system

Abstract

The system according to this embodiment aims to propose the optimal travel plan based on the user's preferences and budget, and to support booking. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a reservation unit, and a suggestion unit. The reception unit receives input from the user regarding their travel preferences and budget. The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The reservation unit makes reservations for accommodations, flights, and tourist destinations based on the travel plan generated by the generation unit. The suggestion unit proposes unexpected and attractive places based on the travel plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose an optimal travel plan based on the user's preferences and budget and support reservations, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal travel plan based on the user's preferences and budget and support reservations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a reservation unit, and a suggestion unit. The reception unit receives input from the user regarding their travel preferences and budget. The generation unit analyzes the information received by the reception unit and generates an optimal travel plan. The reservation unit makes reservations for accommodations, flights, and tourist destinations based on the travel plan generated by the generation unit. The suggestion unit proposes unexpected and attractive places based on the travel plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal travel plan based on the user's preferences and budget, and support booking. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel plan suggestion system according to an embodiment of the present invention is a system that suggests an optimal travel plan based on the user's preferences and budget, and supports the booking of accommodations, flights, and tourist destinations. This travel plan suggestion system suggests an optimal travel plan based on the user's preferences and budget, and supports the booking of accommodations, flights, and tourist destinations. For example, the user inputs their travel wishes and budget into the travel plan suggestion system. For example, the user inputs wishes such as "I want to relax at a beach resort" and "My budget is under 100,000 yen." This information is input into the generating AI. Next, the generating AI analyzes the input information. The generating AI generates an optimal travel plan based on the user's preferences and budget. For example, in response to the wish to relax at a beach resort, the generating AI compares multiple beach resorts and suggests the optimal accommodations and flights within the budget. The generated travel plan includes bookings for accommodations, flights, and tourist destinations. For example, the generating AI suggests to the user the booking of a hotel and flights at a specific beach resort. It also supports the booking of tourist destinations and suggests tourist destinations that the user should visit. Furthermore, the generating AI also provides a function to explore the enjoyment and adventurousness of travel and suggest unexpected and attractive places. For example, if a user desires a beach resort, the generating AI will suggest nearby hidden attractions and local restaurants. This allows users to discover unexpected and appealing places, maximizing the enjoyment of their trip. This system makes it easy for users to create the perfect travel plan based on their preferences and budget. Furthermore, the unexpected and appealing place suggestions provided by the generating AI enhance the enjoyment and sense of adventure of the trip, allowing users to create special memories. In this way, the travel plan suggestion system can propose the best travel plan based on the user's preferences and budget, and support booking.

[0029] The travel plan suggestion system according to this embodiment comprises a reception unit, a generation unit, a booking unit, and a suggestion unit. The reception unit receives input from the user regarding their travel preferences and budget. The user's travel preferences include, for example, a travel destination, travel period, and purpose of travel, but are not limited to such examples. For example, the reception unit can receive information if the user desires a beach resort as a travel destination and sets a budget of 100,000 yen or less. The generation unit uses a generation AI to analyze the information received by the reception unit and generate an optimal travel plan. For example, the generation unit compares multiple travel plans based on the user's travel preferences and budget and selects the optimal plan. The generation unit can use a generation AI to suggest the optimal accommodation and flights based on the user's preferences and budget. For example, in response to a user's desire to relax at a beach resort, the generation unit compares multiple beach resorts and suggests the optimal accommodation and flights within the budget. The booking unit makes reservations for accommodation, flights, and tourist attractions based on the travel plan generated by the generation unit. For example, the booking unit supports the reservations of accommodation and flights suggested by the generation unit. The booking unit can also support users in booking tourist destinations they should visit. The suggestion unit proposes unexpected and attractive places based on the travel plan generated by the generation unit. For example, if a user wants a beach resort, the suggestion unit can suggest nearby hidden tourist spots or local restaurants. The suggestion unit can use generation AI to propose unexpected and attractive places based on the user's preferences and budget. As a result, the travel plan suggestion system according to the embodiment can propose the optimal travel plan based on the user's preferences and budget and support bookings.

[0030] The reception desk receives user input regarding travel preferences and budgets. User travel preferences include, but are not limited to, destination, duration, and purpose of travel. For example, if a user desires a beach resort as their destination and sets a budget of 100,000 yen or less, the reception desk can receive this information. Specifically, the reception desk provides web forms and mobile applications to efficiently collect user input. This allows users to easily input details such as destination, duration, purpose, budget, and desired activities. Furthermore, the reception desk analyzes user input in real time and requests additional information as needed. For example, if a user desires a beach resort, it can ask for specific beach names and preferred types of accommodation (hotel, villa, bungalow, etc.). The reception desk can also present more personalized questions by considering the user's past travel history and preferences. This allows the reception desk to accurately understand the user's detailed travel preferences and provide the information necessary for the next step, the generation desk, to generate the optimal travel plan. Additionally, the reception desk securely stores user input and implements security measures to protect privacy. This allows users to confidently enter their travel preferences and budget.

[0031] The generation unit uses a generation AI to analyze information received by the reception unit and generate the optimal travel plan. For example, the generation unit compares multiple travel plans based on the user's travel preferences and budget, and selects the best plan. Specifically, the generation AI searches a vast database for relevant travel information based on the user's input and selects the most suitable accommodation, flights, tourist destinations, and activities for the user's preferences. The generation AI uses natural language processing technology to understand the user's input and generate an appropriate travel plan. For example, if the user inputs "I want to relax at a beach resort," the generation AI will suggest accommodations and activities that offer a particularly relaxing environment among beach resorts. The generation AI also considers the user's budget and selects a plan that offers the best cost performance. For example, if the budget is within 100,000 yen, it will generate the most satisfying travel plan within that range. Furthermore, the generation AI can learn the user's past travel history and preferences to provide more personalized suggestions. As a result, the generation unit can quickly and accurately generate the optimal travel plan that meets the user's expectations.

