system

Abstract

The system according to this embodiment aims to enable users to find the optimal travel plan in a short amount of time and to make reservations and payments. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a proposal unit, and a settlement unit. The reception unit receives travel information from the user. The generation unit analyzes the travel information received by the reception unit and generates an optimal travel plan. The proposal unit proposes the travel plan generated by the generation unit to the user. The settlement unit makes reservations and payments based on the travel plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a user requires a lot of time and labor to find an optimal travel plan.

[0005] The system according to the embodiment aims to enable a user to find an optimal travel plan in a short time and make a reservation and settlement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a settlement unit. The reception unit receives travel information from the user. The generation unit analyzes the travel information received by the reception unit and generates an optimal travel plan. The proposal unit proposes the travel plan generated by the generation unit to the user. The settlement unit makes reservations and payments based on the travel plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to find the optimal travel plan in a short amount of time and to make reservations and payments. [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 applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel plan suggestion system according to an embodiment of the present invention is a system that enables the suggestion and booking of the optimal travel plan in as little as one minute, simply by inputting text and adjusting parameters. The travel plan suggestion system takes information such as the place the user wants to go, desired dates, budget, travel companions, and desired experiences as input. The generating AI analyzes this information and proposes three optimal travel plans. Each plan is assigned advantages and disadvantages. The user can select from the proposed plans and make a reservation and payment on the spot. This mechanism simplifies travel planning and booking, allowing users to obtain the optimal travel plan in a short amount of time. For example, the user inputs information such as the place the user wants to go, desired dates, budget, travel companions, and desired experiences. For example, the user inputs information such as "I want to go to Tokyo," "next weekend," "budget is 50,000 yen," "with family," and "I want to go to a hot spring." This information is input into the generating AI. Next, the generating AI analyzes the input information and proposes three optimal travel plans. The generating AI generates a plan that matches the user's wishes based on data such as accommodations, transportation, and tourist spots. For example, plans such as "Plan A: Stay at a luxury hotel and enjoy hot springs and sightseeing," "Plan B: Stay at a reasonably priced accommodation and enjoy hot springs and shopping," and "Plan C: Stay at a hot spring inn and spend a relaxing time" are suggested. Each plan is given advantages and disadvantages. For example, "Advantages of Plan A: Luxurious and comfortable stay. Disadvantages: May exceed budget," "Advantages of Plan B: Enjoyable within budget. Disadvantages: Accommodation quality may be low," and "Advantages of Plan C: Relaxing. Disadvantages: Few sightseeing spots." Users can select from the suggested plans and make reservations and payments on the spot. For example, if a user selects "Plan A," the generating AI will make reservations for accommodation and transportation, and make payments simultaneously. This system makes it easy for users to plan and book trips. This system is extremely convenient for users who find planning and booking trips troublesome. Users can get the optimal travel plan in a short time without having to go through complicated procedures.Furthermore, because the generating AI proposes plans tailored to the user's preferences, users can enjoy a trip that suits them. For example, it can cater to a variety of users, such as busy business people, the elderly, and families. Moreover, this system holds great potential in the travel agency market. The domestic travel agency market is estimated to be worth 500 billion yen annually, and introducing this system is expected to increase the sales and profits of travel agencies. Furthermore, by considering overseas expansion, the market size can be further expanded. Thus, this system, which enables the proposal and booking of the optimal travel plan in as little as one minute with just text input and parameter adjustment, simplifies travel planning and booking, making it extremely convenient for users. It also holds great potential in the travel agency market, and is expected to increase sales and profits. In this way, the travel plan proposal system can efficiently receive and analyze user travel information, propose the optimal travel plan, and handle booking and payment.

[0029] The travel plan proposal system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a payment unit. The reception unit receives travel information from the user. Travel information includes, but is not limited to, destination, itinerary, budget, and activities of interest. The reception unit analyzes, for example, the text information entered by the user and extracts the necessary information. The generation unit uses a generation AI to analyze the travel information received by the reception unit and generate an optimal travel plan. The generation AI uses, for example, technologies such as neural networks and genetic algorithms to generate a plan that matches the user's preferences. The generation unit uses the generation AI to generate three patterns of travel plans and assigns advantages and disadvantages to each plan. For example, the generation AI generates a plan that matches the user's preferences based on data such as accommodation, transportation, and tourist spots. The proposal unit proposes the travel plans generated by the generation unit to the user. The proposal unit makes reservations for accommodation and transportation based on the plan selected by the user from among the generated travel plans. The proposal unit displays the advantages and disadvantages of the generated travel plan. For example, the proposal unit displays the advantages and disadvantages of the plan selected by the user, making it easier for the user to compare plans. The payment unit makes reservations and payments based on the travel plans proposed by the proposal unit. The payment unit provides payment methods such as credit cards, debit cards, and electronic money. As a result, the travel plan proposal system according to the embodiment can efficiently receive and analyze the user's travel information, propose the optimal travel plan, and make reservations and payments.

[0030] The reception desk receives travel information from users. This travel information includes, but is not limited to, destination, itinerary, budget, and activities of interest. The reception desk analyzes the text information entered by the user and extracts the necessary information. Specifically, the information entered by the user is collected through web forms and mobile applications. Users can freely enter their destination, travel duration, budget, desired activities, etc. The reception desk analyzes this information using natural language processing (NLP) techniques and extracts the necessary information. For example, if a user enters "I want to stay in Paris for 5 days and visit museums," the reception desk will extract the keywords "Paris," "5 days," and "visit museums." Furthermore, the reception desk performs contextual and semantic analysis to accurately understand the user's input and grasp the user's intent. This allows the reception desk to collect detailed travel information based on the user's wishes and pass it on to the next step, the generation department. The reception desk can also employ interactive question formats to supplement user input errors or incomplete information. For example, if a user does not enter a budget, the reception desk will ask additional questions such as, "Please tell us your budget," to supplement the necessary information. This allows the reception desk to collect user travel information accurately and efficiently, improving the overall accuracy of the system and the user experience.

[0031] The generation unit uses a generation AI to analyze travel information received by the reception unit and generate the optimal travel plan. The generation AI uses technologies such as neural networks and genetic algorithms to generate a plan that matches the user's preferences. Specifically, the generation AI generates the optimal travel plan based on the user's input information, referencing past travel data and current trend information. For example, a neural network selects the optimal accommodation, transportation, and sightseeing spots based on the user's desired destination and activities. A genetic algorithm generates multiple plans, evaluates the advantages and disadvantages of each, and selects the optimal plan. The generation unit uses the generation AI to generate three travel plan patterns and assigns advantages and disadvantages to each plan. For example, the generation AI generates a plan that matches the user's preferences based on data such as accommodation, transportation, and sightseeing spots. Specifically, the generation AI selects the optimal accommodation and transportation based on the user's budget and schedule, and optimizes the order of visits to sightseeing spots and the duration of stay. This allows the generation unit to generate the optimal travel plan that matches the user's preferences and pass it on to the next step, the proposal unit. Furthermore, the generation unit can evaluate the generated plans and establish a feedback loop to select the most attractive plan for the user. This allows the generation unit to generate optimal travel plans that meet the user's preferences, improving the overall accuracy of the system and the user experience.

