business negotiations. In addition, in the case of a sightseeing trip, the generation unit can also apply an algorithm that prioritizes tourist destinations and activities. Furthermore, in the case of a family trip, the generation unit can also apply an algorithm that prioritizes activities and accommodations for families. This makes it possible to improve the accuracy of the trip plan by providing an optimal itinerary according to the category of the trip. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's input information to the generation AI and cause the generation AI to apply the generation algorithm according to the category of the trip.

[0087] The generation unit can estimate the user's emotion and adjust the length of the itinerary based on the estimated user's emotion. The generation unit uses a generation AI to estimate the user's emotion and adjust the length of the itinerary based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to improve the satisfaction of the travel plan by providing the itinerary length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0088] The generation unit can determine the priority of the itinerary based on the submission time of the trip at the time of generation. The generation unit uses the generation AI to determine the priority of the itinerary based on the submission time of the trip at the time of generation. For example, the generation unit applies an algorithm for rapid generation when the user submits the itinerary just before the trip. The generation unit can also generate an itinerary including a detailed plan when the user submits the itinerary several months before the trip. Furthermore, the generation unit can also generate an itinerary by allocating appropriate resources according to the submission time of the trip. This makes it possible to provide an efficient travel plan by setting the priority according to the submission time. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's submission time information to the generation AI and cause the generation AI to execute the determination of the priority of the itinerary based on the submission time.

[0089] The generation unit can adjust the order of the itinerary based on the relevance of the trip at the time of generation. The generation unit adjusts the order of the itinerary based on the relevance of the trip at the time of generation using the generation AI. For example, the generation unit analyzes the purpose and relevance of the trip input by the user and adjusts the order of the itinerary based on the purpose and relevance. The generation unit, for example, prioritizes major tourist attractions and places them at the beginning of the itinerary. The generation unit can also arrange related tourist attractions consecutively based on the user's interest. Furthermore, the generation unit can also arrange tourist attractions and activities in an optimal order according to the purpose of the trip. As a result, an efficient travel plan can be provided by providing an itinerary in an order based on the relevance. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's input information to the generation AI and cause the generation AI to adjust the order of the itinerary based on the relevance of the trip.

[0090] The suggestion unit can estimate the user's emotion and adjust the stop-off suggestion method based on the estimated user's emotion. The suggestion unit uses the generation AI to estimate the user's emotion and adjust the stop-off suggestion method based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to improve the satisfaction of the trip by providing a suggestion method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0091] The suggestion unit can adjust the level of detail of the proposal based on the importance of the stop-off destination at the time of suggestion. The suggestion unit adjusts the level of detail of the proposal based on the importance of the stop-off destination at the time of suggestion using the generation AI. For example, the suggestion unit analyzes the importance of the stop-off destination input by the user and adjusts the level of detail of the proposal based on the importance. For example, in the case of an important tourist destination, the suggestion unit makes a proposal including detailed information and an access method. In addition, in the case of a small tourist destination, the suggestion unit can also make a proposal including concise information and an access method. Furthermore, the suggestion unit can make a proposal with an appropriate level of detail according to the importance of the stop-off destination. This makes it possible to improve the satisfaction of the trip by providing a detailed proposal according to the importance of the stop-off destination. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to adjust the level of detail of the proposal based on the importance of the stop-off destination.

[0092] The suggestion unit can apply different proposal algorithms according to the category of the stopover at the time of suggestion. The suggestion unit uses the generation AI to apply different proposal algorithms according to the category of the stopover at the time of suggestion. For example, the suggestion unit analyzes the category of the stopover input by the user (historical tourist destination, natural scenery, gourmet spot, etc.) and applies the corresponding proposal algorithm. For example, in the case of a historical tourist destination, the suggestion unit makes a proposal including historical background and attractions. In addition, in the case of a natural scenery, the suggestion unit can also make a proposal including scenic points and hiking trails. Furthermore, in the case of a gourmet spot, the suggestion unit can also make a proposal including popular menus and reservation methods. In this way, the satisfaction of the trip can be improved by providing an optimal proposal according to the category of the stopover. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to apply the proposal algorithm according to the category of the stopover.

