System

Abstract

An object of a system according to an embodiment is to propose an optimal travel destination and a customized travel plan on the basis of user information.SOLUTION: A system according to an embodiment includes a destination selection unit, a travel plan creation unit, and an activity reservation unit. The destination selection unit proposes an optimal travel destination based on the information of the user. The travel plan creation unit creates a travel plan customized based on the travel destination proposed by the destination selection unit. The activity reservation unit supports reservation of an activity selected by the user based on the travel plan created by the travel plan creation 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 technologies do not adequately propose optimal travel destinations and travel plans based on a user's individual preferences and needs, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal travel destinations and customized travel plans based on user information. [Means for solving the problem]

[0006] The system according to the embodiment includes a destination selection unit, a travel plan creation unit, and an activity booking unit. The destination selection unit suggests optimal travel destinations based on user information. The travel plan creation unit creates a customized travel plan based on the travel destinations suggested by the destination selection unit. The activity booking unit supports booking of activities selected by the user based on the travel plan created by the travel plan creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal travel destinations and customized travel plans based on user information. [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) TravelWhiz Explorer, an embodiment of the present invention, is a system that provides support to travelers at every step, from selecting a destination to booking activities. This system creates a customized travel plan that is individually optimized for each user and matches their travel preferences and needs, improving travelers' engagement and experience. This allows TravelWhiz Explorer to provide consistent support to travelers and improve their engagement and experience at every step, from trip planning to execution and feedback.

[0029] TravelWhiz Explorer according to an embodiment includes a destination selection unit, a travel plan creation unit, and an activity booking unit. The destination selection unit suggests optimal travel destinations based on user information. For example, a generation AI suggests optimal travel destinations based on information such as the user's past travel history, preferences, budget, and travel purpose. The generation AI analyzes user information using a text generation AI (e.g., LLM) to suggest optimal travel destinations. The generation AI can also use a multimodal generation AI to analyze user information from multiple angles and suggest optimal travel destinations. The travel plan creation unit creates a customized travel plan based on the travel destinations suggested by the destination selection unit. For example, the generation AI creates a customized travel plan based on the user's preferences and needs. The generation AI creates a travel plan including activities, accommodations, restaurants, etc. based on information related to the user's preferences and needs. The activity booking unit supports booking activities selected by the user based on the travel plan created by the travel plan creation unit. For example, if a user wants to reserve tickets to a museum, the generation AI automatically handles the reservation process. The activity booking unit also supports restaurant reservations and tour arrangements. Generative AI processes the booking process based on information about the activities the user wants to book, allowing TravelWhiz Explorer to improve traveler engagement and experience by suggesting the best travel destinations based on the user's information, creating customized itineraries, and assisting with activity booking.

[0030] The destination selection unit can analyze the user's social media posts or photos and reflect them in travel destination suggestions. For example, the destination selection unit analyzes the user's social media posts, and the generation AI suggests travel destinations based on the content. For example, if the user frequently posts photos of nature, the generation AI will suggest travel destinations rich in nature. Also, if the user posts about cultural events, the generation AI will suggest cities where cultural experiences are available. This makes it possible to suggest more personalized travel destinations by analyzing the user's social media posts and photos.

[0031] The destination selection unit can suggest travel destinations that are optimal for the user's physical condition based on the user's health data. For example, the destination selection unit analyzes the user's step count data, and the generation AI suggests travel destinations based on that data. For example, if the user normally walks a lot, the generation AI will suggest places with plenty of hiking trails. The generation AI can also analyze the user's heart rate data, and based on that data, suggest relaxing travel destinations. For example, if the user's heart rate is high, the generation AI will suggest a spa resort. This makes it possible to suggest optimal travel destinations based on the user's health data, thereby providing travel plans that take the traveler's health into consideration.

[0032] The travel plan creation unit can analyze the success rate of the user's past travel plans and recreate the travel plan that provides the highest level of satisfaction. The travel plan creation unit, for example, analyzes the success rate of the user's past travel plans and the generation AI recreates the plan that provides the highest level of satisfaction. For example, it proposes a plan that includes activities that have received high ratings in the past. The generation AI also creates a new travel plan based on the success rate of the user's past travel plans. For example, it proposes a new plan by combining elements of plans that have been successful in the past. In this way, the plan that provides the highest level of satisfaction can be recreated by analyzing the success rate of the user's past travel plans.

[0033] The travel plan creation unit can customize restaurants and meal plans based on the user's dietary preferences and allergy information. For example, the travel plan creation unit collects the user's dietary preferences and allergy information, and the generation AI customizes restaurants and meal plans based on that data. For example, a vegetarian user can be suggested a vegetarian restaurant. Also, if the user is allergic to a specific ingredient, the generation AI can suggest a restaurant that offers a menu that avoids that ingredient. In this way, customizing restaurants and meal plans based on the user's dietary preferences and allergy information improves user satisfaction.