[0032] The suggestion unit proposes travel plans generated by the generation unit to the user. For example, the suggestion unit makes reservations for accommodations and transportation based on the plan selected by the user from the generated travel plans. The suggestion unit displays the advantages and disadvantages of the generated travel plans. Specifically, the suggestion unit displays the advantages and disadvantages of the plan selected by the user, making it easier for the user to compare plans. For example, the suggestion unit displays the ratings of accommodations, the convenience of transportation, and the popularity of tourist spots for each plan, making it easier for the user to compare plans. In addition, the suggestion unit can improve the accuracy of the generation AI by collecting user feedback and providing it to the generation unit. Furthermore, the suggestion unit makes reservations for accommodations and transportation based on the plan selected by the user. Specifically, the suggestion unit works in conjunction with accommodation and transportation reservation systems and makes reservations based on the plan selected by the user. This allows the suggestion unit to propose the optimal travel plan for the user and to facilitate the reservation process. Furthermore, the suggestion unit can improve the accuracy of the generation AI by collecting user feedback and providing it to the generation unit. This allows the suggestion unit to propose the optimal travel plan for the user and improve the overall accuracy of the system and the user experience.

[0033] The payment department handles bookings and payments based on travel plans proposed by the proposal department. The payment department provides payment methods such as credit cards, debit cards, and electronic money. Specifically, the payment department settles booking fees for accommodations and transportation based on the payment method selected by the user. For example, if a user selects a credit card, the payment department provides a secure form for entering credit card information and processes the payment. After the payment is complete, the payment department sends a confirmation email to the user, allowing them to verify the booking details. Furthermore, the payment department monitors the progress of the payment process in real time and can respond quickly if any problems occur. For example, if a payment fails, the payment department notifies the user and provides instructions for attempting the payment again. This allows the payment department to provide a safe and smooth payment process for users, improving the overall reliability of the system and the user experience. Additionally, the payment department can improve user convenience by offering multiple payment methods. For example, by offering various payment methods such as electronic money and bank transfers in addition to credit and debit cards, users can choose the payment method that suits them best. This allows the payment department to provide users with a secure and smooth payment process, improving the overall reliability of the system and the user experience.

[0034] The generation unit can generate three travel plans using a generation AI and assign advantages and disadvantages to each plan. For example, the generation unit uses the generation AI to generate a plan that suits the user's preferences based on data such as accommodation, transportation, and tourist spots. For example, the generation unit can generate Plan A, which involves staying at a luxury hotel and enjoying hot springs and sightseeing. It can also generate Plan B, which involves staying at a reasonably priced accommodation and enjoying hot springs and shopping. Furthermore, it can generate Plan C, which involves staying at a hot spring inn and spending a relaxing time. In this way, the generation AI generates multiple travel plans and assigns advantages and disadvantages to each plan, allowing the user to select the most suitable plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit inputs a prompt to the generation AI based on the user's preferences, and the generation AI generates a travel plan. For example, the generation AI receives a prompt such as "Please generate three travel plans based on the user's preferences" and generates the travel plans. The generation AI assigns advantages and disadvantages to the generated plan and proposes it to the user.

[0035] The suggestion unit can make reservations for accommodations and transportation based on a plan selected by the user from the generated travel plans. For example, the suggestion unit can make reservations for accommodations based on the plan selected by the user. For example, the suggestion unit can make reservations for luxury hotels. It can also make reservations for reasonably priced accommodations. Furthermore, the suggestion unit can also make reservations for hot spring inns. For example, the suggestion unit can make reservations for transportation based on a plan selected by the user. For example, the suggestion unit can make reservations for flights. It can also make reservations for trains. Furthermore, the suggestion unit can also make reservations for buses. This simplifies travel planning by allowing users to make reservations for accommodations and transportation based on their selected travel plans. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the suggestion unit inputs a prompt to the generating AI based on the plan selected by the user, and the generating AI makes reservations for accommodations and transportation. For example, the generating AI receives a prompt such as "Please make reservations for accommodations and transportation based on the plan selected by the user" and makes the reservations.

[0036] The payment unit can process payments based on the travel plan selected by the user. The payment unit can process payments using, for example, a credit card. For example, the payment unit enters credit card information based on the plan selected by the user and completes the payment. The payment unit can also process payments using a debit card. For example, the payment unit enters debit card information based on the plan selected by the user and completes the payment. Furthermore, the payment unit can also process payments using electronic money. For example, the payment unit enters electronic money information based on the plan selected by the user and completes the payment. This integrates travel booking and payment by processing payments based on the travel plan selected by the user. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI based on the plan selected by the user, and the generative AI may process the payment. For example, the generative AI may receive a prompt such as "Please process the payment based on the plan selected by the user" and process the payment.

[0037] The suggestion section can display the advantages and disadvantages of the travel plan selected by the user. For example, the suggestion section displays the advantages and disadvantages of the plan selected by the user. For example, the suggestion section displays that Plan A has a luxurious feel and a comfortable stay as an advantage, and that it may exceed the budget as a disadvantage. For example, the suggestion section displays that Plan B can be enjoyed within the budget as an advantage, and that the accommodation may be of low quality as a disadvantage. For example, the suggestion section displays that Plan C can be relaxing as an advantage, and that there are few tourist attractions as a disadvantage. By displaying the advantages and disadvantages of the travel plan selected by the user, it makes it easier for the user to compare plans. Some or all of the above processing in the suggestion section may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion section inputs a prompt to the generative AI based on the plan selected by the user, and the generative AI displays the advantages and disadvantages. For example, the generative AI receives a prompt such as "Please display the advantages and disadvantages of the plan selected by the user" and displays the advantages and disadvantages.

[0038] The generation unit can analyze data such as accommodations, transportation, and tourist spots in order to generate a plan tailored to the user's preferences. For example, the generation unit can analyze data on accommodations and select accommodations that match the user's preferences. For example, the generation unit can analyze data on luxury hotels, budget accommodations, and hot spring inns. The generation unit can also analyze data on transportation and select transportations that match the user's preferences. For example, the generation unit can analyze data on airplanes, trains, and buses. Furthermore, the generation unit can analyze data on tourist spots and select tourist spots that match the user's preferences. For example, the generation unit can analyze data on tourist attractions, shopping spots, and places to relax. By analyzing data in this way, the generation unit can provide a more appropriate travel plan tailored to the user's preferences. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input prompts to the generation AI based on the user's preferences, and the generation AI will analyze the data. The generating AI, for example, receives a prompt such as, "Analyze data on accommodations, transportation, and tourist attractions based on the user's preferences," and then analyzes the data.

[0039] The reception desk can analyze the user's past travel history and select the optimal input method. For example, the reception desk can automatically display as suggestions destinations and dates that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. In this way, the reception desk can provide the optimal input method by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input a prompt to the generative AI to analyze the user's past travel history, and the generative AI can analyze the history. For example, the generative AI can receive a prompt such as "Analyze the user's past travel history and select the optimal input method," analyze the history, and select an input method.

[0040] The reception unit can filter travel information based on the user's current lifestyle and areas of interest when receiving it. For example, the reception unit can suggest relevant travel destinations based on keywords the user has recently searched for or their browsing history. For example, the reception unit can suggest travel destinations that are appropriate for the user's current lifestyle (e.g., newly married, raising children). For example, the reception unit can filter and suggest appropriate travel destinations based on the user's areas of interest (e.g., outdoor activities, cultural experiences). This allows for the provision of more appropriate travel information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input a prompt to the generative AI to filter the user's lifestyle and areas of interest, and the generative AI can perform the filtering. For example, the generative AI can receive a prompt such as "Please filter travel information based on the user's lifestyle and areas of interest" and perform the filtering.