[0093] The suggestion unit can estimate the user's emotion and determine the priority order of the stopovers based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using the generation AI and determine the priority order of the stopovers based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to improve the satisfaction of the trip by setting the priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0094] The suggestion unit can make a suggestion taking into consideration the geographical distribution of the stopovers when making a suggestion. The suggestion unit makes a suggestion taking into consideration the geographical distribution of the stopovers when making a suggestion using the generation AI. For example, the suggestion unit analyzes the geographical distribution of the stopovers input by the user and makes a suggestion based on the analysis. The suggestion unit, for example, suggests nearby tourist spots together to enable efficient travel. In addition, when visiting geographically distant tourist spots, the suggestion unit can also make a suggestion taking into consideration the means of transportation and time. Furthermore, the suggestion unit can make a suggestion in an optimal order based on the geographical distribution of the stopovers. As a result, an efficient travel plan can be provided by providing a suggestion based on the geographical distribution. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to execute a suggestion based on the geographical distribution of the stopovers.

[0095] The suggestion unit can improve the accuracy of the suggestion by referring to related literature of the stop-off destination at the time of suggestion. The suggestion unit uses the generation AI to improve the accuracy of the suggestion by referring to related literature of the stop-off destination at the time of suggestion. For example, the suggestion unit refers to guidebooks and reviews related to the stop-off destination input by the user and makes a suggestion based on the guidebooks and reviews. The suggestion unit, for example, analyzes guidebooks and reviews related to the stop-off destination and reflects related information in the suggestion. The suggestion unit can also make a suggestion by referring to historical literature related to the stop-off destination. Furthermore, the suggestion unit can also make a suggestion by referring to the latest information on the stop-off destination. This makes it possible to improve the accuracy of the suggestion by providing a suggestion based on related literature. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's input information to the generation AI and cause the generation AI to execute a suggestion based on related literature of the stop-off destination.

[0096] The reservation unit can estimate the user's emotion and adjust the reservation method based on the estimated user's emotion. The reservation unit estimates the user's emotion using the generation AI and adjusts the reservation method based on the estimated user's emotion. For example, the reservation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reservation unit calculates an emotion score based on the change in facial expression. The reservation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reservation unit analyzes the tone and speed of the voice and calculates the emotion score. The reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reservation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to reduce the stress of the reservation work by providing a reservation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0097] The reservation unit can select the optimal reservation method by analyzing the user's past reservation history at the time of reservation. The reservation unit uses the generation AI to analyze the user's past reservation history at the time of reservation and select the optimal reservation method. For example, the reservation unit preferentially displays reservation sites that the user has frequently used in the past. The reservation unit can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. Furthermore, the reservation unit can also suggest a reservation method suitable for a specific time period or season from the user's past reservation history. This makes it possible to streamline reservation work by providing an optimal reservation method based on the past reservation history. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's past reservation history data into the generation AI and cause the generation AI to select the optimal reservation method.

[0098] The reservation unit can customize the reservation means based on the user's current living situation at the time of reservation. The reservation unit uses the generation AI to customize the reservation means based on the user's current living situation at the time of reservation. For example, if the user is on a family trip, the reservation unit can preferentially suggest family-oriented accommodations and activities. Also, if the user is on a business trip, the reservation unit can preferentially suggest business-oriented accommodations and conference rooms. Furthermore, the reservation unit can also suggest the optimal reservation means based on the user's current living situation (e.g., budget, time constraints, etc.). This makes it possible to streamline reservation work by providing a reservation means according to the user's living situation. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into the generation AI and cause the generation AI to execute a suggestion of the optimal reservation means.

[0099] The reservation unit can estimate the user's emotion and determine the priority of reservations based on the estimated user's emotion. The reservation unit estimates the user's emotion using a generation AI and determines the priority of reservations based on the estimated user's emotion. For example, the reservation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reservation unit calculates an emotion score based on the change in facial expression. The reservation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reservation unit analyzes the tone and speed of the voice and calculates the emotion score. The reservation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reservation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to improve the satisfaction of the reservation work by setting a priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit may input image data of a user taken by a camera to the generation AI, and cause the generation AI to estimate the user's emotions.

