System

Abstract

An object of a system according to an embodiment is to generate an optimal itinerary based on various orders from a user.SOLUTION: A system includes an order input unit, an information collection unit, an analysis unit, and a plan generation unit. The order input unit inputs an order of a user. The information collection unit collects various types of information based on the order input by the order input unit. The analysis unit analyzes the information collected by the information collection unit. The plan generation unit generates an optimal itinerary plan based on a result of the analysis by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to generate an optimal itinerary plan based on a variety of user orders.

[0005] The system according to the embodiment aims to generate an optimal itinerary plan based on various orders from users. [Means for solving the problem]

[0006] The system according to the embodiment includes an order input unit, an information collection unit, an analysis unit, and a plan generation unit. The order input unit inputs a user's order. The information collection unit collects various information based on the order input by the order input unit. The analysis unit analyzes the information collected by the information collection unit. The plan generation unit generates an optimal itinerary plan based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an optimal itinerary plan based on various orders from a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The travel plan coordination system according to the embodiment of the present invention is a system in which a generation AI coordinates an optimal itinerary plan according to the various orders of each traveler. As a result, the travel plan coordination system allows travelers to significantly reduce their time and enjoy original routes and plans that they would not have thought of on their own.

[0029] A travel plan coordination system according to an embodiment includes an order input unit, an information collection unit, an analysis unit, and a plan generation unit. The order input unit inputs a user's order. For example, the user inputs their preferences and requirements while conversing with the generation AI through an app. The information collection unit collects various information based on the order input by the order input unit. For example, the generation AI collects information about flights, hotels, and tourist destinations based on the user's order. The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes information such as low-cost flights, routes where award tickets can be used, and hotels where points can be used for accommodation. The plan generation unit generates an optimal itinerary plan based on the results of the analysis by the analysis unit. For example, the generation AI generates an optimal itinerary plan that meets the user's preferences based on the collected and analyzed information. This allows an optimal itinerary plan to be generated based on the user's order.

[0030] The order input unit can learn the user's past travel history or preferences and provide an auto-completion function when entering an order. The order input unit, for example, learns the user's past travel history and provides an auto-completion function when entering an order. For example, it automatically suggests a plan that the user prefers based on places visited in the past and services used. The order input unit also learns the user's preferences and provides an auto-completion function when entering an order. For example, it automatically suggests an optimal plan based on the user's preferred meals and activities. The order input unit also learns the user's past feedback and provides an auto-completion function when entering an order. For example, it automatically suggests a plan that the user will be satisfied with based on plans that have received high ratings in the past. This makes it possible to automatically complete order input based on the user's past travel history and preferences.

[0031] The order input unit can accept orders entered by users in a variety of ways, such as voice input or gesture input. The order input unit, for example, allows users to enter orders using voice input. For example, by simply speaking their wishes and requirements through a microphone, the generation AI analyzes them and proposes a plan. The order input unit also allows users to enter orders using gesture input. For example, it recognizes gestures through a smartphone camera and proposes a plan based on them. The order input unit also allows users to enter orders using methods other than text input. For example, it provides an interface for selecting wishes and requirements using a touchscreen. This allows users to enter orders in a variety of ways.

[0032] The order input unit can add a function for group travel that allows multiple users to enter orders simultaneously. The order input unit provides, for example, a function for group travel that allows multiple users to enter orders simultaneously. For example, each user enters their own preferences and conditions, and the orders are integrated to generate an optimal plan. The order input unit also provides a function for group travel that allows each user's preferences and conditions to be shared in real time. For example, the order input unit displays the input contents of each user and generates a plan that reflects everyone's preferences. The order input unit also provides an interface that allows multiple users to enter orders simultaneously. For example, each user enters orders using a smartphone or tablet, and the orders are integrated to generate a plan. This allows multiple users to enter orders simultaneously.

[0033] The information collection unit uses the generation AI to collect the latest travel information in real time and instantly reflect it in the user's order. For example, the generation AI collects the latest flight and hotel information in real time and instantly reflects it in the user's order. For example, the optimal plan is proposed based on the latest prices and seat availability information. The information collection unit also uses the generation AI to collect information on tourist spots in real time and instantly reflect it in the user's order. For example, the optimal plan is proposed based on the latest event information and weather information. The information collection unit also uses the generation AI to collect the latest travel information in real time and instantly reflect it in the user's order. For example, the optimal plan is proposed based on the latest traffic information and restaurant information. This allows the latest travel information to be collected in real time and instantly reflected in the user's order.

[0034] The information collection unit can integrate data from different sources based on a user's order and select the most reliable information. For example, the generation AI collects data from different sources and selects the most reliable information based on a user's order. For example, the information collection unit integrates data from multiple airlines and selects the optimal flight. The generation AI also collects data from different sources and selects the most reliable information based on a user's order. For example, the information collection unit integrates data from multiple hotel reservation sites and selects the optimal hotel. The generation AI also collects data from different sources and selects the most reliable information based on a user's order. For example, the information collection unit integrates data from multiple tourist information sites and selects the optimal tourist destination. This allows data from different sources to be integrated and the most reliable information to be selected.

[0035] The information collection unit can utilize information from the user's SNS account when collecting travel information to make more personalized suggestions. The information collection unit, for example, collects information from the user's SNS account and utilizes it to propose travel plans. For example, the unit proposes optimal plans based on travel destinations and interests shared by the user on SNS. The information collection unit also analyzes information from the user's SNS account to make more personalized suggestions. For example, the unit proposes plans that match the user's interests based on the accounts the user follows and the content of their posts. The information collection unit also collects information from the user's SNS account and utilizes it to propose travel plans. For example, the unit proposes optimal plans based on posts and comments that the user has "liked" on SNS. This allows the information from the user's SNS account to be utilized to make more personalized suggestions.