[0034] The travel plan creation unit can propose travel plans that include special workshops and classes based on the user's hobbies and interests. For example, the travel plan creation unit analyzes the user's hobbies and interests, and the generation AI proposes plans that include special workshops and classes based on that data. For example, a local cooking class can be proposed to a user who likes cooking, or an art class can be proposed to a user who is interested in art. In this way, by proposing special workshops and classes based on the user's hobbies and interests, traveler satisfaction can be improved.

[0035] The travel plan creation unit can suggest activities that will encourage interaction with local people at the user's travel destination. For example, the generation AI of the travel plan creation unit suggests activities that will encourage interaction with local people at the user's travel destination. For example, it may suggest homestays with local families. It may also suggest participation in local cultural events. This improves traveler satisfaction by suggesting activities that will encourage interaction with local people at the user's travel destination.

[0036] The activity reservation unit can analyze the user's past reservation history and prioritize suggesting the activities that provide the highest level of satisfaction. For example, the activity reservation unit analyzes the user's past reservation history, and the generation AI prioritizes suggesting the activities that provide the highest level of satisfaction. For example, activities that have received high ratings in the past may be re-suggested. The generation AI may also suggest new activities based on the user's past reservation history. For example, a new activity may be proposed by combining elements of activities that have been successful in the past. In this way, by analyzing the user's past reservation history, the activity that provides the highest level of satisfaction may be prioritized.

[0037] The activity reservation unit can automatically reserve activities at the optimal timing, taking into account the user's schedule and travel time. The activity reservation unit, for example, analyzes the user's schedule, and the generation AI automatically reserves activities at the optimal timing. For example, it proposes an efficient schedule taking travel time into account. The generation AI also reserves the optimal activity based on the user's schedule and travel time. For example, it reserves an activity at a time that avoids traffic congestion. This improves traveler satisfaction by automatically reserving activities at the optimal timing, taking into account the user's schedule and travel time.

[0038] The activity booking unit can also consider the activity preferences of the user's friends and family and suggest activities that can be enjoyed as a group. For example, the activity booking unit analyzes the activity preferences of the user's friends and family, and the generation AI suggests activities that can be enjoyed as a group based on that data. For example, it suggests activities that everyone can enjoy. The generation AI also suggests new activities based on the preferences of the user's friends and family. For example, it suggests workshops and tours that everyone can participate in. In this way, the activity booking unit takes into consideration the activity preferences of the user's friends and family and suggests activities that can be enjoyed as a group, thereby improving traveler satisfaction.

[0039] The activity booking unit can support reservations for local special events and exclusive activities. For example, the activity booking unit collects information on local special events and exclusive activities, and the generation AI supports reservations based on that data. For example, it can reserve tickets for local festivals and concerts. It also supports reservations for special tours and experiential activities. This improves traveler satisfaction by supporting reservations for local special events and exclusive activities.

[0040] The travel plan creation unit can propose optimal travel routes and means of transportation in real time based on the user's location information. The travel plan creation unit, for example, analyzes the user's location information, and the generation AI proposes optimal travel routes in real time. For example, it proposes routes that avoid traffic congestion. The generation AI also proposes optimal means of transportation based on the user's location information. For example, it suggests using public transportation or a taxi. In this way, by proposing optimal travel routes and means of transportation in real time based on the user's location information, traveler satisfaction is improved.

[0041] The travel plan creation unit can provide advice and support tailored to the user's physical condition based on the user's health data. For example, the travel plan creation unit analyzes the user's health data, and the generation AI provides advice tailored to the user's physical condition. For example, if the user's heart rate is high, the generation AI will suggest a relaxing activity. The generation AI will also suggest an appropriate amount of exercise based on the user's step count data. This improves traveler satisfaction by providing advice and support tailored to the user's physical condition based on the user's health data.

[0042] The travel plan creation unit can also take into account the location information of the user's friends and family to support group travel and activities. For example, the travel plan creation unit analyzes the location information of the user's friends and family, and the generation AI supports group travel. For example, it suggests places where it is easy for everyone to meet up. Furthermore, based on the location information of the user's friends and family, the generation AI suggests activities that can be enjoyed by the group. For example, it suggests tours and events that everyone can participate in. In this way, the location information of the user's friends and family is also taken into account to support group travel and activities, thereby improving traveler satisfaction.

[0043] The travel plan creation unit can provide information on local emergency contacts and medical institutions to support emergency response. For example, the travel plan creation unit collects information on local emergency contacts and medical institutions, and the generation AI uses that data to support emergency response. For example, it provides information on the nearest hospitals and police stations. It also provides local emergency contacts so that users can respond quickly in the event of an emergency. This ensures the safety of travelers by providing information on local emergency contacts and medical institutions and supporting emergency response.