[0041] The reception desk can prioritize receiving highly relevant information when receiving travel information, taking into account the user's geographical location. For example, the reception desk may suggest travel destinations close to the user's current location. For example, the reception desk may suggest tourist spots near places the user has visited in the past. For example, the reception desk may suggest travel destinations that are appropriate for the weather and season at the user's current location. In this way, by taking into account the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk may input a prompt to the generative AI to take into account the user's geographical location, and the generative AI will analyze the geographical location. For example, the generative AI may receive a prompt such as, "Please prioritize receiving highly relevant information, taking into account the user's geographical location," and analyze the geographical location.

[0042] The reception desk can analyze the user's social media activity and accept relevant information when receiving travel information. For example, the reception desk can suggest travel destinations that the user has liked or shared on social media. For example, the reception desk can suggest relevant travel destinations based on places visited by the user's followers or friends. For example, the reception desk can analyze photos and comments posted by the user on social media and suggest travel destinations of interest. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input a prompt to the generative AI to analyze the user's social media activity, and the generative AI can analyze the activity. For example, the generative AI receives a prompt such as "Analyze the user's social media activity and accept relevant information" and analyzes the activity.

[0043] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit generates the optimal plan based on the accommodations and transportation methods the user has used in the past. For example, the generation unit generates a plan that avoids crowds based on the user's past travel history. For example, the generation unit analyzes the user's past travel history and generates the most efficient plan. In this way, the optimal travel plan can be generated by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs a prompt to the generation AI to refer to the user's past travel history, and the generation AI refers to the history. For example, the generation AI receives a prompt such as "Refer to the user's past travel history and generate the optimal plan," refers to the history, and generates a plan.

[0044] The generation unit can customize travel plans based on the user's current living situation when generating them. For example, if the user is newly married, the generation unit will generate a romantic travel plan. For example, if the user is raising children, the generation unit will generate a plan that includes activities for children. For example, if the user is elderly, the generation unit will generate a plan that is easy on the body. By customizing the plan based on the user's current living situation, a more appropriate travel plan can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit may input a prompt to the generation AI to customize the plan based on the user's current living situation, and the generation AI will customize the plan. For example, the generation AI may receive a prompt such as "Please customize the travel plan based on the user's current living situation" and customize the plan.

[0045] The generation unit can generate an optimal travel plan by considering the user's geographical location information. For example, the generation unit may prioritize suggesting travel destinations close to the user's current location. For example, the generation unit may suggest tourist spots near places the user has visited in the past. For example, the generation unit may suggest travel destinations that are appropriate for the weather and season at the user's current location. In this way, an optimal travel plan can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit may input a prompt to the generation AI in order to consider the user's geographical location information, and the generation AI may analyze the geographical location information. For example, the generation AI may receive a prompt such as "Please generate an optimal plan considering the user's geographical location information," analyze the geographical location information, and generate a plan.

[0046] The generation unit can generate travel plans by analyzing the user's social media activity. For example, the generation unit can suggest travel destinations that the user has "liked" or shared on social media. For example, the generation unit can suggest relevant travel destinations based on places visited by the user's followers and friends. For example, the generation unit can analyze photos and comments posted by the user on social media and suggest travel destinations of interest. In this way, by analyzing the user's social media activity, the system can provide the user with the most suitable travel plan. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a prompt to the generation AI to analyze the user's social media activity, and the generation AI can analyze the activity. For example, the generation AI can receive a prompt such as "Analyze the user's social media activity and generate the best plan," analyze the activity, and generate a plan.

[0047] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the travel plan. For example, the suggestion unit provides detailed information for highly important plans. For example, it provides concise information for less important plans. The suggestion unit also adjusts the order of suggestions according to their importance. This allows the user to receive the most relevant information by adjusting the level of detail in suggestions based on the importance of the travel plan. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input a prompt to the generative AI to adjust the level of detail in suggestions based on the importance of the travel plan, and the generative AI can adjust the level of detail. For example, the generative AI receives a prompt such as "Please adjust the level of detail in suggestions based on the importance of the travel plan" and adjusts the level of detail.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, for sightseeing plans, the suggestion unit will make suggestions that emphasize information on tourist spots. For relaxation plans, the suggestion unit will make suggestions that emphasize information on accommodations and hot springs. For activity plans, the suggestion unit will make suggestions that emphasize detailed information on activities. By applying different suggestion algorithms depending on the category of the travel plan, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input a prompt to the generative AI to apply a suggestion algorithm according to the category of the travel plan, and the generative AI can apply the algorithm. For example, the generative AI receives a prompt such as "Apply the suggestion algorithm according to the category of the travel plan" and applies the algorithm.

[0049] The proposal unit can determine the priority of proposals based on the submission timing of travel plans. For example, the proposal unit will prioritize proposals with upcoming submission dates. For example, the proposal unit will provide detailed information to proposals with later submission dates. For example, the proposal unit will adjust the order of proposals according to the submission dates. This allows the proposal unit to provide the user with the best possible proposal by determining the priority of proposals based on the submission timing of travel plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit may input a prompt to the generative AI to determine the priority of proposals based on the submission timing of travel plans, and the generative AI will determine the priority. For example, the generative AI receives a prompt such as "Please determine the priority of proposals based on the submission timing of travel plans" and determines the priority.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans when making suggestions. For example, the suggestion unit will prioritize suggesting highly relevant plans. For example, the suggestion unit will provide concise information for less relevant plans. The suggestion unit will adjust the order of suggestions according to their relevance. This allows the user to receive the best possible suggestions by adjusting the order of suggestions based on the relevance of the travel plans. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit may input a prompt to the generative AI to adjust the order of suggestions based on the relevance of the travel plans, and the generative AI will adjust the order. For example, the generative AI receives a prompt such as "Please adjust the order of suggestions based on the relevance of the travel plans" and adjusts the order.

[0051] The payment unit can select the optimal payment method by referring to the user's past payment history at the time of payment. For example, the payment unit may prioritize suggesting payment methods that the user has used in the past. For example, the payment unit may suggest the most frequently used payment method from the user's past payment history. For example, the payment unit may analyze the user's past payment history and suggest the optimal payment method according to a specific time of day or situation. In this way, the optimal payment method can be provided by referring to the user's past payment history. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI to refer to the user's past payment history, and the generative AI will refer to the history. For example, the generative AI may receive a prompt such as "Refer to the user's past payment history and select the optimal payment method," refer to the history, and select a payment method.

[0052] The payment unit can customize payment methods based on the user's current living situation at the time of payment. For example, if the user is newly married, the payment unit may suggest a payment method that offers special discounts or benefits. For example, if the user is raising children, the payment unit may suggest a payment method that offers family-friendly discounts or benefits. For example, if the user is elderly, the payment unit may suggest a simple and easy-to-use payment method. By customizing payment methods based on the user's current living situation, the payment unit can provide the user with the most suitable payment method. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI to customize the payment method based on the user's current living situation, and the generative AI will customize the method. For example, the generative AI may receive a prompt such as "Please customize the payment method based on the user's current living situation" and customize the method.

[0053] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location information. For example, the payment unit may suggest a payment method available at a location close to the user's current location. For example, the payment unit may suggest a payment method available at a store near a place the user has visited in the past. For example, the payment unit may suggest a payment method that takes into account the weather and season at the user's current location. In this way, the optimal payment method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI in order to take into account the user's geographical location information, and the generative AI will analyze the geographical location information. For example, the generative AI may receive a prompt such as "Please select the optimal payment method considering the user's geographical location information," analyze the geographical location information, and select a payment method.