[0036] The information collection unit collects information from different languages ​​and cultural spheres and can propose travel plans from an international perspective. For example, the generation AI collects information from different languages ​​and cultural spheres and proposes travel plans from an international perspective. For example, it proposes plans based on tourist information and event information provided in the local language. The information collection unit also collects information from different cultural spheres and proposes travel plans from an international perspective based on the user's order. For example, it proposes tourist spots and activities based on local culture and customs. The information collection unit also collects information from different languages ​​and cultural spheres and proposes travel plans from an international perspective. For example, it proposes plans based on local food culture and traditional events. This allows the generation AI to collect information from different languages ​​and cultural spheres and propose travel plans from an international perspective.

[0037] The plan generation unit can generate multiple scenarios based on a user's order and present the advantages and disadvantages of each scenario. For example, the plan generation unit uses a generation AI to generate multiple scenarios based on a user's order and present the advantages and disadvantages of each scenario. For example, it compares direct flights with connecting flights and shows the differences in cost and time. The plan generation unit also uses a generation AI to generate multiple scenarios and present the advantages and disadvantages of each scenario. For example, it presents different hotel options and explains the differences in price, location, and services. The plan generation unit also uses a generation AI to generate multiple scenarios based on a user's order and present the advantages and disadvantages of each scenario. For example, it presents options for tourist destinations and compares them in terms of accessibility and the appeal of tourist spots. This allows multiple scenarios to be generated and the advantages and disadvantages of each to be presented.

[0038] The plan generation unit can learn from the user's past feedback and generate a more accurate itinerary plan. In the plan generation unit, for example, the generation AI learns from the user's past feedback and generates a more accurate itinerary plan. For example, it proposes a plan that suits the user's preferences based on plans that have received high ratings in the past. In addition, the plan generation unit analyzes the user's past feedback and the generation AI generates a more accurate itinerary plan. For example, it proposes a plan that improves on aspects that the user was dissatisfied with in past trips. In addition, the plan generation unit learns from the user's past feedback and generates a more accurate itinerary plan. For example, it proposes a plan that incorporates elements that the user was particularly satisfied with in past trips. In this way, it is possible to learn from the user's past feedback and generate a more accurate itinerary plan.

[0039] The plan generation unit can generate itinerary plans according to different seasons and events, and provide the user with options. For example, the generation AI in the plan generation unit generates itinerary plans according to different seasons and events, and provides the user with options. For example, seasonal plans such as cherry blossom viewing in the spring and beach resorts in the summer are proposed. The plan generation unit also generates itinerary plans according to different events, and provides the user with options. For example, plans tailored to specific events such as music festivals and sporting events are proposed. The generation AI in the plan generation unit also generates itinerary plans according to different seasons and events, and provides the user with options. For example, plans including seasonal events such as Christmas markets and New Year's countdown events are proposed. In this way, itinerary plans according to different seasons and events can be generated, and the user can be provided with options.

[0040] The plan generation unit can generate an optimal itinerary plan for group travel that integrates the orders of multiple users. In the plan generation unit, for example, a generation AI integrates the orders of multiple users to generate an optimal itinerary plan for group travel. For example, it proposes a plan that reflects the preferences of each user. In addition, the plan generation unit generates an optimal itinerary plan for group travel that integrates the orders of multiple users. For example, it proposes a plan that includes tourist spots and activities that will satisfy everyone. In addition, the plan generation unit generates an optimal itinerary plan for group travel that integrates the orders of multiple users. For example, it proposes a plan that reflects the preferences of each user in a balanced manner. In this way, an optimal itinerary plan can be generated that integrates the orders of multiple users.

[0041] The plan generation unit can suggest hidden spots only locals know, instead of regular tourist routes, based on the user's order. For example, the generation AI of the plan generation unit suggests hidden spots only locals know, based on the user's order. For example, it introduces famous local shops and scenery that are not listed in tourist guides. The plan generation unit also suggests hidden spots, instead of regular tourist routes, based on the user's order. For example, it introduces markets and cafes where locals gather. The plan generation unit also suggests hidden spots only locals know, based on the user's order. For example, it introduces quiet beaches and parks with few tourists. This makes it possible to suggest hidden spots only locals know, instead of regular tourist routes.

[0042] The plan generation unit can generate an itinerary plan along a specific theme based on the user's interests and hobbies. In the plan generation unit, for example, a generation AI generates an itinerary plan along a specific theme based on the user's interests and hobbies. For example, a plan touring historical sites is proposed to a user who loves history. The plan generation unit also generates an itinerary plan along a specific theme based on the user's hobbies. For example, a plan to enjoy local gourmet food is proposed to a user who loves food. In addition, the plan generation unit generates an itinerary plan along a specific theme based on the user's interests and hobbies using a generation AI. For example, a plan including hiking and camping is proposed to a user who loves the outdoors. In this way, an itinerary plan along a specific theme can be generated based on the user's interests and hobbies.

[0043] The plan generation unit can propose unique routes that combine different means of transportation, providing the user with a new experience. For example, the generation AI of the plan generation unit proposes unique routes that combine different means of transportation, providing the user with a new experience. For example, it proposes a plan that combines airplanes and ferries. The plan generation unit can also propose routes that combine unique means of transportation, providing the user with a new experience. For example, it proposes a plan that combines trains and bicycles. The plan generation unit can also propose unique routes that combine different means of transportation, providing the user with a new experience. For example, it proposes a plan that combines buses and walking. This allows the generation AI to propose unique routes that combine different means of transportation, providing the user with a new experience.