[0044] The travel plan creation unit can analyze user feedback in detail and reflect it in the next travel plan. For example, the travel plan creation unit analyzes user feedback in detail, and the generation AI optimizes the next travel plan based on that data. For example, if the user enjoyed a particular activity, the generation AI can include a similar activity in the next plan. The generation AI can also create a new travel plan based on user feedback. For example, it can propose a plan that reflects the user's opinions. In this way, by analyzing user feedback in detail and reflecting it in the next travel plan, traveler satisfaction can be improved.

[0045] The travel plan creation unit can automatically generate travel guides and reviews that are useful to other users based on user feedback. In the travel plan creation unit, for example, the generation AI automatically generates travel guides that are useful to other users based on user feedback. For example, it creates guides that include detailed reviews of specific activities or restaurants. The generation AI also creates new reviews based on user feedback. For example, it suggests reviews that reflect the user's opinions. In this way, travel guides and reviews that are useful to other users based on user feedback are automatically generated, thereby improving traveler satisfaction.

[0046] The travel plan creation unit also collects feedback from the user's friends and family, which can improve the satisfaction of the entire group. For example, the travel plan creation unit collects feedback from the user's friends and family, and the generation AI uses that data to improve the satisfaction of the entire group. For example, activities that everyone can enjoy may be included in the next plan. The generation AI may also propose a new plan based on the feedback from the user's friends and family. For example, it may propose a plan that reflects everyone's opinions. In this way, feedback from the user's friends and family may also be collected, improving the satisfaction of the entire group and thereby improving the satisfaction of travelers.

[0047] The travel plan creation unit can suggest new travel destinations and activities based on feedback. In the travel plan creation unit, for example, the generation AI suggests new travel destinations based on user feedback. For example, if the user enjoyed a particular type of travel destination, similar travel destinations are suggested. Also, the generation AI suggests new activities based on user feedback. For example, if the user enjoyed a particular activity, a similar activity is suggested. In this way, suggesting new travel destinations and activities based on feedback improves traveler satisfaction.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] TravelWhiz Explorer can also suggest travel plans that include special workshops and classes based on the user's hobbies and interests. For example, it can suggest local cooking classes to a user who loves cooking, or art classes to a user who is interested in art. This improves traveler satisfaction by suggesting special workshops and classes based on the user's hobbies and interests.

[0050] TravelWhiz Explorer can also suggest travel destinations that are optimal for a user's physical condition based on their health data. For example, the AI ​​can analyze the user's step count data and use that data to suggest travel destinations. For example, if the user normally walks a lot, the AI ​​can suggest places with plenty of hiking trails. The AI ​​can also analyze the user's heart rate data and use that data to suggest relaxing travel destinations. For example, if the user's heart rate is high, the AI ​​can suggest spa resorts. This makes it possible to suggest optimal travel destinations based on the user's health data, providing travel plans that take the traveler's health into consideration.

[0051] TravelWhiz Explorer can also analyze a user's social media posts or photos and reflect this in its travel destination suggestions. For example, a user's social media posts can be analyzed and the generation AI can suggest travel destinations based on the content. For example, if a user frequently posts photos of nature, the generation AI can suggest travel destinations rich in nature. Also, if a user posts about cultural events, the generation AI can suggest cities where users can have cultural experiences. This makes it possible to suggest more personalized travel destinations by analyzing a user's social media posts and photos.

[0052] TravelWhiz Explorer can also analyze the success rate of a user's past travel plans and recreate the most satisfying travel plan. For example, by analyzing the success rate of a user's past travel plans, the generation AI can recreate the plan that gave the most satisfaction. For example, it can suggest plans that include activities that have been highly rated in the past. The generation AI can also create new travel plans based on the success rate of a user's past travel plans. For example, it can propose a new plan by combining elements of plans that have been successful in the past. In this way, by analyzing the success rate of a user's past travel plans, the generation AI can recreate the plan that gave the most satisfaction.

[0053] TravelWhiz Explorer also takes into account the activity preferences of the user's friends and family, allowing it to suggest activities that can be enjoyed as a group. For example, it analyzes the activity preferences of the user's friends and family, and the generation AI uses that data to suggest activities that can be enjoyed as a group. For example, it suggests activities that everyone can enjoy. The generation AI also suggests new activities based on the preferences of the user's friends and family. For example, it suggests workshops and tours that everyone can participate in. In this way, the generation AI takes into account the activity preferences of the user's friends and family and suggests activities that can be enjoyed as a group, thereby improving traveler satisfaction.