[0100] The reservation unit can select the optimal reservation method in consideration of the geographical location information of the user at the time of reservation. The reservation unit uses the generation AI to select the optimal reservation method in consideration of the geographical location information of the user at the time of reservation. For example, the reservation unit preferentially reserves accommodations and restaurants close to the user's current location. In addition, if the user is staying in a specific area, the reservation unit can also preferentially reserve activities and events related to that area. Furthermore, the reservation unit can also suggest the optimal reservation method based on the geographical location information of the user. This makes it possible to streamline reservation work by providing the optimal reservation method based on the geographical location information. Part or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal reservation method.

[0101] The reservation unit can analyze the user's social media activity at the time of reservation and suggest a means of reservation. The reservation unit uses the generation AI to analyze the user's social media activity at the time of reservation and suggest a means of reservation. For example, the reservation unit preferentially reserves restaurants and accommodations that the user has "liked" or commented on on social media. The reservation unit can also suggest a means of reservation with reference to places visited by the user's followers and friends. Furthermore, the reservation unit can also suggest related reservation means based on articles and photos shared by the user on social media. This makes it possible to streamline reservation work by providing a reservation means based on social media activity. Some or all of the above-mentioned processing in the reservation unit may be performed, for example, using AI, or may be performed without using AI. For example, the reservation unit can input the user's social media activity data into the generation AI and have the generation AI execute a suggestion of the optimal reservation means.

[0102] The navigation unit can estimate the user's emotion and adjust the display method of the navigation based on the estimated user's emotion. The navigation unit estimates the user's emotion using a generation AI and adjusts the display method of the navigation based on the estimated user's emotion. For example, the navigation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on the change in facial expression. The navigation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the navigation unit analyzes the tone and speed of the voice and calculates the emotion score. The navigation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the navigation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to reduce the stress of navigation by providing a display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit may input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0103] The navigation unit can suggest an optimal route by referring to the user's past movement history during navigation. The navigation unit uses the generation AI to suggest an optimal route by referring to the user's past movement history during navigation. For example, the navigation unit suggests an optimal route based on routes used by the user in the past. The navigation unit can also suggest a route that avoids congestion from the user's past movement history. Furthermore, the navigation unit can analyze the user's past movement history and suggest the most efficient route. This makes it possible to improve the accuracy of navigation by providing an optimal route based on the past movement history. Some or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's past movement history data to the generation AI and cause the generation AI to execute the proposal of an optimal route.

[0104] The navigation unit can customize the navigation means based on the user's current living situation during navigation. The navigation unit uses the generation AI to customize the navigation means based on the user's current living situation during navigation. For example, if the user is on a family trip, the navigation unit can preferentially suggest routes and activities for families. Also, if the user is on a business trip, the navigation unit can preferentially suggest routes and meeting places for business. Furthermore, the navigation unit can also suggest the optimal navigation means based on the user's current living situation (e.g., budget, time constraints, etc.). This makes it possible to improve the accuracy of navigation by providing a navigation means according to the user's living situation. Some or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's living situation data to the generation AI and cause the generation AI to execute the suggestion of the optimal navigation means.

[0105] The navigation unit can estimate the user's emotion and determine the priority of navigation based on the estimated user's emotion. The navigation unit estimates the user's emotion using a generation AI and determines the priority of navigation based on the estimated user's emotion. For example, the navigation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the navigation unit calculates an emotion score based on the change in facial expression. The navigation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the navigation unit analyzes the tone and speed of the voice and calculates the emotion score. The navigation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the navigation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to improve the satisfaction of the navigation by setting a priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit may input image data of the user captured by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0106] The navigation unit can propose an optimal route during navigation, taking into account the geographical location information of the user. The navigation unit uses the generation AI to propose an optimal route during navigation, taking into account the geographical location information of the user. For example, the navigation unit preferentially includes tourist attractions and restaurants close to the user's current location in the route. In addition, if the user is staying in a specific area, the navigation unit can also preferentially include tourist attractions and activities related to the area in the route. Furthermore, the navigation unit can also propose an optimal route based on the geographical location information of the user. This makes it possible to improve the accuracy of navigation by providing an optimal route based on the geographical location information. Part or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the proposal of an optimal route.