[0032] The booking department makes reservations for accommodations, flights, and tourist attractions based on the travel plans generated by the generation department. For example, the booking department supports the booking of accommodations and flights suggested by the generation department. Specifically, the booking department checks the availability of accommodations and flights based on the travel plan selected by the user and handles the booking process. The booking department integrates with multiple booking sites and airline systems to provide the best booking options. For example, it checks the availability of the user's desired accommodations in real time and confirms the reservation at the most reasonable price. When booking flights, it selects the best flight considering the user's desired departure and arrival locations, departure time, and airline preferences. Furthermore, the booking department also supports the booking of tourist attractions and activities. For example, it makes reservations for tourist attractions the user wants to visit and activities they want to participate in, providing a smooth travel experience. The booking department centrally manages the user's booking information and can also support procedures for changes and cancellations as needed. In this way, the booking department provides comprehensive booking support so that users can prepare for their trip with peace of mind.

[0033] The suggestion unit proposes unexpected and attractive places based on the travel plan generated by the generation unit. For example, if a user is looking for a beach resort, the suggestion unit can suggest nearby hidden tourist spots or local restaurants. Specifically, the suggestion unit uses generational AI to search for additional tourist destinations and activities related to the user's travel plan and proposes places that will be new discoveries for the user. Based on the user's preferences and budget, the generational AI selects hidden gems not found in typical tourist guides, restaurants loved by locals, and activities that offer unique experiences. For example, a user visiting a beach resort could be suggested an eco-tour in a nearby nature reserve or a shopping experience at a local market. The suggestion unit also makes suggestions that take seasonal and event information into account, tailored to the user's travel plan. For example, it could introduce local festivals or special events held during the trip, providing the user with an unforgettable experience. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and appeal of its suggestions. This allows the suggestion unit to propose unexpected and attractive places for the user, further expanding the enjoyment of their trip.

[0034] The generation unit can suggest unique and attractive spots based on the user's travel history and reviews. For example, the generation unit can generate an optimal travel plan based on reviews of places the user has visited in the past. The generation unit can analyze the user's travel history and suggest places that have been highly rated by other travelers. The generation unit can also suggest places related to a specific theme. In this way, the generation unit can enhance the enjoyment of travel by suggesting unique and attractive spots based on the user's travel history and reviews. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel history and reviews into a generation AI and have the generation AI suggest unique and attractive spots.

[0035] The suggestion unit can propose unexpected and attractive places based on the user's preferences and budget. For example, if the user wants a beach resort, the suggestion unit can suggest nearby hidden tourist spots and local restaurants. The suggestion unit can also suggest places not listed in tourist guides or places only known to locals, based on the user's preferences and budget. In this way, the suggestion unit can explore the joy and adventure of travel by suggesting unexpected and attractive places based on the user's preferences and budget. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's preferences and budget into the generative AI and have the generative AI produce suggestions for unexpected and attractive places.

[0036] The booking department can support reservations for accommodations, flights, and tourist attractions. For example, the booking department can support reservations for accommodations and flights suggested by the generation department. The booking department can also support reservations for tourist attractions that the user should visit. For example, the booking department can support reservations for accommodations such as hotels, guesthouses, and inns. The booking department can support reservations for flights, including direct flights, connecting flights, and different airlines. The booking department can support reservations for tourist attractions such as historical sites, natural landscapes, and theme parks. In this way, the booking department can facilitate the user's travel planning by supporting reservations for accommodations, flights, and tourist attractions. Some or all of the above processing in the booking department may be performed using a generation AI, or it may be performed without a generation AI. For example, the booking department can input reservation information for accommodations and flights into a generation AI and have the generation AI execute the reservation procedure.

[0037] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display travel preferences and budgets that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest travel preferences and budgets related to specific seasons or events based on the user's past travel history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past travel history and improve the efficiency of input. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's past travel history into a generative AI and have the generative AI suggest the optimal input method.

[0038] The reception desk can filter the user's current lifestyle and areas of interest when they input their travel preferences and budget. For example, the reception desk can prioritize suggesting relaxing travel destinations based on the user's recent lifestyle. The reception desk can also prompt the user to input relevant travel preferences and budgets based on their areas of interest (e.g., outdoor activities, cultural experiences). The reception desk can suggest the most suitable travel plan based on the user's current lifestyle (e.g., work schedule, family structure). In this way, the reception desk can suggest a more appropriate travel plan by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's current lifestyle and areas of interest into a generative AI and have the generative AI perform the filtering.

[0039] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input their travel preferences and budget. For example, the reception desk can suggest nearby travel destinations based on the user's current location. The reception desk can suggest an optimal travel budget based on the user's geographical location. The reception desk can prioritize displaying travel destinations that are easily accessible from the user's current location. In this way, the reception desk can improve the accuracy of travel plans by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generation AI. For example, the reception desk can input the user's geographical location information into a generation AI and have the generation AI prioritize the input of highly relevant information.

[0040] The reception desk can analyze the user's social media activity and prompt them to input relevant information when they enter their travel preferences and budget. For example, the reception desk can suggest travel destinations of interest based on the user's social media posts. The reception desk can also prompt the user to input relevant travel preferences and budgets by referring to the travel history of the user's friends on social media. The reception desk can suggest the optimal travel plan based on the user's social media activity. In this way, the reception desk can improve the accuracy of the travel plan by analyzing the user's social media activity and prompting them to input relevant information. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's social media activity into a generative AI and have the generative AI input the relevant information.

[0041] The generation unit can generate the optimal travel plan by referring to the user's past travel history and reviews when generating a travel plan. For example, the generation unit can generate the optimal travel plan based on reviews of places the user has visited in the past. The generation unit can suggest routes that avoid crowds based on the user's past travel history. The generation unit can analyze the user's past travel history and generate the most efficient travel plan. In this way, the generation unit can generate the optimal travel plan by referring to the user's past travel history and reviews, thereby improving user satisfaction. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past travel history and reviews into a generation AI and have the generation AI perform the generation of the optimal travel plan.