[0044] The plan generation unit can generate a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. The plan generation unit, for example, uses a generation AI to generate a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. For example, it proposes a plan to visit multiple countries. The plan generation unit also generates a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. For example, it proposes a plan to visit tourist spots in different cultural spheres. The plan generation unit also generates a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. For example, it proposes a plan that includes historical sites and cultural events. This makes it possible to generate a multinational itinerary plan that allows users to experience different cultures and histories.

[0045] The plan generation unit provides the user with a detailed explanation of each element of the itinerary plan, allowing the user to deepen their understanding. In the plan generation unit, for example, the generation AI provides the user with a detailed explanation of each element of the itinerary plan. For example, it provides detailed explanations of the history, highlights, and access methods of each tourist destination. The plan generation unit also provides the user with a detailed explanation of each element of the itinerary plan, allowing the user to deepen their understanding. For example, it provides detailed explanations of hotel facilities and services, surrounding tourist attractions, and the like. In the plan generation unit, the generation AI also provides the user with a detailed explanation of each element of the itinerary plan. For example, it provides detailed explanations of flight flights, connecting flights, and airport procedures. This allows the user to deepen their understanding by providing a detailed explanation of each element of the itinerary plan.

[0046] The plan generation unit can reflect user feedback in real time and instantly present a revised plan. In the plan generation unit, for example, the generation AI reflects user feedback in real time and instantly presents a revised plan. For example, the plan generation unit instantly generates a plan that adds tourist attractions desired by the user. The plan generation unit also reflects user feedback in real time and presents a revised plan. For example, the plan generation unit instantly generates a plan that reflects changes to the hotel desired by the user. The plan generation unit also reflects user feedback in real time and instantly presents a revised plan. For example, the plan generation unit instantly generates a plan that reflects changes to the means of transportation desired by the user. This allows the generation AI to reflect user feedback in real time and instantly present a revised plan.

[0047] The plan generation unit can present multiple revision plans to the user and allow the user to select from them. For example, the generation AI of the plan generation unit presents multiple revision plans to the user and allows the user to select from them. For example, it presents options for different tourist spots and hotels. The plan generation unit can also present multiple revision plans to the user and allow the user to select from them. For example, it presents options for different means of transportation and routes. The plan generation unit can also present multiple revision plans to the user and allow the user to select from them. For example, it presents options for different activities and events. This allows the generation AI of the plan generation unit to present multiple revision plans to the user and allow the user to select from them.

[0048] The plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, the plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, it may propose optimal tourist spots and hotels based on the user's preferences. Also, the plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, it may propose optimal means of transportation and routes based on the user's preferences. Also, the plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, it may propose optimal activities and events based on the user's preferences. This allows the generation AI to automatically propose optimal revisions based on user feedback.

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

[0050] The order input unit can also monitor the user's health condition and suggest appropriate travel plans. For example, it can measure the user's heart rate and blood pressure and suggest plans based on their health condition. The order input unit can also create a reasonable schedule based on the user's health data. For example, for a user who is not confident in their physical strength, it can suggest a plan that includes more rest. The order input unit can also monitor the user's health condition in real time and adjust the plan as needed. For example, if the user's health deteriorates, the plan can be changed to allow for more rest time.

[0051] The order input unit can suggest meal plans for the user's trip based on the user's past travel history. For example, it can suggest meal plans that the user prefers based on restaurants visited and dishes eaten in the past. The order input unit can also learn the user's food preferences and suggest recommended restaurants and dishes at the travel destination. For example, it can suggest vegetarian restaurants to a vegetarian user. The order input unit can also optimize the meal plan based on the user's past feedback. For example, it can re-suggest restaurants that have received high ratings in the past. This makes it possible to suggest optimal meal plans based on the user's past travel history and preferences.

[0052] The order input unit can accept orders entered by the user through a visual interface. For example, the user can select desired tourist attractions or activities using images or icons. The order input unit can also provide an interface that allows the user to specify a desired route or destination on a map. For example, the user can select a tourist attraction by tapping on the map. The order input unit can also provide an interface that allows the user to combine desired elements by dragging and dropping. For example, the user can add a tourist attraction or activity to the itinerary by dragging it. This allows the user to enter an order visually.

[0053] The order input unit not only allows multiple users to enter orders simultaneously, but also allows each user to set their own roles. For example, a trip leader can manage the overall schedule, while other members enter their own preferences. The order input unit can also provide a function to support coordination of opinions within a group. For example, a voting function can be used to decide which tourist spots to visit. The order input unit can also share the input contents of each user in real time, allowing everyone to create a plan based on the same information. For example, it can display the preferences of each member and generate a plan that reflects everyone's opinions. This allows multiple users to enter orders efficiently.

[0054] The information collection unit can use the generation AI to collect customized travel information based on the user's interests. For example, if the user is interested in history, information on historical sites and museums can be collected preferentially. The information collection unit can also collect travel information on specific themes based on the user's interests. For example, if a user is interested in gourmet food, information on local gourmet food and restaurants can be collected. The information collection unit can also provide customized travel information in real time based on the user's interests. For example, if the user is interested in the outdoors, information on hiking trails and campsites can be collected. This makes it possible to provide customized travel information based on the user's interests.

[0055] The information collection unit can not only integrate data from different sources, but also use an algorithm to evaluate the reliability of the data. For example, it can evaluate the accuracy of past data from each source and prioritize the use of highly reliable sources. The information collection unit can also continuously evaluate the reliability of sources based on user feedback. For example, if a user is satisfied with the information provided, it can determine that the source is highly reliable. The information collection unit can also make the process of integrating data from different sources and selecting highly reliable information transparent. For example, it can display to the user which sources the data was collected from. This makes it possible to integrate data from different sources and provide highly reliable information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The order input unit inputs the user's order. For example, the user can enter their wishes and requirements while talking to the generating AI through the app. Step 2: The information collection unit collects various information based on the order entered by the order input unit. For example, the generation AI collects flight and hotel information, tourist destination data, etc. based on the user's order. Step 3: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes information such as low-cost flights, routes available for award tickets, and hotels where points can be used to stay. Step 4: The plan generation unit generates an optimal itinerary plan based on the results of the analysis by the analysis unit. For example, the generation AI generates an optimal itinerary plan that meets the user's wishes based on the collected and analyzed information.