[0054] TravelWhiz Explorer can also suggest optimal travel routes and means of transportation in real time based on the user's location information. For example, by analyzing the user's location information, the generation AI can suggest optimal travel routes in real time. For example, it can suggest routes that avoid traffic congestion. The generation AI can also suggest optimal means of transportation based on the user's location information. For example, it can suggest the use of public transportation or taxis. This improves traveler satisfaction by suggesting optimal travel routes and means of transportation in real time based on the user's location information.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The destination selection unit suggests the optimal travel destination based on the user's information. For example, the generation AI suggests the optimal travel destination based on information such as the user's past travel history, preferences, budget, and travel purpose. The generation AI uses text generation AI (e.g., LLM) to analyze the user's information and suggest the optimal travel destination. The generation AI can also use multimodal generation AI to analyze the user's information from multiple angles and suggest the optimal travel destination. Step 2: The travel plan creation unit creates a customized travel plan based on the travel destinations suggested by the destination selection unit. For example, the generation AI creates a customized travel plan based on the user's preferences and needs. The generation AI creates a travel plan that includes activities, accommodations, restaurants, etc. based on information about the user's preferences and needs. Step 3: The activity booking unit helps the user book the activities they select based on the travel plan created by the travel plan creation unit. For example, if the user wants to reserve tickets to a museum, the generation AI automatically handles the booking process. It also supports restaurant reservations and tour arrangements. The generation AI handles the booking process based on information about the activities the user wants to book.

[0057] (Example 2) TravelWhiz Explorer, an embodiment of the present invention, is a system that provides support to travelers at every step, from selecting a destination to booking activities. This system creates a customized travel plan that is individually optimized for each user and matches their travel preferences and needs, improving travelers' engagement and experience. This allows TravelWhiz Explorer to provide consistent support to travelers and improve their engagement and experience at every step, from trip planning to execution and feedback.

[0058] TravelWhiz Explorer according to an embodiment includes a destination selection unit, a travel plan creation unit, and an activity booking unit. The destination selection unit suggests optimal travel destinations based on user information. For example, a generation AI suggests optimal travel destinations based on information such as the user's past travel history, preferences, budget, and travel purpose. The generation AI analyzes user information using a text generation AI (e.g., LLM) to suggest optimal travel destinations. The generation AI can also use a multimodal generation AI to analyze user information from multiple angles and suggest optimal travel destinations. The travel plan creation unit creates a customized travel plan based on the travel destinations suggested by the destination selection unit. For example, the generation AI creates a customized travel plan based on the user's preferences and needs. The generation AI creates a travel plan including activities, accommodations, restaurants, etc. based on information related to the user's preferences and needs. The activity booking unit supports booking activities selected by the user based on the travel plan created by the travel plan creation unit. For example, if a user wants to reserve tickets to a museum, the generation AI automatically handles the reservation process. The activity booking unit also supports restaurant reservations and tour arrangements. Generative AI processes the booking process based on information about the activities the user wants to book, allowing TravelWhiz Explorer to improve traveler engagement and experience by suggesting the best travel destinations based on the user's information, creating customized itineraries, and assisting with activity booking.

[0059] The destination selection unit can analyze the user's real-time emotional state and suggest a travel destination that best suits that emotion. For example, when a user is choosing a travel destination, the destination selection unit uses the generation AI to analyze the user's real-time emotional state and suggest a travel destination that best suits that emotion. For example, if the user is feeling stressed, the generation AI will suggest a relaxing beach resort. Also, if the user is excited, the generation AI will suggest a place with plenty of adventure activities. This improves traveler satisfaction by suggesting the best travel destination based on the user's real-time emotional state.

[0060] The destination selection unit can analyze the user's social media posts or photos and reflect them in travel destination suggestions. For example, the destination selection unit analyzes the user's social media posts, and the generation AI suggests travel destinations based on the content. For example, if the user frequently posts photos of nature, the generation AI will suggest travel destinations rich in nature. Also, if the user posts about cultural events, the generation AI will suggest cities where cultural experiences are available. This makes it possible to suggest more personalized travel destinations by analyzing the user's social media posts and photos.

[0061] The destination selection unit can suggest travel destinations that are optimal for the user's physical condition based on the user's health data. For example, the destination selection unit analyzes the user's step count data, and the generation AI suggests travel destinations based on that data. For example, if the user normally walks a lot, the generation AI will suggest places with plenty of hiking trails. The generation AI can also analyze the user's heart rate data, and based on that data, suggest relaxing travel destinations. For example, if the user's heart rate is high, the generation AI will suggest a spa resort. This makes it possible to suggest optimal travel destinations based on the user's health data, thereby providing travel plans that take the traveler's health into consideration.