[0054] The payment unit can analyze a user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can suggest payment methods for stores or services that the user has "liked" or shared on social media. For example, the payment unit can suggest relevant payment methods based on payment methods used by the user's followers or friends. For example, the payment unit can analyze photos and comments posted by the user on social media and suggest payment methods of interest. In this way, by analyzing the user's social media activity, the payment unit can provide the optimal payment method for the user. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit inputs a prompt to the generative AI to analyze the user's social media activity, and the generative AI analyzes the activity. For example, the generative AI receives a prompt such as "Analyze the user's social media activity and suggest the optimal payment method," analyzes the activity, and suggests a payment method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can generate the optimal plan by referring to the user's past travel history. For example, it can generate the optimal plan based on the accommodations and transportation methods the user has used in the past. It can also generate a plan that avoids crowds based on the user's past travel history. Furthermore, it can analyze the user's past travel history and generate the most efficient plan. In this way, the optimal travel plan can be generated by referring to the user's past travel history.

[0057] The generation unit can customize plans based on the user's current lifestyle. For example, if the user is newly married, it can generate a romantic travel plan. If the user is raising children, it can generate a plan that includes activities for children. Furthermore, if the user is elderly, it can generate a plan that is less strenuous on the body. This allows for the provision of more appropriate travel plans by customizing them based on the user's current lifestyle.

[0058] The proposal function can adjust the level of detail in a proposal based on the importance of the travel plan. For example, it can provide detailed information for highly important plans, and concise information for less important plans. Furthermore, it can adjust the order of proposals according to their importance. This allows the system to provide users with the most relevant information by adjusting the level of detail in proposals based on the importance of the travel plan.

[0059] The reception desk can analyze a user's past travel history and select the most suitable input method. For example, it can automatically display destinations and dates that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest destinations related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, it can provide the most suitable input method.

[0060] The suggestion function can apply different suggestion algorithms depending on the travel plan category. For example, sightseeing plans can be suggested with an emphasis on information about tourist spots. Relaxation plans can be suggested with an emphasis on information about accommodations and hot springs. Furthermore, activity plans can be suggested with an emphasis on detailed activity information. By applying different suggestion algorithms according to the travel plan category, the system can provide users with the most suitable suggestions.

[0061] The payment processing unit can select the optimal payment method by referring to the user's past payment history during the payment process. For example, it can prioritize suggesting payment methods the user has used in the past. It can also suggest the most frequently used payment method based on the user's past payment history. Furthermore, it can analyze the user's past payment history and suggest the optimal payment method for specific times of day or situations. This allows the system to provide the most suitable payment method by referring to the user's past payment history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives travel information from users. This travel information includes destination, dates, budget, and activities of interest. The reception desk analyzes the text information entered by the user and extracts the necessary information. Step 2: The generation unit uses a generation AI to analyze the travel information received by the reception unit and generate the optimal travel plan. The generation AI uses technologies such as neural networks and genetic algorithms to generate a plan that matches the user's preferences. The generation unit generates three travel plan patterns and assigns advantages and disadvantages to each plan. Step 3: The suggestion unit proposes the travel plan generated by the generation unit to the user. The suggestion unit makes reservations for accommodation and transportation based on the plan selected by the user from the generated travel plans. The suggestion unit displays the advantages and disadvantages of the generated travel plan to make it easier for the user to compare the plans. Step 4: The payment department makes reservations and payments based on the travel plans proposed by the proposal department. The payment department provides payment methods such as credit cards, debit cards, and electronic money.

[0064] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system that enables the suggestion and booking of the optimal travel plan in as little as one minute, simply by inputting text and adjusting parameters. The travel plan suggestion system takes information such as the place the user wants to go, desired dates, budget, travel companions, and desired experiences as input. The generating AI analyzes this information and proposes three optimal travel plans. Each plan is assigned advantages and disadvantages. The user can select from the proposed plans and make a reservation and payment on the spot. This mechanism simplifies travel planning and booking, allowing users to obtain the optimal travel plan in a short amount of time. For example, the user inputs information such as the place the user wants to go, desired dates, budget, travel companions, and desired experiences. For example, the user inputs information such as "I want to go to Tokyo," "next weekend," "budget is 50,000 yen," "with family," and "I want to go to a hot spring." This information is input into the generating AI. Next, the generating AI analyzes the input information and proposes three optimal travel plans. The generating AI generates a plan that matches the user's wishes based on data such as accommodations, transportation, and tourist spots. For example, plans such as "Plan A: Stay at a luxury hotel and enjoy hot springs and sightseeing," "Plan B: Stay at a reasonably priced accommodation and enjoy hot springs and shopping," and "Plan C: Stay at a hot spring inn and spend a relaxing time" are suggested. Each plan is given advantages and disadvantages. For example, "Advantages of Plan A: Luxurious and comfortable stay. Disadvantages: May exceed budget," "Advantages of Plan B: Enjoyable within budget. Disadvantages: Accommodation quality may be low," and "Advantages of Plan C: Relaxing. Disadvantages: Few sightseeing spots." Users can select from the suggested plans and make reservations and payments on the spot. For example, if a user selects "Plan A," the generating AI will make reservations for accommodation and transportation, and make payments simultaneously. This system makes it easy for users to plan and book trips. This system is extremely convenient for users who find planning and booking trips troublesome. Users can get the optimal travel plan in a short time without having to go through complicated procedures.Furthermore, because the generating AI proposes plans tailored to the user's preferences, users can enjoy a trip that suits them. For example, it can cater to a variety of users, such as busy business people, the elderly, and families. Moreover, this system holds great potential in the travel agency market. The domestic travel agency market is estimated to be worth 500 billion yen annually, and introducing this system is expected to increase the sales and profits of travel agencies. Furthermore, by considering overseas expansion, the market size can be further expanded. Thus, this system, which enables the proposal and booking of the optimal travel plan in as little as one minute with just text input and parameter adjustment, simplifies travel planning and booking, making it extremely convenient for users. It also holds great potential in the travel agency market, and is expected to increase sales and profits. In this way, the travel plan proposal system can efficiently receive and analyze user travel information, propose the optimal travel plan, and handle booking and payment.

[0065] The travel plan proposal system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a payment unit. The reception unit receives travel information from the user. Travel information includes, but is not limited to, destination, itinerary, budget, and activities of interest. The reception unit analyzes, for example, the text information entered by the user and extracts the necessary information. The generation unit uses a generation AI to analyze the travel information received by the reception unit and generate an optimal travel plan. The generation AI uses, for example, technologies such as neural networks and genetic algorithms to generate a plan that matches the user's preferences. The generation unit uses the generation AI to generate three patterns of travel plans and assigns advantages and disadvantages to each plan. For example, the generation AI generates a plan that matches the user's preferences based on data such as accommodation, transportation, and tourist spots. The proposal unit proposes the travel plans generated by the generation unit to the user. The proposal unit makes reservations for accommodation and transportation based on the plan selected by the user from among the generated travel plans. The proposal unit displays the advantages and disadvantages of the generated travel plan. For example, the proposal unit displays the advantages and disadvantages of the plan selected by the user, making it easier for the user to compare plans. The payment unit makes reservations and payments based on the travel plans proposed by the proposal unit. The payment unit provides payment methods such as credit cards, debit cards, and electronic money. As a result, the travel plan proposal system according to the embodiment can efficiently receive and analyze the user's travel information, propose the optimal travel plan, and make reservations and payments.