[0042] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest when generating them. For example, the generation unit can generate a relaxing travel plan based on the user's recent lifestyle. The generation unit can generate relevant travel plans based on the user's areas of interest (e.g., outdoor activities, cultural experiences). The generation unit can generate the optimal travel plan based on the user's current lifestyle (e.g., work schedule, family structure). In this way, the generation unit can provide the user with the optimal travel plan by customizing it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current lifestyle and areas of interest into a generation AI and have the generation AI perform the plan customization.

[0043] The generation unit can generate an optimal travel plan by considering the user's geographical location information. For example, the generation unit can prioritize suggesting nearby travel destinations based on the user's current location. The generation unit can also suggest an optimal travel budget based on the user's geographical location information. The generation unit can prioritize displaying travel destinations that are easily accessible from the user's current location. In this way, the generation unit can generate an optimal travel plan by considering the user's geographical location information, thereby improving user satisfaction. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of an optimal plan.

[0044] The generation unit can analyze the user's social media activity and generate relevant plans when generating travel plans. For example, the generation unit can suggest travel destinations of interest based on the user's social media posts. The generation unit can generate relevant travel plans by referring to the travel history of the user's friends on social media. The generation unit can generate the optimal travel plan based on the user's social media activity. In this way, the generation unit can generate relevant plans by analyzing the user's social media activity and improve user satisfaction. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity into a generation AI and have the generation AI perform the generation of relevant plans.

[0045] The reservation department can suggest the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department can automatically display accommodations or flights that the user has frequently booked in the past as options. The reservation department can prioritize suggesting reservation methods that the user has used in the past (online, telephone, etc.). The reservation department can predict and suggest reservation methods related to specific seasons or events based on the user's past reservation history. In this way, the reservation department can suggest the optimal reservation method by referring to the user's past reservation history and improve the efficiency of reservations. Some or all of the above processes in the reservation department may be performed using or without a generative AI. For example, the reservation department can input the user's past reservation history into a generative AI and have the generative AI suggest the optimal reservation method.

[0046] The reservation department can customize reservation details based on the user's current lifestyle and areas of interest at the time of booking. For example, the reservation department can prioritize suggesting relaxing accommodations based on the user's recent lifestyle. The reservation department can suggest relevant accommodations and flights based on the user's areas of interest (e.g., outdoor activities, cultural experiences). The reservation department can suggest the most suitable reservation details based on the user's current lifestyle (e.g., work schedule, family structure). In this way, the reservation department can provide the user with the most suitable reservation details by customizing them based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reservation department may be performed using or without a generative AI. For example, the reservation department can input the user's current lifestyle and areas of interest into a generative AI and have the generative AI perform the customization of the reservation details.

[0047] The reservation department can suggest the optimal reservation method when a reservation is made, taking into account the user's geographical location. For example, the reservation department can prioritize suggesting nearby accommodations and flights based on the user's current location. The reservation department can also suggest an optimal reservation budget based on the user's geographical location. The reservation department can prioritize displaying accommodations and flights that are easily accessible from the user's current location. In this way, the reservation department can suggest the optimal reservation method by considering the user's geographical location, thereby improving user convenience. Some or all of the above processing in the reservation department may be performed using a generation AI, or not. For example, the reservation department can input the user's geographical location information into a generation AI and have the generation AI suggest the optimal reservation method.

[0048] The booking department can analyze a user's social media activity during the booking process and suggest relevant booking options. For example, it can suggest accommodations or flights of interest based on the user's social media posts. It can also suggest relevant booking options by referencing the travel history of the user's friends on social media. Finally, it can suggest the most suitable booking options based on the user's social media activity. This allows the booking department to analyze the user's social media activity, suggest relevant booking options, and improve user satisfaction. Some or all of the above processes in the booking department may be performed using or without a generative AI. For example, the booking department can input the user's social media activity into a generative AI and have the generative AI suggest relevant booking options.

[0049] The suggestion unit can suggest the most suitable locations by referring to the user's past travel history and reviews when suggesting attractive places. For example, the suggestion unit can suggest the most attractive places based on reviews of places the user has visited in the past. The suggestion unit can suggest attractive places that avoid crowds based on the user's past travel history. The suggestion unit can analyze the user's past travel history and suggest the most efficient attractive places. In this way, the suggestion unit can suggest the most attractive places by referring to the user's past travel history and reviews, thereby improving user satisfaction. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's past travel history and reviews into a generative AI and have the generative AI perform the task of suggesting the most suitable places.

[0050] The suggestion unit can customize its suggestions for attractive places based on the user's current lifestyle and areas of interest. For example, the suggestion unit can suggest attractive places where the user can relax based on their recent lifestyle. The suggestion unit can suggest attractive places related to the user's areas of interest (e.g., outdoors, cultural experiences, etc.). The suggestion unit can suggest the most suitable attractive places based on the user's current lifestyle (e.g., work schedule, family structure, etc.). In this way, the suggestion unit can provide the user with the most suitable suggestions by customizing the suggestions based on the user's current lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's current lifestyle and areas of interest into the generative AI and have the generative AI perform the customization of the suggestions.

[0051] The suggestion unit can suggest the most suitable location by considering the user's geographical location when proposing attractive places. For example, the suggestion unit can prioritize suggesting nearby attractive places based on the user's current location. The suggestion unit can also suggest an optimal travel budget based on the user's geographical location. The suggestion unit can prioritize displaying attractive places that are easily accessible from the user's current location. In this way, the suggestion unit can suggest the most suitable attractive places by considering the user's geographical location, thereby improving user satisfaction. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI perform the task of suggesting the most suitable location.

[0052] The suggestion unit can analyze the user's social media activity and suggest relevant locations when proposing attractive places. For example, the suggestion unit can suggest attractive places of interest based on the user's social media posts. The suggestion unit can suggest relevant attractive places by referring to the travel history of the user's friends on social media. The suggestion unit can suggest the most attractive places based on the user's social media activity. In this way, the suggestion unit can improve user satisfaction by suggesting relevant places through analysis of the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI perform the task of suggesting relevant places.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The generation unit can customize travel plans based on the user's past travel history and reviews. For example, it can refer to reviews of places the user has visited in the past and suggest places highly rated by other travelers with similar preferences. It can also consider the crowd levels of places the user has visited in the past and suggest the best time and route to avoid crowds. By analyzing the user's past travel history, it can generate efficient travel plans. In this way, the generation unit can provide more satisfying travel plans by utilizing the user's past travel history and reviews.