[0058] (Example 2) The travel plan coordination system according to the embodiment of the present invention is a system in which a generation AI coordinates an optimal itinerary plan according to the various orders of each traveler. As a result, the travel plan coordination system allows travelers to significantly reduce their time and enjoy original routes and plans that they would not have thought of on their own.

[0059] A travel plan coordination system according to an embodiment includes an order input unit, an information collection unit, an analysis unit, and a plan generation unit. The order input unit inputs a user's order. For example, the user inputs their preferences and requirements while conversing with the generation AI through an app. The information collection unit collects various information based on the order input by the order input unit. For example, the generation AI collects information about flights, hotels, and tourist destinations based on the user's order. The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes information such as low-cost flights, routes where award tickets can be used, and hotels where points can be used for accommodation. The plan generation unit generates an optimal itinerary plan based on the results of the analysis by the analysis unit. For example, the generation AI generates an optimal itinerary plan that meets the user's preferences based on the collected and analyzed information. This allows an optimal itinerary plan to be generated based on the user's order.

[0060] The order input unit can analyze the tone or speed of the user's voice, infer their emotional state, and optimize the order contents. For example, when the user inputs voice, the generation AI analyzes the tone and speed of the voice to infer the user's emotional state. For example, if the user is excited, the generation AI suggests an active plan, and if the user is relaxed, the generation AI suggests a relaxing plan. The order input unit also analyzes the tone and speed of the user's voice, and if the user is feeling stressed or anxious, the generation AI makes suggestions to alleviate that stress. For example, if the user is feeling stressed, the generation AI suggests tourist spots and activities that will help them relax. The order input unit also analyzes the tone and speed of the user's voice to optimize the order contents according to their emotional state. For example, if the user is in a hurry, the generation AI suggests a plan that allows for quick travel, and if the user wants to enjoy themselves at a leisurely pace, the generation AI suggests a plan with more time. This makes it possible to optimize the order contents based on the user's emotional state.

[0061] The order input unit can learn the user's past travel history or preferences and provide an auto-completion function when entering an order. The order input unit, for example, learns the user's past travel history and provides an auto-completion function when entering an order. For example, it automatically suggests a plan that the user prefers based on places visited in the past and services used. The order input unit also learns the user's preferences and provides an auto-completion function when entering an order. For example, it automatically suggests an optimal plan based on the user's preferred meals and activities. The order input unit also learns the user's past feedback and provides an auto-completion function when entering an order. For example, it automatically suggests a plan that the user will be satisfied with based on plans that have received high ratings in the past. This makes it possible to automatically complete order input based on the user's past travel history and preferences.

[0062] The order input unit can use the emotion estimation function to make suggestions to reduce the stress or anxiety the user feels while entering an order. For example, the order input unit uses the emotion estimation function to detect the stress or anxiety the user feels while entering an order and makes suggestions to reduce it. For example, it displays relaxing music or scenery. The order input unit also uses the emotion estimation function to make suggestions to reduce the stress or anxiety the user feels while entering an order. For example, if the order is difficult to enter, it changes the question format to a simpler one. The order input unit also monitors the user's emotional state in real time, and if the user feels stress or anxiety, the generation AI makes suggestions to reduce it. For example, it suggests relaxing tourist spots or activities. This helps reduce the stress and anxiety the user feels while entering an order.

[0063] The order input unit can accept orders entered by users in a variety of ways, such as voice input or gesture input. The order input unit, for example, allows users to enter orders using voice input. For example, by simply speaking their wishes and requirements through a microphone, the generation AI analyzes them and proposes a plan. The order input unit also allows users to enter orders using gesture input. For example, it recognizes gestures through a smartphone camera and proposes a plan based on them. The order input unit also allows users to enter orders using methods other than text input. For example, it provides an interface for selecting wishes and requirements using a touchscreen. This allows users to enter orders in a variety of ways.

[0064] The order input unit can add a function for group travel that allows multiple users to enter orders simultaneously. The order input unit provides, for example, a function for group travel that allows multiple users to enter orders simultaneously. For example, each user enters their own preferences and conditions, and the orders are integrated to generate an optimal plan. The order input unit also provides a function for group travel that allows each user's preferences and conditions to be shared in real time. For example, the order input unit displays the input contents of each user and generates a plan that reflects everyone's preferences. The order input unit also provides an interface that allows multiple users to enter orders simultaneously. For example, each user enters orders using a smartphone or tablet, and the orders are integrated to generate a plan. This allows multiple users to enter orders simultaneously.

[0065] The order input unit uses the emotion estimation function to provide real-time feedback on the order entered by the user, thereby eliciting positive emotions. The order input unit, for example, uses the emotion estimation function to provide real-time feedback on the order entered by the user. For example, it displays an encouraging message to elicit positive emotions. The order input unit also monitors the user's emotional state in real time and provides feedback according to the input content. For example, it displays words of praise if the input is progressing smoothly. The order input unit also uses the emotion estimation function to provide real-time feedback on the order entered by the user, thereby eliciting positive emotions. For example, it displays appropriate advice according to the input content. This makes it possible to elicit positive emotions about the order entered by the user.