[0066] The reception desk receives travel information from users. This travel information includes, but is not limited to, destination, itinerary, budget, and activities of interest. The reception desk analyzes the text information entered by the user and extracts the necessary information. Specifically, the information entered by the user is collected through web forms and mobile applications. Users can freely enter their destination, travel duration, budget, desired activities, etc. The reception desk analyzes this information using natural language processing (NLP) techniques and extracts the necessary information. For example, if a user enters "I want to stay in Paris for 5 days and visit museums," the reception desk will extract the keywords "Paris," "5 days," and "visit museums." Furthermore, the reception desk performs contextual and semantic analysis to accurately understand the user's input and grasp the user's intent. This allows the reception desk to collect detailed travel information based on the user's wishes and pass it on to the next step, the generation department. The reception desk can also employ interactive question formats to supplement user input errors or incomplete information. For example, if a user does not enter a budget, the reception desk will ask additional questions such as, "Please tell us your budget," to supplement the necessary information. This allows the reception desk to collect user travel information accurately and efficiently, improving the overall accuracy of the system and the user experience.

[0067] The generation unit uses a generation AI to analyze travel information received by the reception unit and generate the optimal travel plan. The generation AI uses technologies such as neural networks and genetic algorithms to generate a plan that matches the user's preferences. Specifically, the generation AI generates the optimal travel plan based on the user's input information, referencing past travel data and current trend information. For example, a neural network selects the optimal accommodation, transportation, and sightseeing spots based on the user's desired destination and activities. A genetic algorithm generates multiple plans, evaluates the advantages and disadvantages of each, and selects the optimal plan. The generation unit uses the generation AI to generate three travel plan patterns and assigns advantages and disadvantages to each plan. For example, the generation AI generates a plan that matches the user's preferences based on data such as accommodation, transportation, and sightseeing spots. Specifically, the generation AI selects the optimal accommodation and transportation based on the user's budget and schedule, and optimizes the order of visits to sightseeing spots and the duration of stay. This allows the generation unit to generate the optimal travel plan that matches the user's preferences and pass it on to the next step, the proposal unit. Furthermore, the generation unit can evaluate the generated plans and establish a feedback loop to select the most attractive plan for the user. This allows the generation unit to generate optimal travel plans that meet the user's preferences, improving the overall accuracy of the system and the user experience.

[0068] The suggestion unit proposes travel plans generated by the generation unit to the user. For example, the suggestion unit makes reservations for accommodations and transportation based on the plan selected by the user from the generated travel plans. The suggestion unit displays the advantages and disadvantages of the generated travel plans. Specifically, the suggestion unit displays the advantages and disadvantages of the plan selected by the user, making it easier for the user to compare plans. For example, the suggestion unit displays the ratings of accommodations, the convenience of transportation, and the popularity of tourist spots for each plan, making it easier for the user to compare plans. In addition, the suggestion unit can improve the accuracy of the generation AI by collecting user feedback and providing it to the generation unit. Furthermore, the suggestion unit makes reservations for accommodations and transportation based on the plan selected by the user. Specifically, the suggestion unit works in conjunction with accommodation and transportation reservation systems and makes reservations based on the plan selected by the user. This allows the suggestion unit to propose the optimal travel plan for the user and to facilitate the reservation process. Furthermore, the suggestion unit can improve the accuracy of the generation AI by collecting user feedback and providing it to the generation unit. This allows the suggestion unit to propose the optimal travel plan for the user and improve the overall accuracy of the system and the user experience.

[0069] The payment department handles bookings and payments based on travel plans proposed by the proposal department. The payment department provides payment methods such as credit cards, debit cards, and electronic money. Specifically, the payment department settles booking fees for accommodations and transportation based on the payment method selected by the user. For example, if a user selects a credit card, the payment department provides a secure form for entering credit card information and processes the payment. After the payment is complete, the payment department sends a confirmation email to the user, allowing them to verify the booking details. Furthermore, the payment department monitors the progress of the payment process in real time and can respond quickly if any problems occur. For example, if a payment fails, the payment department notifies the user and provides instructions for attempting the payment again. This allows the payment department to provide a safe and smooth payment process for users, improving the overall reliability of the system and the user experience. Additionally, the payment department can improve user convenience by offering multiple payment methods. For example, by offering various payment methods such as electronic money and bank transfers in addition to credit and debit cards, users can choose the payment method that suits them best. This allows the payment department to provide users with a secure and smooth payment process, improving the overall reliability of the system and the user experience.

[0070] The generation unit can generate three travel plans using a generation AI and assign advantages and disadvantages to each plan. For example, the generation unit uses the generation AI to generate a plan that suits the user's preferences based on data such as accommodation, transportation, and tourist spots. For example, the generation unit can generate Plan A, which involves staying at a luxury hotel and enjoying hot springs and sightseeing. It can also generate Plan B, which involves staying at a reasonably priced accommodation and enjoying hot springs and shopping. Furthermore, it can generate Plan C, which involves staying at a hot spring inn and spending a relaxing time. In this way, the generation AI generates multiple travel plans and assigns advantages and disadvantages to each plan, allowing the user to select the most suitable plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit inputs a prompt to the generation AI based on the user's preferences, and the generation AI generates a travel plan. For example, the generation AI receives a prompt such as "Please generate three travel plans based on the user's preferences" and generates the travel plans. The generation AI assigns advantages and disadvantages to the generated plan and proposes it to the user.

[0071] The suggestion unit can make reservations for accommodations and transportation based on a plan selected by the user from the generated travel plans. For example, the suggestion unit can make reservations for accommodations based on the plan selected by the user. For example, the suggestion unit can make reservations for luxury hotels. It can also make reservations for reasonably priced accommodations. Furthermore, the suggestion unit can also make reservations for hot spring inns. For example, the suggestion unit can make reservations for transportation based on a plan selected by the user. For example, the suggestion unit can make reservations for flights. It can also make reservations for trains. Furthermore, the suggestion unit can also make reservations for buses. This simplifies travel planning by allowing users to make reservations for accommodations and transportation based on their selected travel plans. Some or all of the above processing in the suggestion unit may be performed using, for example, a generating AI, or without a generating AI. For example, the suggestion unit inputs a prompt to the generating AI based on the plan selected by the user, and the generating AI makes reservations for accommodations and transportation. For example, the generating AI receives a prompt such as "Please make reservations for accommodations and transportation based on the plan selected by the user" and makes the reservations.

[0072] The payment unit can process payments based on the travel plan selected by the user. The payment unit can process payments using, for example, a credit card. For example, the payment unit enters credit card information based on the plan selected by the user and completes the payment. The payment unit can also process payments using a debit card. For example, the payment unit enters debit card information based on the plan selected by the user and completes the payment. Furthermore, the payment unit can also process payments using electronic money. For example, the payment unit enters electronic money information based on the plan selected by the user and completes the payment. This integrates travel booking and payment by processing payments based on the travel plan selected by the user. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI based on the plan selected by the user, and the generative AI may process the payment. For example, the generative AI may receive a prompt such as "Please process the payment based on the plan selected by the user" and process the payment.

[0073] The suggestion section can display the advantages and disadvantages of the travel plan selected by the user. For example, the suggestion section displays the advantages and disadvantages of the plan selected by the user. For example, the suggestion section displays that Plan A has a luxurious feel and a comfortable stay as an advantage, and that it may exceed the budget as a disadvantage. For example, the suggestion section displays that Plan B can be enjoyed within the budget as an advantage, and that the accommodation may be of low quality as a disadvantage. For example, the suggestion section displays that Plan C can be relaxing as an advantage, and that there are few tourist attractions as a disadvantage. By displaying the advantages and disadvantages of the travel plan selected by the user, it makes it easier for the user to compare plans. Some or all of the above processing in the suggestion section may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion section inputs a prompt to the generative AI based on the plan selected by the user, and the generative AI displays the advantages and disadvantages. For example, the generative AI receives a prompt such as "Please display the advantages and disadvantages of the plan selected by the user" and displays the advantages and disadvantages.