[0055] The suggestion function can customize travel plans based on the user's current lifestyle and areas of interest. For example, it can suggest relaxing travel destinations based on the user's recent lifestyle. It can suggest relevant travel plans based on the user's areas of interest (e.g., outdoor activities, cultural experiences). It can suggest the optimal travel plan based on the user's current lifestyle (e.g., work schedule, family structure). As a result, the suggestion function can provide more appropriate travel plans based on the user's current lifestyle and areas of interest.

[0056] The reception desk can adjust how users input their travel preferences and budget, taking into account their geographical location. For example, it can prioritize suggesting nearby travel destinations based on the user's current location. It can also suggest an optimal travel budget based on the user's geographical location. It can prioritize displaying travel destinations that are easily accessible from the user's current location. In this way, the reception desk can improve the accuracy of travel plans by prioritizing the input of highly relevant information based on the user's geographical location.

[0057] The generation unit can analyze the user's social media activity and customize travel plans. For example, it can suggest travel destinations of interest based on the user's social media posts. It can also generate relevant travel plans by referencing the travel history of the user's friends on social media. It can generate the optimal travel plan based on the user's social media activity. In this way, the generation unit can analyze the user's social media activity to generate relevant plans and improve user satisfaction.

[0058] The suggestion function can refer to the user's past travel history and reviews to suggest attractive destinations. For example, based on reviews of places the user has visited in the past, it can suggest places highly rated by other travelers with similar preferences. It can analyze the user's past travel history to suggest the best time and route to avoid crowds. It can refer to the user's past travel history to suggest efficient travel plans. In this way, the suggestion function can leverage the user's past travel history and reviews to suggest more satisfying and attractive destinations.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives the user's travel preferences and budget. These preferences include destination, duration, and purpose of travel. For example, if a user wants to travel to a beach resort and sets a budget of 100,000 yen or less, this information can be received. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and generate the optimal travel plan. Based on the user's travel preferences and budget, the generation unit compares multiple travel plans and selects the best one. For example, if the user wants to relax at a beach resort, the generation unit compares several beach resorts and suggests the best accommodation and flights within the budget. Step 3: The booking unit makes reservations for accommodations, flights, and tourist attractions based on the travel plan generated by the generation unit. For example, it supports the booking of accommodations and flights suggested by the generation unit. It can also support the booking of tourist attractions that the user should visit. Step 4: The suggestion unit proposes unexpected and attractive locations based on the travel plan generated by the generation unit. For example, if the user wants a beach resort, it can suggest nearby hidden tourist spots or local restaurants. Using generation AI, unexpected and attractive locations can be suggested based on the user's preferences and budget.

[0061] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system that suggests an optimal travel plan based on the user's preferences and budget, and supports the booking of accommodations, flights, and tourist destinations. This travel plan suggestion system suggests an optimal travel plan based on the user's preferences and budget, and supports the booking of accommodations, flights, and tourist destinations. For example, the user inputs their travel wishes and budget into the travel plan suggestion system. For example, the user inputs wishes such as "I want to relax at a beach resort" and "My budget is under 100,000 yen." This information is input into the generating AI. Next, the generating AI analyzes the input information. The generating AI generates an optimal travel plan based on the user's preferences and budget. For example, in response to the wish to relax at a beach resort, the generating AI compares multiple beach resorts and suggests the optimal accommodations and flights within the budget. The generated travel plan includes bookings for accommodations, flights, and tourist destinations. For example, the generating AI suggests to the user the booking of a hotel and flights at a specific beach resort. It also supports the booking of tourist destinations and suggests tourist destinations that the user should visit. Furthermore, the generating AI also provides a function to explore the enjoyment and adventurousness of travel and suggest unexpected and attractive places. For example, if a user desires a beach resort, the generating AI will suggest nearby hidden attractions and local restaurants. This allows users to discover unexpected and appealing places, maximizing the enjoyment of their trip. This system makes it easy for users to create the perfect travel plan based on their preferences and budget. Furthermore, the unexpected and appealing place suggestions provided by the generating AI enhance the enjoyment and sense of adventure of the trip, allowing users to create special memories. In this way, the travel plan suggestion system can propose the best travel plan based on the user's preferences and budget, and support booking.

[0062] The travel plan suggestion system according to this embodiment comprises a reception unit, a generation unit, a booking unit, and a suggestion unit. The reception unit receives input from the user regarding their travel preferences and budget. The user's travel preferences include, for example, a travel destination, travel period, and purpose of travel, but are not limited to such examples. For example, the reception unit can receive information if the user desires a beach resort as a travel destination and sets a budget of 100,000 yen or less. The generation unit uses a generation AI to analyze the information received by the reception unit and generate an optimal travel plan. For example, the generation unit compares multiple travel plans based on the user's travel preferences and budget and selects the optimal plan. The generation unit can use a generation AI to suggest the optimal accommodation and flights based on the user's preferences and budget. For example, in response to a user's desire to relax at a beach resort, the generation unit compares multiple beach resorts and suggests the optimal accommodation and flights within the budget. The booking unit makes reservations for accommodation, flights, and tourist attractions based on the travel plan generated by the generation unit. For example, the booking unit supports the reservations of accommodation and flights suggested by the generation unit. The booking unit can also support users in booking tourist destinations they should visit. The suggestion unit proposes unexpected and attractive places based on the travel plan generated by the generation unit. For example, if a user wants a beach resort, the suggestion unit can suggest nearby hidden tourist spots or local restaurants. The suggestion unit can use generation AI to propose unexpected and attractive places based on the user's preferences and budget. As a result, the travel plan suggestion system according to the embodiment can propose the optimal travel plan based on the user's preferences and budget and support bookings.