[0107] The navigation unit can analyze the user's social media activities and suggest navigation means during navigation. The navigation unit uses the generation AI to analyze the user's social media activities and suggest navigation means during navigation. For example, the navigation unit preferentially includes in the route tourist attractions and restaurants that the user has "liked" or commented on on social media. The navigation unit can also suggest navigation means with reference to places visited by the user's followers and friends. Furthermore, the navigation unit can also suggest related navigation means based on articles and photos shared by the user on social media. This makes it possible to improve the accuracy of navigation by providing navigation means based on social media activities. Some or all of the above-mentioned processing in the navigation unit may be performed, for example, using AI, or may be performed without using AI. For example, the navigation unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a suggestion of an optimal navigation means.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The travel planning system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's health data (e.g., heart rate, blood pressure, number of steps, etc.) and supports health management during the trip. For example, the health management unit may suggest a break if the user's heart rate becomes high. The health management unit may also suggest a place to relax if the user's number of steps exceeds a certain standard. Furthermore, the health management unit may suggest meals and activities during the trip based on the user's health condition. This allows the user to enjoy the trip while maintaining their health.

[0110] The suggestion unit can estimate the user's emotions and adjust the suggestions of stopovers based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest relaxing places and activities. Also, if the user is excited, the suggestion unit can suggest active activities and tourist spots. Furthermore, the suggestion unit can adjust the timing of suggestions according to the user's emotions. For example, if the user is tired, the suggestion unit can refrain from making suggestions. In this way, the suggestion unit can improve the satisfaction of the trip by making suggestions according to the user's emotions.

[0111] The reservation unit can analyze the user's past reservation history and suggest the optimal reservation method. For example, it can preferentially display reservation sites that the user has used frequently in the past. It can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. Furthermore, it can also suggest reservation methods suitable for specific time periods or seasons based on the user's past reservation history. This makes it possible to streamline reservation work by providing the optimal reservation method based on the past reservation history.

[0112] The generation unit can estimate the user's emotions and adjust the way the itinerary is presented based on the estimated emotions. For example, if the user is relaxed, an itinerary including detailed information can be provided. Also, if the user is nervous, a simple and highly visible itinerary can be provided. Furthermore, the generation unit can adjust the color and design of the itinerary according to the user's emotions. In this way, by providing an itinerary according to the user's emotions, it is possible to improve satisfaction with travel planning.

[0113] The navigation unit can suggest an optimal route by referring to the user's past movement history. For example, the optimal route can be suggested based on routes the user has used in the past. In addition, a route that avoids congestion can be suggested based on the user's past movement history. Furthermore, the navigation unit can analyze the user's past movement history and suggest the most efficient route. This can improve the accuracy of navigation by providing an optimal route based on the user's past movement history.

[0114] The reception unit can estimate the user's emotions and adjust the input method of basic information based on the estimated emotions. For example, if the user is nervous, a simple and intuitive input method can be provided. Also, if the user is relaxed, detailed input items can be provided. Furthermore, the reception unit can adjust the timing of input according to the user's emotions. In this way, by providing an input method according to the user's emotions, the stress of input work can be reduced.

[0115] The reception unit can customize input items based on the user's current living situation and areas of interest. For example, places and events that the user showed interest in in recent social media activities are suggested as input items. Appropriate input items can also be displayed based on the user's current living situation (e.g., family trip, business trip, etc.). Furthermore, related input items can also be customized based on the user's areas of interest (e.g., history, nature, gourmet, etc.). This can make input work more efficient by providing input items according to the user's living situation and areas of interest.

[0116] The suggestion unit can estimate the user's emotions and determine the priority order of stopovers based on the estimated emotions. For example, if the user is excited, active activities can be preferentially suggested. Also, if the user is relaxed, places where the user can relax can be preferentially suggested. Furthermore, the timing of suggestions can be adjusted according to the user's emotions. In this way, the satisfaction of the trip can be improved by setting priorities according to the user's emotions.

[0117] The reservation unit can select the optimal reservation method in consideration of the user's geographical location information. For example, the reservation unit can give priority to reserving accommodations and restaurants close to the user's current location. Also, if the user is staying in a specific area, the reservation unit can give priority to reserving activities and events related to that area. Furthermore, the reservation unit can suggest the optimal reservation method based on the user's geographical location information. This can make reservation work more efficient by providing the optimal reservation method based on the geographical location information.