[0074] The generation unit can analyze data such as accommodations, transportation, and tourist spots in order to generate a plan tailored to the user's preferences. For example, the generation unit can analyze data on accommodations and select accommodations that match the user's preferences. For example, the generation unit can analyze data on luxury hotels, budget accommodations, and hot spring inns. The generation unit can also analyze data on transportation and select transportations that match the user's preferences. For example, the generation unit can analyze data on airplanes, trains, and buses. Furthermore, the generation unit can analyze data on tourist spots and select tourist spots that match the user's preferences. For example, the generation unit can analyze data on tourist attractions, shopping spots, and places to relax. By analyzing data in this way, the generation unit can provide a more appropriate travel plan tailored to the user's preferences. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input prompts to the generation AI based on the user's preferences, and the generation AI will analyze the data. The generating AI, for example, receives a prompt such as, "Analyze data on accommodations, transportation, and tourist attractions based on the user's preferences," and then analyzes the data.

[0075] The reception desk can estimate the user's emotions and adjust the travel information input interface based on the estimated emotions. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk may provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk may prioritize voice input to allow for quick input of travel information. This allows users to input travel information without stress by adjusting the input interface 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 reception desk may be performed using or without a generative AI. For example, the reception desk may input a prompt to the generative AI to estimate the user's emotions, and the generative AI will estimate the emotions. The generative AI, for example, receives a prompt such as, "Estimate the user's emotions and adjust the interface based on those emotions," and then estimates the emotions and adjusts the interface accordingly.

[0076] The reception desk can analyze the user's past travel history and select the optimal input method. For example, the reception desk can automatically display as suggestions destinations and dates that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. In this way, the reception desk can provide the optimal input method by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input a prompt to the generative AI to analyze the user's past travel history, and the generative AI can analyze the history. For example, the generative AI can receive a prompt such as "Analyze the user's past travel history and select the optimal input method," analyze the history, and select an input method.

[0077] The reception unit can filter travel information based on the user's current lifestyle and areas of interest when receiving it. For example, the reception unit can suggest relevant travel destinations based on keywords the user has recently searched for or their browsing history. For example, the reception unit can suggest travel destinations that are appropriate for the user's current lifestyle (e.g., newly married, raising children). For example, the reception unit can filter and suggest appropriate travel destinations based on the user's areas of interest (e.g., outdoor activities, cultural experiences). This allows for the provision of more appropriate travel information by filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input a prompt to the generative AI to filter the user's lifestyle and areas of interest, and the generative AI can perform the filtering. For example, the generative AI can receive a prompt such as "Please filter travel information based on the user's lifestyle and areas of interest" and perform the filtering.

[0078] The reception desk can estimate the user's emotions and prioritize the travel information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize the input of the most important information (e.g., places to visit, budget, etc.). If the user is relaxed, the reception desk will prioritize the input of detailed information (e.g., experiences to have, information about travel companions, etc.). If the user is in a hurry, the reception desk will prioritize the input of only the minimum information (e.g., places to visit, desired dates, etc.). This allows the user to efficiently input information by prioritizing the information to be entered 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 a generative AI, or not. For example, the reception desk may input a prompt to the generative AI to estimate the user's emotions, and the generative AI will estimate the emotions. The generative AI, for example, receives a prompt such as, "Estimate the user's emotions and determine the priority of the information to be entered based on those emotions," and then estimates the emotions and determines the priority.

[0079] The reception desk can prioritize receiving highly relevant information when receiving travel information, taking into account the user's geographical location. For example, the reception desk may suggest travel destinations close to the user's current location. For example, the reception desk may suggest tourist spots near places the user has visited in the past. For example, the reception desk may suggest travel destinations that are appropriate for the weather and season at the user's current location. In this way, by taking into account the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk may input a prompt to the generative AI to take into account the user's geographical location, and the generative AI will analyze the geographical location. For example, the generative AI may receive a prompt such as, "Please prioritize receiving highly relevant information, taking into account the user's geographical location," and analyze the geographical location.

[0080] The reception desk can analyze the user's social media activity and accept relevant information when receiving travel information. For example, the reception desk can suggest travel destinations that the user has liked or shared on social media. For example, the reception desk can suggest relevant travel destinations based on places visited by the user's followers or friends. For example, the reception desk can analyze photos and comments posted by the user on social media and suggest travel destinations of interest. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input a prompt to the generative AI to analyze the user's social media activity, and the generative AI can analyze the activity. For example, the generative AI receives a prompt such as "Analyze the user's social media activity and accept relevant information" and analyzes the activity.

[0081] The generation unit can estimate the user's emotions and adjust how the generated travel plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit will generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit will generate a travel plan with visually stimulating effects. By adjusting how the travel plan is presented according to the user's emotions, the system can provide the user with the most suitable plan. 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-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit may input a prompt to the generation AI to estimate the user's emotions, and the generation AI will estimate the emotions. The generative AI, for example, receives a prompt such as, "Estimate the user's emotions and adjust the way the travel plan is presented based on those emotions," and then estimates the emotions and adjusts the presentation accordingly.

[0082] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit generates the optimal plan based on the accommodations and transportation methods the user has used in the past. For example, the generation unit generates a plan that avoids crowds based on the user's past travel history. For example, the generation unit analyzes the user's past travel history and generates the most efficient plan. In this way, the optimal travel plan can be generated by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs a prompt to the generation AI to refer to the user's past travel history, and the generation AI refers to the history. For example, the generation AI receives a prompt such as "Refer to the user's past travel history and generate the optimal plan," refers to the history, and generates a plan.

[0083] The generation unit can customize travel plans based on the user's current living situation when generating them. For example, if the user is newly married, the generation unit will generate a romantic travel plan. For example, if the user is raising children, the generation unit will generate a plan that includes activities for children. For example, if the user is elderly, the generation unit will generate a plan that is easy on the body. By customizing the plan based on the user's current living situation, a more appropriate travel plan can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit may input a prompt to the generation AI to customize the plan based on the user's current living situation, and the generation AI will customize the plan. For example, the generation AI may receive a prompt such as "Please customize the travel plan based on the user's current living situation" and customize the plan.

[0084] The generation unit can estimate the user's emotions and determine the priority of the travel plans to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating relaxing plans. For example, if the user is excited, the generation unit will prioritize generating active plans. For example, if the user is relaxed, the generation unit will prioritize generating leisurely plans. In this way, by prioritizing travel plans according to the user's emotions, the system can provide the user with the most suitable plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit may input a prompt to the generation AI to estimate the user's emotions, and the generation AI will estimate the emotions. The generative AI, for example, receives a prompt such as, "Estimate the user's emotions and determine the priority of travel plans based on those emotions," and then estimates the emotions and determines the priorities.

[0085] The generation unit can generate an optimal travel plan by considering the user's geographical location information. For example, the generation unit may prioritize suggesting travel destinations close to the user's current location. For example, the generation unit may suggest tourist spots near places the user has visited in the past. For example, the generation unit may suggest travel destinations that are appropriate for the weather and season at the user's current location. In this way, an optimal travel plan can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit may input a prompt to the generation AI in order to consider the user's geographical location information, and the generation AI may analyze the geographical location information. For example, the generation AI may receive a prompt such as "Please generate an optimal plan considering the user's geographical location information," analyze the geographical location information, and generate a plan.