[0062] The travel plan creation unit can analyze the success rate of the user's past travel plans and recreate the travel plan that provides the highest level of satisfaction. The travel plan creation unit, for example, analyzes the success rate of the user's past travel plans and the generation AI recreates the plan that provides the highest level of satisfaction. For example, it proposes a plan that includes activities that have received high ratings in the past. The generation AI also creates a new travel plan based on the success rate of the user's past travel plans. For example, it proposes a new plan by combining elements of plans that have been successful in the past. In this way, the plan that provides the highest level of satisfaction can be recreated by analyzing the success rate of the user's past travel plans.

[0063] The travel plan creation unit can customize restaurants and meal plans based on the user's dietary preferences and allergy information. For example, the travel plan creation unit collects the user's dietary preferences and allergy information, and the generation AI customizes restaurants and meal plans based on that data. For example, a vegetarian user can be suggested a vegetarian restaurant. Also, if the user is allergic to a specific ingredient, the generation AI can suggest a restaurant that offers a menu that avoids that ingredient. In this way, customizing restaurants and meal plans based on the user's dietary preferences and allergy information improves user satisfaction.

[0064] The travel plan creation unit can monitor the user's emotional state in real time and dynamically adjust the plan to match their mood during the trip. For example, the travel plan creation unit monitors the user's emotional state in real time, and the generation AI dynamically adjusts the travel plan based on that data. For example, if the user is tired, the generation AI adds a relaxing activity. Also, if the user is excited, the generation AI adds an adventure activity. This dynamically adjusts the travel plan based on the user's emotional state, thereby improving traveler satisfaction.

[0065] The travel plan creation unit can propose travel plans that include special workshops and classes based on the user's hobbies and interests. For example, the travel plan creation unit analyzes the user's hobbies and interests, and the generation AI proposes plans that include special workshops and classes based on that data. For example, a local cooking class can be proposed to a user who likes cooking, or an art class can be proposed to a user who is interested in art. In this way, by proposing special workshops and classes based on the user's hobbies and interests, traveler satisfaction can be improved.

[0066] The travel plan creation unit can suggest activities that will encourage interaction with local people at the user's travel destination. For example, the generation AI of the travel plan creation unit suggests activities that will encourage interaction with local people at the user's travel destination. For example, it may suggest homestays with local families. It may also suggest participation in local cultural events. This improves traveler satisfaction by suggesting activities that will encourage interaction with local people at the user's travel destination.

[0067] The travel plan creation unit can use the emotion estimation function to propose a travel plan that will allow the user to be most relaxed and explain the reason for that. For example, the travel plan creation unit uses the emotion estimation function to have the generation AI propose a plan that will allow the user to be most relaxed and explain the reason for that. For example, if the user is feeling stressed, the generation AI will suggest time at a spa or the beach and explain the reason for that. Also, if the user wants to relax, the generation AI will suggest a quiet resort and explain the reason for that. In this way, by using the emotion estimation function to propose a plan that will allow the user to be most relaxed and explaining the reason for that, traveler satisfaction is improved.

[0068] The activity reservation unit can analyze the user's past reservation history and prioritize suggesting the activities that provide the highest level of satisfaction. For example, the activity reservation unit analyzes the user's past reservation history, and the generation AI prioritizes suggesting the activities that provide the highest level of satisfaction. For example, activities that have received high ratings in the past may be re-suggested. The generation AI may also suggest new activities based on the user's past reservation history. For example, a new activity may be proposed by combining elements of activities that have been successful in the past. In this way, by analyzing the user's past reservation history, the activity that provides the highest level of satisfaction may be prioritized.

[0069] The activity reservation unit can automatically reserve activities at the optimal timing, taking into account the user's schedule and travel time. The activity reservation unit, for example, analyzes the user's schedule, and the generation AI automatically reserves activities at the optimal timing. For example, it proposes an efficient schedule taking travel time into account. The generation AI also reserves the optimal activity based on the user's schedule and travel time. For example, it reserves an activity at a time that avoids traffic congestion. This improves traveler satisfaction by automatically reserving activities at the optimal timing, taking into account the user's schedule and travel time.

[0070] The activity booking unit can monitor the user's emotional state in real time and suggest activities that match their mood. For example, the activity booking unit monitors the user's emotional state in real time, and the generation AI uses that data to suggest activities that match their mood. For example, if the user wants to relax, the generation AI will suggest a spa or massage. Also, if the user is feeling active, the generation AI will suggest hiking or cycling. In this way, by monitoring the user's emotional state in real time and suggesting activities that match their mood, traveler satisfaction is improved.

[0071] The activity booking unit can also consider the activity preferences of the user's friends and family and suggest activities that can be enjoyed as a group. For example, the activity booking unit analyzes the activity preferences of the user's friends and family, and the generation AI suggests activities that can be enjoyed as a group based on that data. For example, it suggests activities that everyone can enjoy. The generation AI also suggests new activities based on the preferences of the user's friends and family. For example, it suggests workshops and tours that everyone can participate in. In this way, the activity booking unit takes into consideration the activity preferences of the user's friends and family and suggests activities that can be enjoyed as a group, thereby improving traveler satisfaction.