[0066] The information collection unit uses the generation AI to collect the latest travel information in real time and instantly reflect it in the user's order. For example, the generation AI collects the latest flight and hotel information in real time and instantly reflects it in the user's order. For example, the optimal plan is proposed based on the latest prices and seat availability information. The information collection unit also uses the generation AI to collect information on tourist spots in real time and instantly reflect it in the user's order. For example, the optimal plan is proposed based on the latest event information and weather information. The information collection unit also uses the generation AI to collect the latest travel information in real time and instantly reflect it in the user's order. For example, the optimal plan is proposed based on the latest traffic information and restaurant information. This allows the latest travel information to be collected in real time and instantly reflected in the user's order.

[0067] The information collection unit can integrate data from different sources based on a user's order and select the most reliable information. For example, the generation AI collects data from different sources and selects the most reliable information based on a user's order. For example, the information collection unit integrates data from multiple airlines and selects the optimal flight. The generation AI also collects data from different sources and selects the most reliable information based on a user's order. For example, the information collection unit integrates data from multiple hotel reservation sites and selects the optimal hotel. The generation AI also collects data from different sources and selects the most reliable information based on a user's order. For example, the information collection unit integrates data from multiple tourist information sites and selects the optimal tourist destination. This allows data from different sources to be integrated and the most reliable information to be selected.

[0068] The information collection unit can use the emotion estimation function to evaluate how well the collected information meets the user's expectations and re-collect information as necessary. The information collection unit, for example, uses the emotion estimation function to evaluate how well the collected information meets the user's expectations. For example, the information collection unit evaluates the quality of the information based on the user's emotional reaction and re-collects information as necessary. The information collection unit also monitors the user's emotional reaction to the collected information in real time and re-collects information if the information does not meet expectations. For example, if the user is dissatisfied, the information collection unit collects data from another source. The information collection unit also uses the emotion estimation function to evaluate how well the collected information meets the user's expectations and re-collects information as necessary. For example, if the user's emotion score is low, the information is re-collected to improve the plan. This makes it possible to evaluate whether the collected information meets the user's expectations and re-collect information as necessary.

[0069] The information collection unit can utilize information from the user's SNS account when collecting travel information to make more personalized suggestions. The information collection unit, for example, collects information from the user's SNS account and utilizes it to propose travel plans. For example, the unit proposes optimal plans based on travel destinations and interests shared by the user on SNS. The information collection unit also analyzes information from the user's SNS account to make more personalized suggestions. For example, the unit proposes plans that match the user's interests based on the accounts the user follows and the content of their posts. The information collection unit also collects information from the user's SNS account and utilizes it to propose travel plans. For example, the unit proposes optimal plans based on posts and comments that the user has "liked" on SNS. This allows the information from the user's SNS account to be utilized to make more personalized suggestions.

[0070] The information collection unit collects information from different languages ​​and cultural spheres and can propose travel plans from an international perspective. For example, the generation AI collects information from different languages ​​and cultural spheres and proposes travel plans from an international perspective. For example, it proposes plans based on tourist information and event information provided in the local language. The information collection unit also collects information from different cultural spheres and proposes travel plans from an international perspective based on the user's order. For example, it proposes tourist spots and activities based on local culture and customs. The information collection unit also collects information from different languages ​​and cultural spheres and proposes travel plans from an international perspective. For example, it proposes plans based on local food culture and traditional events. This allows the generation AI to collect information from different languages ​​and cultural spheres and propose travel plans from an international perspective.

[0071] The information collection unit can use the emotion estimation function to analyze the user's emotional response to the collected information and preferentially provide information that elicits a positive response. The information collection unit, for example, uses the emotion estimation function to analyze the user's emotional response to the collected information and preferentially provide information that elicits a positive response. For example, information that makes the user feel happy is preferentially displayed. The information collection unit also monitors the user's emotional response to the collected information in real time and preferentially provides information that elicits a positive response. For example, it suggests tourist spots and activities that will excite the user. The information collection unit also uses the emotion estimation function to analyze the user's emotional response to the collected information and preferentially provide information that elicits a positive response. For example, it suggests hotels and restaurants that will satisfy the user. In this way, the user's emotional response to the collected information can be analyzed and information that elicits a positive response can be preferentially provided.

[0072] The plan generation unit can generate multiple scenarios based on a user's order and present the advantages and disadvantages of each scenario. For example, the plan generation unit uses a generation AI to generate multiple scenarios based on a user's order and present the advantages and disadvantages of each scenario. For example, it compares direct flights with connecting flights and shows the differences in cost and time. The plan generation unit also uses a generation AI to generate multiple scenarios and present the advantages and disadvantages of each scenario. For example, it presents different hotel options and explains the differences in price, location, and services. The plan generation unit also uses a generation AI to generate multiple scenarios based on a user's order and present the advantages and disadvantages of each scenario. For example, it presents options for tourist destinations and compares them in terms of accessibility and the appeal of tourist spots. This allows multiple scenarios to be generated and the advantages and disadvantages of each to be presented.

[0073] The plan generation unit can learn from the user's past feedback and generate a more accurate itinerary plan. In the plan generation unit, for example, the generation AI learns from the user's past feedback and generates a more accurate itinerary plan. For example, it proposes a plan that suits the user's preferences based on plans that have received high ratings in the past. In addition, the plan generation unit analyzes the user's past feedback and the generation AI generates a more accurate itinerary plan. For example, it proposes a plan that improves on aspects that the user was dissatisfied with in past trips. In addition, the plan generation unit learns from the user's past feedback and generates a more accurate itinerary plan. For example, it proposes a plan that incorporates elements that the user was particularly satisfied with in past trips. In this way, it is possible to learn from the user's past feedback and generate a more accurate itinerary plan.