[0086] The generation unit can generate travel plans by analyzing the user's social media activity. For example, the generation unit can suggest travel destinations that the user has "liked" or shared on social media. For example, the generation unit can suggest relevant travel destinations based on places visited by the user's followers and friends. For example, the generation unit can analyze photos and comments posted by the user on social media and suggest travel destinations of interest. In this way, by analyzing the user's social media activity, the system can provide the user with the most suitable travel plan. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a prompt to the generation AI to analyze the user's social media activity, and the generation AI can analyze the activity. For example, the generation AI can receive a prompt such as "Analyze the user's social media activity and generate the best plan," analyze the activity, and generate a plan.

[0087] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions at a relaxed pace. If the user is in a hurry, the suggestion unit will present suggestions that emphasize the shortest route. If the user is excited, the suggestion unit will present suggestions with visually stimulating effects. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide the most suitable suggestions for the user. 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 processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit may input a prompt to the generative AI to estimate the user's emotions, and the generative AI will estimate the emotions. The generative AI may receive a prompt such as, "Estimate the user's emotions and adjust the way suggestions are presented based on those emotions," estimate the emotions, and adjust the presentation.

[0088] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the travel plan. For example, the suggestion unit provides detailed information for highly important plans. For example, it provides concise information for less important plans. The suggestion unit also adjusts the order of suggestions according to their importance. This allows the user to receive the most relevant information by adjusting the level of detail in suggestions based on the importance of the travel plan. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input a prompt to the generative AI to adjust the level of detail in suggestions based on the importance of the travel plan, and the generative AI can adjust the level of detail. For example, the generative AI receives a prompt such as "Please adjust the level of detail in suggestions based on the importance of the travel plan" and adjusts the level of detail.

[0089] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, for sightseeing plans, the suggestion unit will make suggestions that emphasize information on tourist spots. For relaxation plans, the suggestion unit will make suggestions that emphasize information on accommodations and hot springs. For activity plans, the suggestion unit will make suggestions that emphasize detailed information on activities. By applying different suggestion algorithms depending on the category of the travel plan, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input a prompt to the generative AI to apply a suggestion algorithm according to the category of the travel plan, and the generative AI can apply the algorithm. For example, the generative AI receives a prompt such as "Apply the suggestion algorithm according to the category of the travel plan" and applies the algorithm.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit will provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion according to the user's emotions, the suggestion unit can provide the most suitable suggestion for the user. 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 suggestion unit may be performed using a generative AI or not. For example, the suggestion unit inputs a prompt to the generative AI to estimate the user's emotions, and the generative AI estimates the emotions. The generative AI receives a prompt, for example, "Estimate the user's emotions and adjust the length of the suggestion based on the emotions," estimates the emotions, and adjusts the length.

[0091] The proposal unit can determine the priority of proposals based on the submission timing of travel plans. For example, the proposal unit will prioritize proposals with upcoming submission dates. For example, the proposal unit will provide detailed information to proposals with later submission dates. For example, the proposal unit will adjust the order of proposals according to the submission dates. This allows the proposal unit to provide the user with the best possible proposal by determining the priority of proposals based on the submission timing of travel plans. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit may input a prompt to the generative AI to determine the priority of proposals based on the submission timing of travel plans, and the generative AI will determine the priority. For example, the generative AI receives a prompt such as "Please determine the priority of proposals based on the submission timing of travel plans" and determines the priority.

[0092] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans when making suggestions. For example, the suggestion unit will prioritize suggesting highly relevant plans. For example, the suggestion unit will provide concise information for less relevant plans. The suggestion unit will adjust the order of suggestions according to their relevance. This allows the user to receive the best possible suggestions by adjusting the order of suggestions based on the relevance of the travel plans. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit may input a prompt to the generative AI to adjust the order of suggestions based on the relevance of the travel plans, and the generative AI will adjust the order. For example, the generative AI receives a prompt such as "Please adjust the order of suggestions based on the relevance of the travel plans" and adjusts the order.

[0093] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is stressed, the payment unit can provide a simple and quick payment method. For example, if the user is relaxed, the payment unit can provide detailed payment options. For example, if the user is in a hurry, the payment unit can provide a method that allows the user to complete the payment with one click. This allows the payment unit to provide the optimal payment method for the user by adjusting the payment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the payment unit may be performed using a generative AI or not. For example, the payment unit inputs a prompt to the generative AI to estimate the user's emotions, and the generative AI estimates the emotions. The generative AI receives a prompt, for example, "Estimate the user's emotions and adjust the payment method based on the emotions," estimates the emotions, and adjusts the method.

[0094] The payment unit can select the optimal payment method by referring to the user's past payment history at the time of payment. For example, the payment unit may prioritize suggesting payment methods that the user has used in the past. For example, the payment unit may suggest the most frequently used payment method from the user's past payment history. For example, the payment unit may analyze the user's past payment history and suggest the optimal payment method according to a specific time of day or situation. In this way, the optimal payment method can be provided by referring to the user's past payment history. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI to refer to the user's past payment history, and the generative AI will refer to the history. For example, the generative AI may receive a prompt such as "Refer to the user's past payment history and select the optimal payment method," refer to the history, and select a payment method.

[0095] The payment unit can customize payment methods based on the user's current living situation at the time of payment. For example, if the user is newly married, the payment unit may suggest a payment method that offers special discounts or benefits. For example, if the user is raising children, the payment unit may suggest a payment method that offers family-friendly discounts or benefits. For example, if the user is elderly, the payment unit may suggest a simple and easy-to-use payment method. By customizing payment methods based on the user's current living situation, the payment unit can provide the user with the most suitable payment method. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI to customize the payment method based on the user's current living situation, and the generative AI will customize the method. For example, the generative AI may receive a prompt such as "Please customize the payment method based on the user's current living situation" and customize the method.

[0096] The payment unit can estimate the user's emotions and determine payment priorities based on those emotions. For example, if the user is stressed, the payment unit will prioritize processing the most important payment items. If the user is relaxed, the payment unit will provide detailed payment options. If the user is in a hurry, the payment unit will provide a way to complete the payment quickly. This allows the payment unit to provide the user with the optimal payment method by determining payment priorities 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 payment unit may be performed using a generative AI or not. For example, the payment unit may input a prompt to the generative AI to estimate the user's emotions, and the generative AI will estimate the emotions. The generative AI may receive a prompt such as, "Estimate the user's emotions and determine payment priorities based on those emotions," estimate the emotions, and determine the priorities.

[0097] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location information. For example, the payment unit may suggest a payment method available at a location close to the user's current location. For example, the payment unit may suggest a payment method available at a store near a place the user has visited in the past. For example, the payment unit may suggest a payment method that takes into account the weather and season at the user's current location. In this way, the optimal payment method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit may input a prompt to the generative AI in order to take into account the user's geographical location information, and the generative AI will analyze the geographical location information. For example, the generative AI may receive a prompt such as "Please select the optimal payment method considering the user's geographical location information," analyze the geographical location information, and select a payment method.

[0098] The payment unit can analyze a user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can suggest payment methods for stores or services that the user has "liked" or shared on social media. For example, the payment unit can suggest relevant payment methods based on payment methods used by the user's followers or friends. For example, the payment unit can analyze photos and comments posted by the user on social media and suggest payment methods of interest. In this way, by analyzing the user's social media activity, the payment unit can provide the optimal payment method for the user. Some or all of the above processing in the payment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the payment unit inputs a prompt to the generative AI to analyze the user's social media activity, and the generative AI analyzes the activity. For example, the generative AI receives a prompt such as "Analyze the user's social media activity and suggest the optimal payment method," analyzes the activity, and suggests a payment method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can estimate the user's emotions and adjust the travel information input interface based on those emotions. 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. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick travel information entry. In this way, by adjusting the input interface according to the user's emotions, users can enter travel information without stress.