[0072] The activity booking unit can support reservations for local special events and exclusive activities. For example, the activity booking unit collects information on local special events and exclusive activities, and the generation AI supports reservations based on that data. For example, it can reserve tickets for local festivals and concerts. It also supports reservations for special tours and experiential activities. This improves traveler satisfaction by supporting reservations for local special events and exclusive activities.

[0073] The activity booking unit can use the emotion estimation function to suggest the activity that the user will enjoy most and explain the reason for that. For example, the activity booking unit uses the emotion estimation function to have the generation AI suggest the activity that the user will enjoy most and explain the reason for that. For example, if the user prefers adventure activities, the generation AI will suggest them while explaining the reason for that. Also, if the user wants to relax, the generation AI will suggest a spa or massage and explain the reason for that. In this way, by using the emotion estimation function to suggest the activity that the user will enjoy most and explaining the reason for that, traveler satisfaction is improved.

[0074] The travel plan creation unit can analyze the user's real-time emotional state and suggest local activities and restaurants that match the user's mood. For example, the travel plan creation unit analyzes the user's real-time emotional state, and the generation AI suggests local activities and restaurants based on that data. For example, if the user wants to relax, the generation AI suggests quiet restaurants and spas. Also, if the user is feeling active, the generation AI suggests active activities and lively restaurants. In this way, analyzing the user's real-time emotional state and suggesting activities and restaurants that match the user's mood improves traveler satisfaction.

[0075] The travel plan creation unit can propose optimal travel routes and means of transportation in real time based on the user's location information. The travel plan creation unit, for example, analyzes the user's location information, and the generation AI proposes optimal travel routes in real time. For example, it proposes routes that avoid traffic congestion. The generation AI also proposes optimal means of transportation based on the user's location information. For example, it suggests using public transportation or a taxi. In this way, by proposing optimal travel routes and means of transportation in real time based on the user's location information, traveler satisfaction is improved.

[0076] The travel plan creation unit can provide advice and support tailored to the user's physical condition based on the user's health data. For example, the travel plan creation unit analyzes the user's health data, and the generation AI provides advice tailored to the user's physical condition. For example, if the user's heart rate is high, the generation AI will suggest a relaxing activity. The generation AI will also suggest an appropriate amount of exercise based on the user's step count data. This improves traveler satisfaction by providing advice and support tailored to the user's physical condition based on the user's health data.

[0077] The travel plan creation unit can also take into account the location information of the user's friends and family to support group travel and activities. For example, the travel plan creation unit analyzes the location information of the user's friends and family, and the generation AI supports group travel. For example, it suggests places where it is easy for everyone to meet up. Furthermore, based on the location information of the user's friends and family, the generation AI suggests activities that can be enjoyed by the group. For example, it suggests tours and events that everyone can participate in. In this way, the location information of the user's friends and family is also taken into account to support group travel and activities, thereby improving traveler satisfaction.

[0078] The travel plan creation unit can provide information on local emergency contacts and medical institutions to support emergency response. For example, the travel plan creation unit collects information on local emergency contacts and medical institutions, and the generation AI uses that data to support emergency response. For example, it provides information on the nearest hospitals and police stations. It also provides local emergency contacts so that users can respond quickly in the event of an emergency. This ensures the safety of travelers by providing information on local emergency contacts and medical institutions and supporting emergency response.

[0079] The travel plan creation unit can use the emotion estimation function to provide the support that the user feels most at ease with and explain the reasons for that. The travel plan creation unit, for example, uses the emotion estimation function to have the generation AI provide the support that the user feels most at ease with and explain the reasons for that. For example, if the user is feeling anxious, the generation AI provides support while explaining the reasons for that. The generation AI also suggests local support services to help the user feel at ease. In this way, by using the emotion estimation function to provide the support that the user feels most at ease with and explaining the reasons for that, traveler satisfaction is improved.

[0080] The travel plan creation unit can analyze user feedback in detail and reflect it in the next travel plan. For example, the travel plan creation unit analyzes user feedback in detail, and the generation AI optimizes the next travel plan based on that data. For example, if the user enjoyed a particular activity, the generation AI can include a similar activity in the next plan. The generation AI can also create a new travel plan based on user feedback. For example, it can propose a plan that reflects the user's opinions. In this way, by analyzing user feedback in detail and reflecting it in the next travel plan, traveler satisfaction can be improved.

[0081] The travel plan creation unit can analyze the user's emotional state based on feedback and identify areas for improvement to elicit positive emotions. For example, the travel plan creation unit analyzes the emotional state based on user feedback, and the generation AI identifies areas for improvement to elicit positive emotions. For example, if the user enjoyed a particular activity, that element is enhanced. The generation AI also suggests new areas for improvement based on user feedback. For example, it proposes a plan that reflects the user's opinions. In this way, the user's emotional state is analyzed based on feedback and areas for improvement to elicit positive emotions are identified, thereby improving traveler satisfaction.