[0063] The reception desk receives user input regarding travel preferences and budgets. User travel preferences include, but are not limited to, destination, duration, and purpose of travel. For example, if a user desires a beach resort as their destination and sets a budget of 100,000 yen or less, the reception desk can receive this information. Specifically, the reception desk provides web forms and mobile applications to efficiently collect user input. This allows users to easily input details such as destination, duration, purpose, budget, and desired activities. Furthermore, the reception desk analyzes user input in real time and requests additional information as needed. For example, if a user desires a beach resort, it can ask for specific beach names and preferred types of accommodation (hotel, villa, bungalow, etc.). The reception desk can also present more personalized questions by considering the user's past travel history and preferences. This allows the reception desk to accurately understand the user's detailed travel preferences and provide the information necessary for the next step, the generation desk, to generate the optimal travel plan. Additionally, the reception desk securely stores user input and implements security measures to protect privacy. This allows users to confidently enter their travel preferences and budget.

[0064] The generation unit uses a generation AI to analyze information received by the reception unit and generate the optimal travel plan. For example, the generation unit compares multiple travel plans based on the user's travel preferences and budget, and selects the best plan. Specifically, the generation AI searches a vast database for relevant travel information based on the user's input and selects the most suitable accommodation, flights, tourist destinations, and activities for the user's preferences. The generation AI uses natural language processing technology to understand the user's input and generate an appropriate travel plan. For example, if the user inputs "I want to relax at a beach resort," the generation AI will suggest accommodations and activities that offer a particularly relaxing environment among beach resorts. The generation AI also considers the user's budget and selects a plan that offers the best cost performance. For example, if the budget is within 100,000 yen, it will generate the most satisfying travel plan within that range. Furthermore, the generation AI can learn the user's past travel history and preferences to provide more personalized suggestions. As a result, the generation unit can quickly and accurately generate the optimal travel plan that meets the user's expectations.

[0065] The booking department makes reservations for accommodations, flights, and tourist attractions based on the travel plans generated by the generation department. For example, the booking department supports the booking of accommodations and flights suggested by the generation department. Specifically, the booking department checks the availability of accommodations and flights based on the travel plan selected by the user and handles the booking process. The booking department integrates with multiple booking sites and airline systems to provide the best booking options. For example, it checks the availability of the user's desired accommodations in real time and confirms the reservation at the most reasonable price. When booking flights, it selects the best flight considering the user's desired departure and arrival locations, departure time, and airline preferences. Furthermore, the booking department also supports the booking of tourist attractions and activities. For example, it makes reservations for tourist attractions the user wants to visit and activities they want to participate in, providing a smooth travel experience. The booking department centrally manages the user's booking information and can also support procedures for changes and cancellations as needed. In this way, the booking department provides comprehensive booking support so that users can prepare for their trip with peace of mind.

[0066] The suggestion unit proposes unexpected and attractive places based on the travel plan generated by the generation unit. For example, if a user is looking for a beach resort, the suggestion unit can suggest nearby hidden tourist spots or local restaurants. Specifically, the suggestion unit uses generational AI to search for additional tourist destinations and activities related to the user's travel plan and proposes places that will be new discoveries for the user. Based on the user's preferences and budget, the generational AI selects hidden gems not found in typical tourist guides, restaurants loved by locals, and activities that offer unique experiences. For example, a user visiting a beach resort could be suggested an eco-tour in a nearby nature reserve or a shopping experience at a local market. The suggestion unit also makes suggestions that take seasonal and event information into account, tailored to the user's travel plan. For example, it could introduce local festivals or special events held during the trip, providing the user with an unforgettable experience. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and appeal of its suggestions. This allows the suggestion unit to propose unexpected and attractive places for the user, further expanding the enjoyment of their trip.

[0067] The generation unit can suggest unique and attractive spots based on the user's travel history and reviews. For example, the generation unit can generate an optimal travel plan based on reviews of places the user has visited in the past. The generation unit can analyze the user's travel history and suggest places that have been highly rated by other travelers. The generation unit can also suggest places related to a specific theme. In this way, the generation unit can enhance the enjoyment of travel by suggesting unique and attractive spots based on the user's travel history and reviews. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel history and reviews into a generation AI and have the generation AI suggest unique and attractive spots.

[0068] The suggestion unit can propose unexpected and attractive places based on the user's preferences and budget. For example, if the user wants a beach resort, the suggestion unit can suggest nearby hidden tourist spots and local restaurants. The suggestion unit can also suggest places not listed in tourist guides or places only known to locals, based on the user's preferences and budget. In this way, the suggestion unit can explore the joy and adventure of travel by suggesting unexpected and attractive places based on the user's preferences and budget. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's preferences and budget into the generative AI and have the generative AI produce suggestions for unexpected and attractive places.

[0069] The booking department can support reservations for accommodations, flights, and tourist attractions. For example, the booking department can support reservations for accommodations and flights suggested by the generation department. The booking department can also support reservations for tourist attractions that the user should visit. For example, the booking department can support reservations for accommodations such as hotels, guesthouses, and inns. The booking department can support reservations for flights, including direct flights, connecting flights, and different airlines. The booking department can support reservations for tourist attractions such as historical sites, natural landscapes, and theme parks. In this way, the booking department can facilitate the user's travel planning by supporting reservations for accommodations, flights, and tourist attractions. Some or all of the above processing in the booking department may be performed using a generation AI, or it may be performed without a generation AI. For example, the booking department can input reservation information for accommodations and flights into a generation AI and have the generation AI execute the reservation procedure.

[0070] The reception desk can estimate the user's emotions and adjust the input method for travel preferences and budget based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel preferences and budget. In this way, the reception desk can improve user convenience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display travel preferences and budgets that the user has frequently entered in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest travel preferences and budgets related to specific seasons or events based on the user's past travel history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past travel history and improve the efficiency of input. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's past travel history into a generative AI and have the generative AI suggest the optimal input method.