[0074] The plan generation unit can use the emotion estimation function to preferentially generate itinerary plans that will evoke the most positive emotions in the user. For example, the plan generation unit uses the emotion estimation function to preferentially generate itinerary plans that will evoke the most positive emotions in the user. For example, the plan generation unit proposes plans that include tourist spots and activities that bring joy to the user. The plan generation unit also monitors the user's emotional responses in real time to generate itinerary plans that will evoke the most positive emotions. For example, the plan generation unit proposes plans that include events and activities that will excite the user. The plan generation unit also uses the emotion estimation function to preferentially generate itinerary plans that will evoke the most positive emotions in the user. For example, the plan generation unit proposes plans that include hotels and restaurants where the user can relax. In this way, it is possible to preferentially generate itinerary plans that will evoke the most positive emotions in the user.

[0075] The plan generation unit can generate itinerary plans according to different seasons and events, and provide the user with options. For example, the generation AI in the plan generation unit generates itinerary plans according to different seasons and events, and provides the user with options. For example, seasonal plans such as cherry blossom viewing in the spring and beach resorts in the summer are proposed. The plan generation unit also generates itinerary plans according to different events, and provides the user with options. For example, plans tailored to specific events such as music festivals and sporting events are proposed. The generation AI in the plan generation unit also generates itinerary plans according to different seasons and events, and provides the user with options. For example, plans including seasonal events such as Christmas markets and New Year's countdown events are proposed. In this way, itinerary plans according to different seasons and events can be generated, and the user can be provided with options.

[0076] The plan generation unit can generate an optimal itinerary plan for group travel that integrates the orders of multiple users. In the plan generation unit, for example, a generation AI integrates the orders of multiple users to generate an optimal itinerary plan for group travel. For example, it proposes a plan that reflects the preferences of each user. In addition, the plan generation unit generates an optimal itinerary plan for group travel that integrates the orders of multiple users. For example, it proposes a plan that includes tourist spots and activities that will satisfy everyone. In addition, the plan generation unit generates an optimal itinerary plan for group travel that integrates the orders of multiple users. For example, it proposes a plan that reflects the preferences of each user in a balanced manner. In this way, an optimal itinerary plan can be generated that integrates the orders of multiple users.

[0077] The plan generation unit can use the emotion estimation function to monitor the user's emotional response to the generated itinerary plan in real time and continuously adjust the optimal plan. The plan generation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the generated itinerary plan in real time and continuously adjust the optimal plan. For example, if the user's emotional score is low, the plan is regenerated. The plan generation unit also monitors the user's emotional response in real time and continuously adjusts the generated itinerary plan. For example, if the user is dissatisfied, a different plan is proposed. The plan generation unit also uses the emotion estimation function to monitor the user's emotional response to the generated itinerary plan in real time and continuously adjust the optimal plan. For example, if the user's emotional score is high, that plan is preferentially adopted. In this way, the user's emotional response to the generated itinerary plan can be monitored in real time and the optimal plan can be continuously adjusted.

[0078] The plan generation unit can suggest hidden spots only locals know, instead of regular tourist routes, based on the user's order. For example, the generation AI of the plan generation unit suggests hidden spots only locals know, based on the user's order. For example, it introduces famous local shops and scenery that are not listed in tourist guides. The plan generation unit also suggests hidden spots, instead of regular tourist routes, based on the user's order. For example, it introduces markets and cafes where locals gather. The plan generation unit also suggests hidden spots only locals know, based on the user's order. For example, it introduces quiet beaches and parks with few tourists. This makes it possible to suggest hidden spots only locals know, instead of regular tourist routes.

[0079] The plan generation unit can generate an itinerary plan along a specific theme based on the user's interests and hobbies. In the plan generation unit, for example, a generation AI generates an itinerary plan along a specific theme based on the user's interests and hobbies. For example, a plan touring historical sites is proposed to a user who loves history. The plan generation unit also generates an itinerary plan along a specific theme based on the user's hobbies. For example, a plan to enjoy local gourmet food is proposed to a user who loves food. In addition, the plan generation unit generates an itinerary plan along a specific theme based on the user's interests and hobbies using a generation AI. For example, a plan including hiking and camping is proposed to a user who loves the outdoors. In this way, an itinerary plan along a specific theme can be generated based on the user's interests and hobbies.

[0080] The plan generation unit can use the emotion estimation function to propose an innovative route that will most surprise or delight the user. For example, the plan generation unit uses the emotion estimation function to propose an innovative route that will most surprise or delight the user. For example, the plan generation unit proposes a plan that includes tourist spots or activities that the user did not expect. The plan generation unit also monitors the user's emotional reactions in real time to propose an innovative route that will most surprise or delight the user. For example, the plan generation unit proposes a plan that includes events or activities that will excite the user. The plan generation unit also uses the emotion estimation function to propose an innovative route that will most surprise or delight the user. For example, the plan generation unit proposes a plan that includes hidden spots or unique experiences where the user can relax. In this way, it is possible to propose an innovative route that will most surprise or delight the user.

[0081] The plan generation unit can propose unique routes that combine different means of transportation, providing the user with a new experience. For example, the generation AI of the plan generation unit proposes unique routes that combine different means of transportation, providing the user with a new experience. For example, it proposes a plan that combines airplanes and ferries. The plan generation unit can also propose routes that combine unique means of transportation, providing the user with a new experience. For example, it proposes a plan that combines trains and bicycles. The plan generation unit can also propose unique routes that combine different means of transportation, providing the user with a new experience. For example, it proposes a plan that combines buses and walking. This allows the generation AI to propose unique routes that combine different means of transportation, providing the user with a new experience.