[0082] The travel plan creation unit can automatically generate travel guides and reviews that are useful to other users based on user feedback. In the travel plan creation unit, for example, the generation AI automatically generates travel guides that are useful to other users based on user feedback. For example, it creates guides that include detailed reviews of specific activities or restaurants. The generation AI also creates new reviews based on user feedback. For example, it suggests reviews that reflect the user's opinions. In this way, travel guides and reviews that are useful to other users based on user feedback are automatically generated, thereby improving traveler satisfaction.

[0083] The travel plan creation unit also collects feedback from the user's friends and family, which can improve the satisfaction of the entire group. For example, the travel plan creation unit collects feedback from the user's friends and family, and the generation AI uses that data to improve the satisfaction of the entire group. For example, activities that everyone can enjoy may be included in the next plan. The generation AI may also propose a new plan based on the feedback from the user's friends and family. For example, it may propose a plan that reflects everyone's opinions. In this way, feedback from the user's friends and family may also be collected, improving the satisfaction of the entire group and thereby improving the satisfaction of travelers.

[0084] The travel plan creation unit can suggest new travel destinations and activities based on feedback. In the travel plan creation unit, for example, the generation AI suggests new travel destinations based on user feedback. For example, if the user enjoyed a particular type of travel destination, similar travel destinations are suggested. Also, the generation AI suggests new activities based on user feedback. For example, if the user enjoyed a particular activity, a similar activity is suggested. In this way, suggesting new travel destinations and activities based on feedback improves traveler satisfaction.

[0085] The travel plan creation unit can use the emotion estimation function to identify the points that the user was most satisfied with and reflect that information in the next travel plan. The travel plan creation unit, for example, uses the emotion estimation function to identify the points that the user was most satisfied with, and the generation AI reflects that information in the next travel plan. For example, if the user enjoyed a particular activity, that element will be included in the next plan. The generation AI also proposes new plans based on user feedback. For example, if the user liked a particular place, a plan including that place will be proposed. In this way, by using the emotion estimation function to identify the points that the user was most satisfied with and reflecting that information in the next travel plan, traveler satisfaction is improved.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] TravelWhiz Explorer can also suggest travel plans that include special workshops and classes based on the user's hobbies and interests. For example, it can suggest local cooking classes to a user who loves cooking, or art classes to a user who is interested in art. This improves traveler satisfaction by suggesting special workshops and classes based on the user's hobbies and interests.

[0088] TravelWhiz Explorer can also suggest travel destinations that are optimal for a user's physical condition based on their health data. For example, the AI ​​can analyze the user's step count data and use that data to suggest travel destinations. For example, if the user normally walks a lot, the AI ​​can suggest places with plenty of hiking trails. The AI ​​can also analyze the user's heart rate data and use that data to suggest relaxing travel destinations. For example, if the user's heart rate is high, the AI ​​can suggest spa resorts. This makes it possible to suggest optimal travel destinations based on the user's health data, providing travel plans that take the traveler's health into consideration.

[0089] TravelWhiz Explorer can also analyze a user's real-time emotional state and suggest travel destinations that best suit that emotion. For example, if a user is feeling stressed, the AI ​​will suggest a relaxing beach resort. If a user is excited, the AI ​​will suggest a place with plenty of adventure activities. This improves traveler satisfaction by suggesting the best travel destination based on the user's real-time emotional state.

[0090] TravelWhiz Explorer can also analyze a user's social media posts or photos and reflect this in its travel destination suggestions. For example, a user's social media posts can be analyzed and the generation AI can suggest travel destinations based on the content. For example, if a user frequently posts photos of nature, the generation AI can suggest travel destinations rich in nature. Also, if a user posts about cultural events, the generation AI can suggest cities where users can have cultural experiences. This makes it possible to suggest more personalized travel destinations by analyzing a user's social media posts and photos.

[0091] TravelWhiz Explorer can also monitor the user's emotional state in real time and dynamically adjust the plan to match their mood during the trip. For example, the user's emotional state can be monitored in real time, and the generation AI can dynamically adjust the travel plan based on that data. For example, if the user is tired, the generation AI will add relaxing activities. If the user is excited, the generation AI will add adventurous activities. This dynamically adjusts the travel plan based on the user's emotional state, improving traveler satisfaction.