[0072] The reception desk can filter the user's current lifestyle and areas of interest when they input their travel preferences and budget. For example, the reception desk can prioritize suggesting relaxing travel destinations based on the user's recent lifestyle. The reception desk can also prompt the user to input relevant travel preferences and budgets based on their areas of interest (e.g., outdoor activities, cultural experiences). The reception desk can suggest the most suitable travel plan based on the user's current lifestyle (e.g., work schedule, family structure). In this way, the reception desk can suggest a more appropriate travel plan by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's current lifestyle and areas of interest into a generative AI and have the generative AI perform the filtering.

[0073] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input items and simplifying other items. If the user is relaxed, the reception desk can display detailed input items and provide a customizable input method. If the user is in a hurry, the reception desk can display only the most important input items to allow for quick input. In this way, the reception desk can improve user convenience by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input their travel preferences and budget. For example, the reception desk can suggest nearby travel destinations based on the user's current location. The reception desk can suggest an optimal travel budget based on the user's geographical location. The reception desk can prioritize displaying travel destinations that are easily accessible from the user's current location. In this way, the reception desk can improve the accuracy of travel plans by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using or without a generation AI. For example, the reception desk can input the user's geographical location information into a generation AI and have the generation AI prioritize the input of highly relevant information.

[0075] The reception desk can analyze the user's social media activity and prompt them to input relevant information when they enter their travel preferences and budget. For example, the reception desk can suggest travel destinations of interest based on the user's social media posts. The reception desk can also prompt the user to input relevant travel preferences and budgets by referring to the travel history of the user's friends on social media. The reception desk can suggest the optimal travel plan based on the user's social media activity. In this way, the reception desk can improve the accuracy of the travel plan by analyzing the user's social media activity and prompting them to input relevant information. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's social media activity into a generative AI and have the generative AI input the relevant information.

[0076] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit can generate a travel plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal travel plan by adjusting the travel plan generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the travel plan generation method.

[0077] The generation unit can generate the optimal travel plan by referring to the user's past travel history and reviews when generating a travel plan. For example, the generation unit can generate the optimal travel plan based on reviews of places the user has visited in the past. The generation unit can suggest routes that avoid crowds based on the user's past travel history. The generation unit can analyze the user's past travel history and generate the most efficient travel plan. In this way, the generation unit can generate the optimal travel plan by referring to the user's past travel history and reviews, thereby improving user satisfaction. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past travel history and reviews into a generation AI and have the generation AI perform the generation of the optimal travel plan.

[0078] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest when generating them. For example, the generation unit can generate a relaxing travel plan based on the user's recent lifestyle. The generation unit can generate relevant travel plans based on the user's areas of interest (e.g., outdoor activities, cultural experiences). The generation unit can generate the optimal travel plan based on the user's current lifestyle (e.g., work schedule, family structure). In this way, the generation unit can provide the user with the optimal travel plan by customizing it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current lifestyle and areas of interest into a generation AI and have the generation AI perform the plan customization.

[0079] The generation unit can estimate the user's emotions and prioritize travel plans based on those emotions. For example, if the user is stressed, the generation unit will prioritize suggesting relaxing travel plans. If the user is relaxed, the generation unit can suggest adventurous travel plans. If the user is in a hurry, the generation unit can prioritize suggesting travel plans that can be executed quickly. In this way, the generation unit can provide the user with the optimal travel plan by prioritizing travel plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of travel plans.

[0080] The generation unit can generate an optimal travel plan by considering the user's geographical location information. For example, the generation unit can prioritize suggesting nearby travel destinations based on the user's current location. The generation unit can also suggest an optimal travel budget based on the user's geographical location information. The generation unit can prioritize displaying travel destinations that are easily accessible from the user's current location. In this way, the generation unit can generate an optimal travel plan by considering the user's geographical location information, thereby improving user satisfaction. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of an optimal plan.

[0081] The generation unit can analyze the user's social media activity and generate relevant plans when generating travel plans. For example, the generation unit can suggest travel destinations of interest based on the user's social media posts. The generation unit can generate relevant travel plans by referring to the travel history of the user's friends on social media. The generation unit can generate the optimal travel plan based on the user's social media activity. In this way, the generation unit can generate relevant plans by analyzing the user's social media activity and improve user satisfaction. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity into a generation AI and have the generation AI perform the generation of relevant plans.

[0082] The reservation system can estimate the user's emotions and adjust the reservation method based on those emotions. For example, if the user is stressed, the system can provide a simple interface and minimize the reservation process. If the user is relaxed, the system can provide detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, the system can prioritize voice input to allow for quick reservations. In this way, the reservation system can improve user convenience by adjusting the reservation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using or without generative AI. For example, the reservation system can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reservation method.

[0083] The reservation department can suggest the optimal reservation method by referring to the user's past reservation history when a reservation is made. For example, the reservation department can automatically display accommodations or flights that the user has frequently booked in the past as options. The reservation department can prioritize suggesting reservation methods that the user has used in the past (online, telephone, etc.). The reservation department can predict and suggest reservation methods related to specific seasons or events based on the user's past reservation history. In this way, the reservation department can suggest the optimal reservation method by referring to the user's past reservation history and improve the efficiency of reservations. Some or all of the above processes in the reservation department may be performed using or without a generative AI. For example, the reservation department can input the user's past reservation history into a generative AI and have the generative AI suggest the optimal reservation method.

[0084] The reservation department can customize reservation details based on the user's current lifestyle and areas of interest at the time of booking. For example, the reservation department can prioritize suggesting relaxing accommodations based on the user's recent lifestyle. The reservation department can suggest relevant accommodations and flights based on the user's areas of interest (e.g., outdoor activities, cultural experiences). The reservation department can suggest the most suitable reservation details based on the user's current lifestyle (e.g., work schedule, family structure). In this way, the reservation department can provide the user with the most suitable reservation details by customizing them based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reservation department may be performed using or without a generative AI. For example, the reservation department can input the user's current lifestyle and areas of interest into a generative AI and have the generative AI perform the customization of the reservation details.