[0101] The generation unit can generate the optimal plan by referring to the user's past travel history. For example, it can generate the optimal plan based on the accommodations and transportation methods the user has used in the past. It can also generate a plan that avoids crowds based on the user's past travel history. Furthermore, it can analyze the user's past travel history and generate the most efficient plan. In this way, the optimal travel plan can be generated by referring to the user's past travel history.

[0102] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can present suggestions at a leisurely pace. If the user is in a hurry, it can present suggestions that emphasize the shortest route. Furthermore, if the user is excited, it can present suggestions with visually stimulating effects. By adjusting the presentation of suggestions according to the user's emotions, it becomes possible to provide the most suitable suggestions for the user.

[0103] The payment system can estimate the user's emotions and adjust the payment method based on those emotions. For example, if the user is stressed, it can offer a simple and quick payment method. If the user is relaxed, it can offer more detailed payment options. Furthermore, if the user is in a hurry, it can offer a one-click payment method. This allows the system to provide the user with the most suitable payment method by adjusting the payment method according to their emotions.

[0104] The generation unit can customize plans based on the user's current lifestyle. For example, if the user is newly married, it can generate a romantic travel plan. If the user is raising children, it can generate a plan that includes activities for children. Furthermore, if the user is elderly, it can generate a plan that is less strenuous on the body. This allows for the provision of more appropriate travel plans by customizing them based on the user's current lifestyle.

[0105] The proposal function can adjust the level of detail in a proposal based on the importance of the travel plan. For example, it can provide detailed information for highly important plans, and concise information for less important plans. Furthermore, it can adjust the order of proposals according to their importance. This allows the system to provide users with the most relevant information by adjusting the level of detail in proposals based on the importance of the travel plan.

[0106] The reception desk can analyze a user's past travel history and select the most suitable input method. For example, it can automatically display destinations and dates that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest destinations related to specific seasons or events based on the user's past travel history. In this way, by analyzing the user's past travel history, it can provide the most suitable input method.

[0107] The generation unit can estimate the user's emotions and determine the priority of the travel plans to generate based on those emotions. For example, if the user is feeling stressed, it can prioritize generating relaxing plans. If the user is excited, it can prioritize generating active plans. Furthermore, if the user is relaxed, it can prioritize generating leisurely plans. By prioritizing travel plans according to the user's emotions, it becomes possible to provide the user with the most suitable plan.

[0108] The suggestion function can apply different suggestion algorithms depending on the travel plan category. For example, sightseeing plans can be suggested with an emphasis on information about tourist spots. Relaxation plans can be suggested with an emphasis on information about accommodations and hot springs. Furthermore, activity plans can be suggested with an emphasis on detailed activity information. By applying different suggestion algorithms according to the travel plan category, the system can provide users with the most suitable suggestions.

[0109] The payment processing unit can select the optimal payment method by referring to the user's past payment history during the payment process. For example, it can prioritize suggesting payment methods the user has used in the past. It can also suggest the most frequently used payment method based on the user's past payment history. Furthermore, it can analyze the user's past payment history and suggest the optimal payment method for specific times of day or situations. This allows the system to provide the most suitable payment method by referring to the user's past payment history.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives travel information from users. This travel information includes destination, dates, budget, and activities of interest. The reception desk analyzes the text information entered by the user and extracts the necessary information. Step 2: The generation unit uses a generation AI to analyze the travel information received by the reception unit and generate the optimal travel plan. The generation AI uses technologies such as neural networks and genetic algorithms to generate a plan that matches the user's preferences. The generation unit generates three travel plan patterns and assigns advantages and disadvantages to each plan. Step 3: The suggestion unit proposes the travel plan generated by the generation unit to the user. The suggestion unit makes reservations for accommodation and transportation based on the plan selected by the user from the generated travel plans. The suggestion unit displays the advantages and disadvantages of the generated travel plan to make it easier for the user to compare the plans. Step 4: The payment department makes reservations and payments based on the travel plans proposed by the proposal department. The payment department provides payment methods such as credit cards, debit cards, and electronic money.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and payment 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, which analyzes the text information entered by the user and extracts the necessary information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan using generation AI. The proposal unit is implemented by the output device 40 of the smart device 14, which proposes the generated travel plan to the user. The payment unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides payment methods such as credit cards and electronic money. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and payment unit, is implemented in 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, which analyzes the voice information input by the user and extracts the necessary information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan using generation AI. The proposal unit is implemented by the speaker 240 of the smart glasses 214, which proposes the generated travel plan to the user. The payment unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides payment methods such as credit cards and electronic money. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and payment 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, which analyzes the voice information input by the user and extracts the necessary information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan using generation AI. The proposal unit is implemented by, for example, the display 343 of the headset terminal 314, which proposes the generated travel plan to the user. The payment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides payment methods such as credit cards and electronic money. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and payment 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, which analyzes the voice information input by the user and extracts the necessary information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan using generation AI. The proposal unit is implemented by, for example, the speaker 240 of the robot 414, which proposes the generated travel plan to the user. The payment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides payment methods such as credit cards and electronic money. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that receives travel information from users, A generation unit analyzes the travel information received by the reception unit and generates an optimal travel plan, A proposal unit that proposes a travel plan generated by the generation unit to the user, The system includes a settlement unit that makes reservations and payments based on the travel plan proposed by the proposal unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates three different travel plans, and each plan is assigned its own advantages and disadvantages. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the travel plan selected by the user from the generated travel plans, reservations for accommodation and transportation are made. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned settlement unit, Payment is processed based on the travel plan selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Display the advantages and disadvantages of the travel plan selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is To generate plans tailored to the user's preferences, data such as accommodation, transportation, and tourist attractions are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the travel information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past travel history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving travel information, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of travel information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving travel information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving travel information, the system analyzes the user's social media activity and accepts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust how the travel plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a travel plan, the system references the user's past travel history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a travel plan, customize the plan based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of travel plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The generating unit is When generating travel plans, the system analyzes the user's social media activity to create the plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the travel plan is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned settlement unit, It estimates the user's emotions and adjusts the payment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned settlement unit, During payment, the system selects the most suitable payment method by referring to the user's past payment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned settlement unit, At the time of payment, the payment method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned settlement unit, It estimates the user's emotions and determines payment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned settlement unit, During payment, the system selects the most suitable payment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned settlement unit, At the time of payment, the system analyzes the user's social media activity and suggests payment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 receives travel information from users, A generation unit analyzes the travel information received by the reception unit and generates an optimal travel plan, A proposal unit that proposes a travel plan generated by the generation unit to the user, The system includes a settlement unit that makes reservations and payments based on the travel plan proposed by the proposal unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates three different travel plans, and each plan is assigned its own advantages and disadvantages. The system according to feature 1.

3. The aforementioned proposal section is, Based on the travel plan selected by the user from the generated travel plans, reservations for accommodation and transportation are made. The system according to feature 1.

4. The aforementioned settlement unit, Payment is processed based on the travel plan selected by the user. The system according to feature 1.

5. The aforementioned proposal section is, Display the advantages and disadvantages of the travel plan selected by the user. The system according to feature 1.

6. The generating unit is To generate plans tailored to the user's preferences, data such as accommodation, transportation, and tourist attractions are analyzed. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the travel information input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past travel history and select the optimal input method. The system according to feature 1.

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