[0082] The plan generation unit can generate a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. The plan generation unit, for example, uses a generation AI to generate a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. For example, it proposes a plan to visit multiple countries. The plan generation unit also generates a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. For example, it proposes a plan to visit tourist spots in different cultural spheres. The plan generation unit also generates a multinational itinerary plan that allows users to experience different cultures and histories based on a user's order. For example, it proposes a plan that includes historical sites and cultural events. This makes it possible to generate a multinational itinerary plan that allows users to experience different cultures and histories.

[0083] The plan generation unit can use the emotion estimation function to propose a plan including a unique activity that will make the user feel the most positive emotion. For example, the plan generation unit uses the emotion estimation function to propose a plan including a unique activity that will make the user feel the most positive emotion. For example, it proposes a plan including an activity that will excite the user. The plan generation unit also monitors the user's emotional response in real time to propose a plan including a unique activity that will make the user feel the most positive emotion. For example, it proposes a plan including an activity that will make the user feel joy. The plan generation unit also uses the emotion estimation function to propose a plan including a unique activity that will make the user feel the most positive emotion. For example, it proposes a plan including an activity that will relax the user. In this way, it is possible to propose a plan including a unique activity that will make the user feel the most positive emotion.

[0084] The plan generation unit provides the user with a detailed explanation of each element of the itinerary plan, allowing the user to deepen their understanding. In the plan generation unit, for example, the generation AI provides the user with a detailed explanation of each element of the itinerary plan. For example, it provides detailed explanations of the history, highlights, and access methods of each tourist destination. The plan generation unit also provides the user with a detailed explanation of each element of the itinerary plan, allowing the user to deepen their understanding. For example, it provides detailed explanations of hotel facilities and services, surrounding tourist attractions, and the like. In the plan generation unit, the generation AI also provides the user with a detailed explanation of each element of the itinerary plan. For example, it provides detailed explanations of flight flights, connecting flights, and airport procedures. This allows the user to deepen their understanding by providing a detailed explanation of each element of the itinerary plan.

[0085] The plan generation unit can reflect user feedback in real time and instantly present a revised plan. In the plan generation unit, for example, the generation AI reflects user feedback in real time and instantly presents a revised plan. For example, the plan generation unit instantly generates a plan that adds tourist attractions desired by the user. The plan generation unit also reflects user feedback in real time and presents a revised plan. For example, the plan generation unit instantly generates a plan that reflects changes to the hotel desired by the user. The plan generation unit also reflects user feedback in real time and instantly presents a revised plan. For example, the plan generation unit instantly generates a plan that reflects changes to the means of transportation desired by the user. This allows the generation AI to reflect user feedback in real time and instantly present a revised plan.

[0086] The plan generation unit can use the emotion estimation function to preferentially present revision proposals that will most satisfy the user. The plan generation unit, for example, uses the emotion estimation function to preferentially present revision proposals that will most satisfy the user. For example, revision proposals with high user emotion scores are preferentially displayed. The plan generation unit also monitors the user's emotional reactions in real time and preferentially presents revision proposals that will most satisfy the user. For example, revision proposals that make the user feel happy are preferentially displayed. The plan generation unit also uses the emotion estimation function to preferentially present revision proposals that will most satisfy the user. For example, revision proposals that will relax the user are preferentially displayed. In this way, revision proposals that will most satisfy the user can be preferentially presented.

[0087] The plan generation unit can present multiple revision plans to the user and allow the user to select from them. For example, the generation AI of the plan generation unit presents multiple revision plans to the user and allows the user to select from them. For example, it presents options for different tourist spots and hotels. The plan generation unit can also present multiple revision plans to the user and allow the user to select from them. For example, it presents options for different means of transportation and routes. The plan generation unit can also present multiple revision plans to the user and allow the user to select from them. For example, it presents options for different activities and events. This allows the generation AI of the plan generation unit to present multiple revision plans to the user and allow the user to select from them.

[0088] The plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, the plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, it may propose optimal tourist spots and hotels based on the user's preferences. Also, the plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, it may propose optimal means of transportation and routes based on the user's preferences. Also, the plan generation unit allows the generation AI to automatically propose optimal revisions based on user feedback. For example, it may propose optimal activities and events based on the user's preferences. This allows the generation AI to automatically propose optimal revisions based on user feedback.

[0089] The plan generation unit can use the emotion estimation function to generate, in real time, proposed revisions that will make the user feel the most positive emotion. The plan generation unit, for example, uses the emotion estimation function to generate, in real time, proposed revisions that will make the user feel the most positive emotion. For example, proposed revisions with a high user emotion score are preferentially displayed. The plan generation unit also monitors the user's emotional response in real time and generates, in real time, proposed revisions that will make the user feel the most positive emotion. For example, proposed revisions that make the user feel happy are preferentially displayed. The plan generation unit also uses the emotion estimation function to generate, in real time, proposed revisions that will make the user feel the most positive emotion. For example, proposed revisions that will make the user feel relaxed are preferentially displayed. In this way, proposed revisions that will make the user feel the most positive emotion can be generated in real time.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The order input unit can also monitor the user's health condition and suggest appropriate travel plans. For example, it can measure the user's heart rate and blood pressure and suggest plans based on their health condition. The order input unit can also create a reasonable schedule based on the user's health data. For example, for a user who is not confident in their physical strength, it can suggest a plan that includes more rest. The order input unit can also monitor the user's health condition in real time and adjust the plan as needed. For example, if the user's health deteriorates, the plan can be changed to allow for more rest time.

[0092] The order input unit can estimate the user's emotions and suggest activities for the trip based on the estimated emotions. For example, if the user is excited, active sports or adventurous activities can be suggested. If the user feels like relaxing, relaxation activities such as spas or hot springs can be suggested. If the user is feeling stressed, activities that help relieve stress, such as walking in nature or meditation, can be suggested. In this way, activities can be suggested that match the user's emotional state.