[0085] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if the user is stressed, the system can prioritize displaying important booking items and simplifying others. If the user is relaxed, the system can display detailed booking items and provide a customizable booking method. If the user is in a hurry, the system can display only the most important booking items to allow for quick booking. In this way, the booking system can provide the user with the best possible booking method by prioritizing bookings according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using or without generative AI. For example, the booking system can input user emotion data into a generative AI and have the generative AI determine the booking priorities.

[0086] The reservation department can suggest the optimal reservation method when a reservation is made, taking into account the user's geographical location. For example, the reservation department can prioritize suggesting nearby accommodations and flights based on the user's current location. The reservation department can also suggest an optimal reservation budget based on the user's geographical location. The reservation department can prioritize displaying accommodations and flights that are easily accessible from the user's current location. In this way, the reservation department can suggest the optimal reservation method by considering the user's geographical location, thereby improving user convenience. Some or all of the above processing in the reservation department may be performed using a generation AI, or not. For example, the reservation department can input the user's geographical location information into a generation AI and have the generation AI suggest the optimal reservation method.

[0087] The booking department can analyze a user's social media activity during the booking process and suggest relevant booking options. For example, it can suggest accommodations or flights of interest based on the user's social media posts. It can also suggest relevant booking options by referencing the travel history of the user's friends on social media. Finally, it can suggest the most suitable booking options based on the user's social media activity. This allows the booking department to analyze the user's social media activity, suggest relevant booking options, and improve user satisfaction. Some or all of the above processes in the booking department may be performed using or without a generative AI. For example, the booking department can input the user's social media activity into a generative AI and have the generative AI suggest relevant booking options.

[0088] The suggestion unit can estimate the user's emotions and adjust how it suggests attractive locations based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest attractive locations that proceed at a leisurely pace. If the user is in a hurry, the suggestion unit can suggest attractive locations that emphasize the shortest route. If the user is excited, the suggestion unit can suggest attractive locations with visually stimulating effects. In this way, the suggestion unit can provide the user with the most suitable suggestions by adjusting how it suggests attractive locations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestion method.

[0089] The suggestion unit can suggest the most suitable locations by referring to the user's past travel history and reviews when suggesting attractive places. For example, the suggestion unit can suggest the most attractive places based on reviews of places the user has visited in the past. The suggestion unit can suggest attractive places that avoid crowds based on the user's past travel history. The suggestion unit can analyze the user's past travel history and suggest the most efficient attractive places. In this way, the suggestion unit can suggest the most attractive places by referring to the user's past travel history and reviews, thereby improving user satisfaction. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's past travel history and reviews into a generative AI and have the generative AI perform the task of suggesting the most suitable places.

[0090] The suggestion unit can customize its suggestions for attractive places based on the user's current lifestyle and areas of interest. For example, the suggestion unit can suggest attractive places where the user can relax based on their recent lifestyle. The suggestion unit can suggest attractive places related to the user's areas of interest (e.g., outdoors, cultural experiences, etc.). The suggestion unit can suggest the most suitable attractive places based on the user's current lifestyle (e.g., work schedule, family structure, etc.). In this way, the suggestion unit can provide the user with the most suitable suggestions by customizing the suggestions based on the user's current lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's current lifestyle and areas of interest into the generative AI and have the generative AI perform the customization of the suggestions.

[0091] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting relaxing and appealing places. If the user is relaxed, the suggestion unit can suggest adventurous and appealing places. If the user is in a hurry, the suggestion unit can prioritize suggesting places that can be done quickly. In this way, the suggestion unit can provide the user with the most suitable suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0092] The suggestion unit can suggest the most suitable location by considering the user's geographical location when proposing attractive places. For example, the suggestion unit can prioritize suggesting nearby attractive places based on the user's current location. The suggestion unit can also suggest an optimal travel budget based on the user's geographical location. The suggestion unit can prioritize displaying attractive places that are easily accessible from the user's current location. In this way, the suggestion unit can suggest the most suitable attractive places by considering the user's geographical location, thereby improving user satisfaction. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI perform the task of suggesting the most suitable location.

[0093] The suggestion unit can analyze the user's social media activity and suggest relevant locations when proposing attractive places. For example, the suggestion unit can suggest attractive places of interest based on the user's social media posts. The suggestion unit can suggest relevant attractive places by referring to the travel history of the user's friends on social media. The suggestion unit can suggest the most attractive places based on the user's social media activity. In this way, the suggestion unit can improve user satisfaction by suggesting relevant places through analysis of the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI perform the task of suggesting relevant places.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The suggestion function can estimate the user's emotions and adjust how travel plans are suggested based on those estimates. For example, if the user is stressed, it will prioritize suggesting relaxing destinations and activities. If the user is excited, it can suggest adventurous activities and exciting tourist destinations. If the user is relaxed, it can suggest tourist destinations and activities that can be enjoyed at a leisurely pace. In this way, the suggestion function can provide the optimal travel plan according to the user's emotions.

[0096] The generation unit can customize travel plans based on the user's past travel history and reviews. For example, it can refer to reviews of places the user has visited in the past and suggest places highly rated by other travelers with similar preferences. It can also consider the crowd levels of places the user has visited in the past and suggest the best time and route to avoid crowds. By analyzing the user's past travel history, it can generate efficient travel plans. In this way, the generation unit can provide more satisfying travel plans by utilizing the user's past travel history and reviews.

[0097] The reception desk can estimate the user's emotions and adjust how they input their travel preferences and budget based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow them to quickly input their travel preferences and budget. In this way, the reception desk can improve user convenience by adjusting the input method according to the user's emotions.

[0098] The suggestion function can customize travel plans based on the user's current lifestyle and areas of interest. For example, it can suggest relaxing travel destinations based on the user's recent lifestyle. It can suggest relevant travel plans based on the user's areas of interest (e.g., outdoor activities, cultural experiences). It can suggest the optimal travel plan based on the user's current lifestyle (e.g., work schedule, family structure). As a result, the suggestion function can provide more appropriate travel plans based on the user's current lifestyle and areas of interest.

[0099] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate a travel plan that emphasizes the shortest route. If the user is excited, it can generate a travel plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal travel plan by adjusting the travel plan generation method according to the user's emotions.