[0092] TravelWhiz Explorer can also analyze the success rate of a user's past travel plans and recreate the most satisfying travel plan. For example, by analyzing the success rate of a user's past travel plans, the generation AI can recreate the plan that gave the most satisfaction. For example, it can suggest plans that include activities that have been highly rated in the past. The generation AI can also create new travel plans based on the success rate of a user's past travel plans. For example, it can propose a new plan by combining elements of plans that have been successful in the past. In this way, by analyzing the success rate of a user's past travel plans, the generation AI can recreate the plan that gave the most satisfaction.

[0093] TravelWhiz Explorer can also use the user's emotion estimation function to suggest the most relaxing travel plan for the user and explain the reason for it. For example, using the emotion estimation function, the generation AI can suggest the most relaxing plan for the user and explain the reason for it. For example, if the user is feeling stressed, the generation AI can suggest time at a spa or the beach and explain the reason for it. Also, if the user wants to relax, the generation AI can suggest a quiet resort and explain the reason for it. In this way, by using the emotion estimation function to suggest the most relaxing plan for the user and explaining the reason for it, traveler satisfaction is improved.

[0094] TravelWhiz Explorer also takes into account the activity preferences of the user's friends and family, allowing it to suggest activities that can be enjoyed as a group. For example, it analyzes the activity preferences of the user's friends and family, and the generation AI uses that data to suggest activities that can be enjoyed as a group. For example, it suggests activities that everyone can enjoy. The generation AI also suggests new activities based on the preferences of the user's friends and family. For example, it suggests workshops and tours that everyone can participate in. In this way, the generation AI takes into account the activity preferences of the user's friends and family and suggests activities that can be enjoyed as a group, thereby improving traveler satisfaction.

[0095] TravelWhiz Explorer can also use the user's emotion estimation function to suggest the activity the user would enjoy most and explain the reason. For example, using the emotion estimation function, the generation AI can suggest the activity the user would enjoy most and explain the reason. For example, if the user prefers adventure activities, the generation AI will suggest them while explaining the reason. Also, if the user wants to relax, the generation AI will suggest spa or massage and explain the reason. In this way, by using the emotion estimation function to suggest the activity the user would enjoy most and explaining the reason, traveler satisfaction is improved.

[0096] TravelWhiz Explorer can also suggest optimal travel routes and means of transportation in real time based on the user's location information. For example, by analyzing the user's location information, the generation AI can suggest optimal travel routes in real time. For example, it can suggest routes that avoid traffic congestion. The generation AI can also suggest optimal means of transportation based on the user's location information. For example, it can suggest the use of public transportation or taxis. This improves traveler satisfaction by suggesting optimal travel routes and means of transportation in real time based on the user's location information.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The destination selection unit suggests the optimal travel destination based on the user's information. For example, the generation AI suggests the optimal travel destination based on information such as the user's past travel history, preferences, budget, and travel purpose. The generation AI uses text generation AI (e.g., LLM) to analyze the user's information and suggest the optimal travel destination. The generation AI can also use multimodal generation AI to analyze the user's information from multiple angles and suggest the optimal travel destination. Step 2: The travel plan creation unit creates a customized travel plan based on the travel destinations suggested by the destination selection unit. For example, the generation AI creates a customized travel plan based on the user's preferences and needs. The generation AI creates a travel plan that includes activities, accommodations, restaurants, etc. based on information about the user's preferences and needs. Step 3: The activity booking unit helps the user book the activities they select based on the travel plan created by the travel plan creation unit. For example, if the user wants to reserve tickets to a museum, the generation AI automatically handles the booking process. It also supports restaurant reservations and tour arrangements. The generation AI handles the booking process based on information about the activities the user wants to book.

[0099] 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.

[0100] 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.

[0101] 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.

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

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0115] 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 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.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a destination selection unit that proposes optimal travel destinations based on user information; a travel plan creation unit that creates a customized travel plan based on the travel destinations proposed by the destination selection unit; an activity booking unit that supports booking of activities selected by a user based on the travel plan created by the travel plan creation unit; A system characterized by:

2. The destination selection unit Analyzing the user's real-time emotional state and suggesting the travel destination that best suits that emotion 2. The system of claim 1.

3. The travel plan creation unit Analyzing the success rate of the user's past travel plans and recreating the most satisfying travel plan 2. The system of claim 1.

4. The activity reservation unit Analyzing the user's past reservation history and preferentially suggesting the activity with the highest satisfaction rate 2. The system of claim 1.

5. The travel plan creation unit Analyzing the user's real-time emotional state and suggesting activities and restaurants that match their mood 2. The system of claim 1.

6. The travel plan creation unit The user's feedback is analyzed in detail and reflected in the next travel plan.

2. The system of claim 1.

7. The activity reservation unit Monitoring the user's emotional state in real time and suggesting the activity according to the user's mood 2. The system of claim 1.

8. The travel plan creation unit Identifying the points that the user was most satisfied with and reflecting that information in the next travel plan 2. The system of claim 1.

Images