[0118] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, the tone and speed of the navigation voice guidance can be adjusted according to the user's emotions. In this way, by providing a display method according to the user's emotions, it is possible to reduce the stress of navigation.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit accepts basic information from the user. Basic information includes the schedule, means of transportation (air, bullet train, car, etc.), accommodation, desired sightseeing spots, and desired food. For example, the user might input "Traveling from March 1st to March 5th, means of transportation is the bullet train, accommodation is Tokyo, desired sightseeing spot is tourist spot A, and desired food is dish B." This information is input into the AI. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and create an optimal itinerary. The generation unit calculates efficient routes based on information such as the schedule, means of transportation, accommodation, desired sightseeing spots, and desired foods, and creates a schedule for each day. For example, the generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to calculate efficient routes and create a schedule for each day. Step 3: The suggestion unit suggests recommended stopovers other than the desired tourist spots based on the itinerary created by the generation unit. For example, it suggests visiting a nearby tourist spot D after visiting tourist spot A. The suggestion unit can also use the generation AI to suggest stopovers based on the user's preferences. Step 4: The reservation department checks availability and makes reservations for restaurants, experiences, and other things that require reservations. For example, it checks availability at restaurant C, which requires a reservation to eat dish B, and automatically makes a reservation if it is available. The reservation department can also use generative AI to search multiple reservation sites across the board and provide the best reservation options. Step 5: The navigation unit performs navigation in conjunction with the map application. For example, when the user heads to tourist spot A, the map application displays the route from the current location to tourist spot A and performs navigation. The navigation unit can also use the generation AI to estimate the user's emotions and adjust the navigation display method based on the estimated user's emotions.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned reception unit, generation unit, proposal unit, reservation unit, and navigation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives basic information from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates an optimal itinerary using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recommended stopovers using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and inquires about availability and makes a reservation. The navigation unit is realized, for example, by the control unit 46A of the smart device 14, and performs navigation in conjunction with a map application. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, generation unit, proposal unit, reservation unit, and navigation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives basic information from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an optimal itinerary using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes recommended stopovers using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inquires about availability and makes a reservation. The navigation unit is realized, for example, by the control unit 46A of the smart glasses 214 and performs navigation in conjunction with a map application. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, generation unit, proposal unit, reservation unit, and navigation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives basic information from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an optimal itinerary using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes recommended stopovers using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and inquires about availability and makes reservations. The navigation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and performs navigation in conjunction with a map application. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] Each of the multiple elements including the reception unit, the generation unit, the proposal unit, the reservation unit, and the navigation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and receives basic information from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates an optimal itinerary using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recommended stopovers using a generation AI. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and inquires about availability and makes a reservation. The navigation unit is realized, for example, by the control unit 46A of the robot 414, and performs navigation in conjunction with a map application. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A reception unit that receives basic information from a user; a generation unit that analyzes the information received by the reception unit and creates an efficient itinerary; A suggestion unit that suggests stopovers other than the desired sightseeing spot based on the itinerary created by the generation unit; A reservation department that checks availability of items that require reservations and makes reservations; A navigation unit that performs navigation in conjunction with a map application. A system characterized by: (Appendix 2) The suggestion unit, Suggesting stops based on user preferences 2. The system of claim 1, (Appendix 3) The reservation unit: Search across multiple booking sites 2. The system of claim 1, (Appendix 4) The generation unit is Calculate efficient routes based on your dates, transportation, accommodation, desired sightseeing spots, and food you want to eat, and create a schedule for each day. 2. The system of claim 1, (Appendix 5) The navigation unit includes: Display the route from your current location to your destination and perform navigation 2. The system of claim 1, (Appendix 6) The reception unit is Infer user emotions and adjust the input method of basic information based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyzes the user's past travel history and suggests the best way to input information 2. The system of claim 1, (Appendix 8) The reception unit is As you type, customize the input based on your current life situation and interests 2. The system of claim 1, (Appendix 9) The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The reception unit is As you type, it takes your geographic location into account to prioritize relevant input fields. 