[0093] The order input unit can suggest meal plans for the user's trip based on the user's past travel history. For example, it can suggest meal plans that the user prefers based on restaurants visited and dishes eaten in the past. The order input unit can also learn the user's food preferences and suggest recommended restaurants and dishes at the travel destination. For example, it can suggest vegetarian restaurants to a vegetarian user. The order input unit can also optimize the meal plan based on the user's past feedback. For example, it can re-suggest restaurants that have received high ratings in the past. This makes it possible to suggest optimal meal plans based on the user's past travel history and preferences.

[0094] The order input unit can use the emotion estimation function to provide support to reduce the stress and anxiety the user feels during their trip. For example, if the user feels stressed, it can display relaxing music or scenery. If the user feels anxious, it can provide reassuring messages or advice. The order input unit can also monitor the user's emotional state in real time and provide support as needed. For example, if the user feels nervous, it can suggest deep breathing or relaxation techniques. This can reduce the stress and anxiety the user feels during their trip.

[0095] The order input unit can accept orders entered by the user through a visual interface. For example, the user can select desired tourist attractions or activities using images or icons. The order input unit can also provide an interface that allows the user to specify a desired route or destination on a map. For example, the user can select a tourist attraction by tapping on the map. The order input unit can also provide an interface that allows the user to combine desired elements by dragging and dropping. For example, the user can add a tourist attraction or activity to the itinerary by dragging it. This allows the user to enter an order visually.

[0096] The order input unit not only allows multiple users to enter orders simultaneously, but also allows each user to set their own roles. For example, a trip leader can manage the overall schedule, while other members enter their own preferences. The order input unit can also provide a function to support coordination of opinions within a group. For example, a voting function can be used to decide which tourist spots to visit. The order input unit can also share the input contents of each user in real time, allowing everyone to create a plan based on the same information. For example, it can display the preferences of each member and generate a plan that reflects everyone's opinions. This allows multiple users to enter orders efficiently.

[0097] The order input unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the user while they are typing. For example, if the user is having fun, an encouraging message or emoticon can be displayed. If the user is satisfied, a compliment or message of gratitude can be displayed. The order input unit can also monitor the user's emotional state in real time and suggest actions to elicit positive emotions. For example, if the user is excited, more interesting information or activities can be suggested. This can reinforce the positive emotions felt by the user while they are typing.

[0098] The information collection unit can use the generation AI to collect customized travel information based on the user's interests. For example, if the user is interested in history, information on historical sites and museums can be collected preferentially. The information collection unit can also collect travel information on specific themes based on the user's interests. For example, if a user is interested in gourmet food, information on local gourmet food and restaurants can be collected. The information collection unit can also provide customized travel information in real time based on the user's interests. For example, if the user is interested in the outdoors, information on hiking trails and campsites can be collected. This makes it possible to provide customized travel information based on the user's interests.

[0099] The information collection unit can not only integrate data from different sources, but also use an algorithm to evaluate the reliability of the data. For example, it can evaluate the accuracy of past data from each source and prioritize the use of highly reliable sources. The information collection unit can also continuously evaluate the reliability of sources based on user feedback. For example, if a user is satisfied with the information provided, it can determine that the source is highly reliable. The information collection unit can also make the process of integrating data from different sources and selecting highly reliable information transparent. For example, it can display to the user which sources the data was collected from. This makes it possible to integrate data from different sources and provide highly reliable information.

[0100] The information collection unit can use the emotion estimation function to evaluate how well the collected information meets the user's expectations and re-collect information as necessary. For example, the quality of the information is evaluated based on the user's emotional response and re-collect information as necessary. The information collection unit also monitors the user's emotional response to the collected information in real time and re-collects information if the information does not meet expectations. For example, if the user is dissatisfied, it collects data from another source. The information collection unit also uses the emotion estimation function to evaluate how well the collected information meets the user's expectations and re-collects information as necessary. For example, if the user's emotion score is low, it re-collects information and improves the plan. This makes it possible to evaluate whether the collected information meets the user's expectations and re-collect information as necessary.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The order input unit inputs the user's order. For example, the user can enter their wishes and requirements while talking to the generating AI through the app. Step 2: The information collection unit collects various information based on the order entered by the order input unit. For example, the generation AI collects flight and hotel information, tourist destination data, etc. based on the user's order. Step 3: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes information such as low-cost flights, routes available for award tickets, and hotels where points can be used to stay. Step 4: The plan generation unit generates an optimal itinerary plan based on the results of the analysis by the analysis unit. For example, the generation AI generates an optimal itinerary plan that meets the user's wishes based on the collected and analyzed information.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an order input unit for inputting a user's order; an information collection unit that collects various information based on the order input by the order input unit; an analysis unit that analyzes the information collected by the information collection unit; a plan generation unit that generates an optimal itinerary plan based on the results of the analysis by the analysis unit; A system characterized by:

2. The order input unit Analyzing the tone or speed of the user's voice to estimate their emotional state and optimize the order content 2. The system of claim 1.

3. The information collecting unit The generation AI is used to collect the latest travel information in real time and immediately reflect it in the user's order.

2. The system of claim 1.

4. The plan generation unit Generate multiple scenarios based on the user's order and present the advantages and disadvantages of each scenario.

2. The system of claim 1.

5. The plan generation unit Based on the user's order, the hidden spots known only to locals are suggested instead of the usual tourist routes.

2. The system of claim 1.

6. The information collecting unit Evaluating how well the collected information meets the user's expectations and recollecting the information as necessary.

2. The system of claim 1.

7. The plan generation unit The itinerary plan that gives the user the most positive feelings is generated preferentially.

2. The system of claim 1.

8. The plan generation unit Proposing a novel route that will give the user the most surprise or delight 2. The system of claim 1.

Images