[0100] The reception desk can adjust how users input their travel preferences and budget, taking into account their geographical location. For example, it can prioritize suggesting nearby travel destinations based on the user's current location. It can also suggest an optimal travel budget based on the user's geographical location. It can prioritize displaying travel destinations that are easily accessible from the user's current location. In this way, the reception desk can improve the accuracy of travel plans by prioritizing the input of highly relevant information based on the user's geographical location.

[0101] The suggestion function can estimate the user's emotions and adjust how attractive locations are suggested based on those emotions. For example, if the user is relaxed, it can suggest attractive locations that proceed at a leisurely pace. If the user is in a hurry, it can suggest attractive locations that emphasize the shortest route. If the user is excited, it can suggest attractive locations with visually stimulating effects. In this way, the suggestion function can provide the user with the most suitable suggestions by adjusting how attractive locations are suggested according to the user's emotions.

[0102] The generation unit can analyze the user's social media activity and customize travel plans. For example, it can suggest travel destinations of interest based on the user's social media posts. It can also generate relevant travel plans by referencing the travel history of the user's friends on social media. It can generate the optimal travel plan based on the user's social media activity. In this way, the generation unit can analyze the user's social media activity to generate relevant plans and improve user satisfaction.

[0103] The reservation system can estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the reservation process. If the user is relaxed, it can offer detailed reservation options and suggest a customizable reservation method. If the user is in a hurry, it can prioritize voice input to allow for quick reservations. In this way, the reservation system can improve user convenience by adjusting the reservation process according to the user's emotions.

[0104] The suggestion function can refer to the user's past travel history and reviews to suggest attractive destinations. For example, based on reviews of places the user has visited in the past, it can suggest places highly rated by other travelers with similar preferences. It can analyze the user's past travel history to suggest the best time and route to avoid crowds. It can refer to the user's past travel history to suggest efficient travel plans. In this way, the suggestion function can leverage the user's past travel history and reviews to suggest more satisfying and attractive destinations.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives the user's travel preferences and budget. These preferences include destination, duration, and purpose of travel. For example, if a user wants to travel to a beach resort and sets a budget of 100,000 yen or less, this information can be received. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and generate the optimal travel plan. Based on the user's travel preferences and budget, the generation unit compares multiple travel plans and selects the best one. For example, if the user wants to relax at a beach resort, the generation unit compares several beach resorts and suggests the best accommodation and flights within the budget. Step 3: The booking unit makes reservations for accommodations, flights, and tourist attractions based on the travel plan generated by the generation unit. For example, it supports the booking of accommodations and flights suggested by the generation unit. It can also support the booking of tourist attractions that the user should visit. Step 4: The suggestion unit proposes unexpected and attractive locations based on the travel plan generated by the generation unit. For example, if the user wants a beach resort, it can suggest nearby hidden tourist spots or local restaurants. Using generation AI, unexpected and attractive locations can be suggested based on the user's preferences and budget.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input from the user regarding their travel preferences and budget. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The reservation unit is implemented by the control unit 46A of the smart device 14 and makes reservations for accommodations and flights based on the generated travel plan. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests unexpectedly attractive places. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and suggestion unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of the user's travel preferences and budget. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and makes reservations for accommodations and flights based on the generated travel plan. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests unexpectedly attractive places. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and suggestion unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input of the user's travel preferences and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The reservation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and makes reservations for accommodations and flights based on the generated travel plan. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests unexpectedly attractive places. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input of the user's travel preferences and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal travel plan using generation AI. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 and makes reservations for accommodations and flights based on the generated travel plan. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests unexpectedly attractive places. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception desk that accepts users' travel preferences and budgets, A generation unit analyzes the information received by the reception unit and generates an optimal travel plan, A reservation unit that makes reservations for accommodations, flights, and tourist destinations based on the travel plan generated by the generation unit, The system includes a suggestion unit that proposes unexpected and attractive places based on the travel plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Based on users' travel history and reviews, we suggest unique and attractive spots. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the user's preferences and budget, we suggest unexpected and appealing locations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reservation section is, Supports booking accommodations, flights, and tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts how they input their travel preferences and budget based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter their travel preferences and budget, the system filters the results based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their travel preferences and budget, the system prioritizes input of highly relevant information, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter their travel preferences and budget, the system analyzes their social media activity and prompts them to enter relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating travel plans, the system references the user's past travel history and reviews to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating travel plans, the plan is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and prioritizes travel plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating travel plans, the system analyzes the user's social media activity and generates relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reservation section is, It estimates the user's emotions and adjusts the booking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reservation section is, When you make a reservation, we will refer to your past reservation history and suggest the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, When making a reservation, the reservation details are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, When making a reservation, we will suggest the optimal reservation method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's social media activity and suggests relevant reservation options. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts how attractive locations are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When suggesting attractive locations, the system refers to the user's past travel history and reviews to recommend the most suitable places. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When suggesting attractive locations, the suggestions are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When suggesting attractive locations, the system takes the user's geographical location into consideration to suggest the most suitable place. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When suggesting attractive locations, we analyze the user's social media activity and suggest relevant locations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts users' travel preferences and budgets, A generation unit analyzes the information received by the reception unit and generates an optimal travel plan, A reservation unit that makes reservations for accommodations, flights, and tourist destinations based on the travel plan generated by the generation unit, The system includes a suggestion unit that proposes unexpected and attractive places based on the travel plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is Based on users' travel history and reviews, we suggest unique and attractive spots. The system according to feature 1.

3. The aforementioned proposal section is, Based on the user's preferences and budget, we suggest unexpected and appealing locations. The system according to feature 1.

4. The aforementioned reservation section is, Supports booking accommodations, flights, and tourist attractions. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts how they input their travel preferences and budget based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When users enter their travel preferences and budget, the system filters the results based on their current living situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When users enter their travel preferences and budget, the system prioritizes input of highly relevant information, taking into account their geographical location. The system according to feature 1.

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