2. The system of claim 1, (Appendix 11) The reception unit is Analyzes your social media activity and suggests relevant entries as you type 2. The system of claim 1, (Appendix 12) The generation unit is Infer a user's emotions and adjust the way the itinerary is presented based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The generation unit is At generation time, adjust the detail of the itinerary based on the importance of the trip 2. The system of claim 1, (Appendix 14) The generation unit is During generation, different generation algorithms are applied depending on the trip category. 2. The system of claim 1, (Appendix 15) The generation unit is Infer a user's emotion and adjust the length of the itinerary based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The generation unit is At generation time, prioritize itineraries based on when the trip was submitted 2. The system of claim 1, (Appendix 17) The generation unit is At generation time, adjust the order of trip itineraries based on trip relevance 2. The system of claim 1, (Appendix 18) The suggestion unit, Estimate the user's emotions and adjust the method of suggesting places to stop by based on the estimated user's emotions 2. The system of claim 1, (Appendix 19) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the stops 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the stop. 2. The system of claim 1, (Appendix 21) The suggestion unit, Estimating user emotions and prioritizing destinations based on the estimated user emotions 2. The system of claim 1, (Appendix 22) The suggestion unit, When making proposals, consider the geographical distribution of stops. 2. The system of claim 1, (Appendix 23) The suggestion unit, When making a proposal, the accuracy of the proposal is improved by referring to related literature of the destination. 2. The system of claim 1, (Appendix 24) The reservation unit: To estimate a user's emotion and adjust a reservation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The reservation unit: When making a reservation, analyze the user's past reservation history to select the optimal reservation method 2. The system of claim 1, (Appendix 26) The reservation unit: At the time of booking, customize the booking experience based on the user's current life situation 2. The system of claim 1, (Appendix 27) The reservation unit: To estimate a user's emotion and prioritize reservations based on the estimated user's emotion. 2. The system of claim 1, (Appendix 28) The reservation unit: When making a reservation, consider the user's geographic location to determine the best way to make the reservation 2. The system of claim 1, (Appendix 29) The reservation unit: When booking, analyze users' social media activity to suggest options for booking 2. The system of claim 1, (Appendix 30) The navigation unit includes: Inferring a user's emotions and adjusting the navigation display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 31) The navigation unit includes: When navigating, the system suggests optimal routes by referencing the user's past travel history. 2. The system of claim 1, (Appendix 32) The navigation unit includes: When navigating, customize the navigation experience based on the user's current life situation 2. The system of claim 1, (Appendix 33) The navigation unit includes: Infer user emotions and determine navigation priorities based on the inferred user emotions 2. The system of claim 1, (Appendix 34) The navigation unit includes: When navigating, it takes the user's geographic location into account to suggest the best route 2. The system of claim 1, (Appendix 35) The navigation unit includes: When navigating, analyze the user's social media activity to suggest navigation options 2. The system of claim 1, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives basic information from a user; a generation unit that analyzes the information received by the reception unit and creates an efficient itinerary; A suggestion unit that suggests stopovers other than the desired sightseeing spot based on the itinerary created by the generation unit; A reservation department that checks availability of items that require reservations and makes reservations; A navigation unit that performs navigation in conjunction with a map application. A system characterized by:

2. The suggestion unit, Suggesting stops based on user preferences 2. The system of claim 1 .

3. The reservation unit: Search across multiple booking sites 2. The system of claim 1 .

4. The generation unit is Calculate efficient routes based on your dates, transportation, accommodation, desired sightseeing spots, and food you want to eat, and create a schedule for each day.

2. The system of claim 1 .

5. The navigation unit includes: Display the route from your current location to your destination and perform navigation 2. The system of claim 1 .

6. The reception unit is Infer user emotions and adjust the input method of basic information based on the estimated user emotions 2. The system of claim 1 .

7. The reception unit is Analyzes the user's past travel history and suggests the best way to input information 2. The system of claim 1 .

8. The reception unit is As you type, customize the input based on your current life situation and interests 2. The system of claim 1 .

9. The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion.

2. The system of claim 1 .

10. The reception unit is As you type, it takes your geographic location into account to prioritize relevant input fields.

2. The system of claim 1 .

11. The reception unit is Analyzes your social media activity and suggests relevant entries as you type 2. The system of claim 1 .

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